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 .
Status of the Claims
Claims 1-21 are currently pending and under exam herein.
Claims 1-21 are rejected.
Priority
The instant application claims priority from provisional application 63/404,197 filed on 9/7/2022. Thus, the effective filing date of the instant application is 9/07/2022.
Drawings
The Drawings filed on 09/07/2023 were considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/07/2023 and 09/21/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements has been considered by the examiner.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-21 are directed to a method for using bipartite networks to determine interactions between analytes and chemical treatments.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
accessing information relating to effects of chemical treatments on analyte production; building, based on the accessed information, a bipartite network comprising chemical treatment nodes and analyte nodes, wherein the bipartite network quantitatively represents the effects of chemical treatments to trigger production of analytes; analyzing the bipartite network to identify dominant chemical treatments among the chemical treatments and identify secondary metabolites among the analytes; and outputting the identified dominant chemical treatments and the identified secondary metabolites. (mathematical concept and/or mental process)
iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. (MPEP 2106.04)
a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) (MPEP 2106.04)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing the bipartite network includes at least one of analyzing the bipartite network via a direct route to identify known and putative secondary metabolites and analyzing the bipartite network via an auxiliary route to identify untargeted and unknown analytes of interest. (mathematical concept and/or mental process)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the operations follow a direct route approach such that the analyte nodes of the built bipartite network are either known secondary metabolites or putative secondary metabolites or both, and wherein the analyzing the bipartite network analysis comprises identifying the most influenced secondary metabolites from among the known or putative secondary metabolites (mathematical concept and/or mental process)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the building the bipartite network comprises: defining two bipartite sets of nodes, one of the bipartite sets of nodes including chemical treatments and the other bipartite sets of nodes including analytes; constructing directional, weighted edges between nodes using log2fold change of an analyte by a chemical treatment; and assigning positive or negative sign to each edge for visualization of metabolite upregulation or metabolite downregulation. (mathematical concept and/or mental process)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing the bipartite network analysis comprises: computing a plurality of network centrality measures of the bipartite network including: out-degrees for each chemical treatment; in-degrees for each analyte; broadcasting rank for each chemical treatment; and receiving rank for each analyte. (mathematical concept and/or mental process)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the broadcasting ranks and receiving ranks are normalized PageRank measures. (mathematical concept and/or mental process)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the operations follow an auxiliary route approach, and wherein the analyzing the bipartite network includes analyzing the bipartite network to identify untargeted and unknown analytes of interest (mathematical concept and/or mental process)
independent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
accessing spectra of unknown analytes relating to chemical treatments; (mathematical concept and/or mental process)
generating a matrix relating the spectra of the unknown analytes to the chemical treatments; ; (mathematical concept)
applying fold change rank order statistics (FCROS) to the matrix to determine a p-value and an f-value for each unknown analyte; building a bipartite network using unknown analytes with statistically significant p-values and f-values; (mathematical concept)
selecting one or more unknown analytes by fold change or edge degree; and identifying secondary metabolites from among the selected one or more unknown analytes. (mental process)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
herein the applying fold change rank order statistics (FCROS) to the matrix comprises: (mathematical concept)
repeatedly, for all combinations of controls and treatments: selecting a control sample and a treatment sample; (mathematical concept)
computing a fold change for each analyte; ranking analytes in increasing order to obtain an associated rank with each analyte; computing an average of ranks for each analyte(mathematical concept)
using the mean and variance of the average of ranks to generate a normal distribution to associate a probability with each rank; (mathematical concept)
and defining two cutoff values to identify up- and down-regulated analytes, wherein an analyte is downregulated if below a first cutoff value and an analyte is upregulated if above a second cutoff value. (mathematical concept)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the building the bipartite network comprises: repeatedly, for each treatment: selecting a treatment-specific FCROS matrix; in response to an analyte in the matrix having significant f-value and p-value, (mathematical concept)
generating a treatment graph connecting all analyte nodes to a single node representing a treatment type associated with the treatment-specific FCROS matrix; (mathematical concept)
represent edges between nodes and treatment type by fold change; (mathematical concept)
unioning the treatment graphs to generate a full union of all graphs and a network of similar treatments. (mathematical concept)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the selecting one or more unknown analytes by fold change or edge degree includes scoring the one or more analytes by at least one of:d egree connected to a singular treatment; upregulation value; downregulation value; and shared analytes between similar treatments. (mathematical concept and/or mental process)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the selecting one or more unknown analytes by degrees indicative of production of an unknown analyte. (mental process)
Dependent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
accessing information relating to effects of chemical treatments on analyte production; (mental process)
building, based on the accessed information, a bipartite network comprising chemical treatment nodes and analyte nodes, wherein the bipartite network quantitatively represents the effects of chemical treatments to trigger production of analytes; (mental process and/or mathematical concept)
analyzing the bipartite network to identify dominant chemical treatments among the chemical treatments and identify secondary metabolites among the analytes; (mental process and/or mathematical concept)
and outputting the identified dominant chemical treatments and the identified secondary metabolites. (mental process and/or mathematical concept)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing the bipartite network includes at least one of analyzing the bipartite network via a direct route to identify known and putative secondary metabolites and analyzing the bipartite network via an auxiliary route to identify untargeted and unknown analytes of interest (mental process and/or mathematical concept)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing follows a direct route approach such that the analyte nodes of the built bipartite network are either known secondary metabolites or putative secondary metabolites or both, and wherein the analyzing the bipartite network analysis comprises identifying the most influenced secondary metabolites from among the known or putative secondary metabolites. (mental process and/or mathematical concept)
Dependent claim 18 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the building the bipartite network comprises: defining two bipartite sets of nodes, one of the bipartite sets of nodes including chemical treatments and the other bipartite sets of nodes including analytes; constructing directional, weighted edges between nodes using log2fold change of an analyte by a chemical treatment; and assigning positive or negative sign to each edge for visualization of metabolite upregulation or metabolite downregulation. (mental process and/or mathematical concept)
Dependent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing the bipartite network analysis comprises: computing a plurality of network centrality measures of the bipartite network including: out-degrees for each chemical treatments; in-degrees for each analyte; broadcasting rank for each chemical treatment; and receiving rank for each analyte. (mental process and/or mathematical concept)
Dependent claim 20 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the analyzing the bipartite network includes analyzing the bipartite network via an auxiliary route to identify untargeted and unknown analytes of interest (mental process and/or mathematical concept)
Dependent claim 21 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein analyzing the bipartite network includes: accessing spectra of unknown analytes relating to chemical treatments; (mental process)
generating a matrix relating the spectra of the unknown analytes to the chemical treatments; (mental process and/or mathematical concept)
applying fold change rank order statistics (FCROS) to the matrix to determine a p-value and an f-value for each unknown analyte; (mathematical concept)
building a bipartite network using unknown analytes with statistically significant p-values and f-values; selecting one or more unknown analytes by fold change or edge degree; (mathematical concept)
and identifying secondary metabolites from among the selected one or more unknown analytes. (mental process)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-21 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising
The additional element in dependent claim 8 includes:
wherein the information relating to effects of chemical treatments on analyte production comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments.
The additional element in Independent claim 9 includes:
Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising
The additional element in dependent claim 14 includes:
wherein the spectra of unknown analytes relating to chemical treatments comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments.
The additional element in dependent claim 15 includes:
A method for recommending usage of chemical treatments, the method comprising
The additional elements of wherein the information relating to effects of chemical treatments on analyte production comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments (Claim 8), wherein the spectra of unknown analytes relating to chemical treatments comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments (Claim 14) are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements of Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 1), Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 9) a method for recommending usage of chemical treatments, the method comprising (Claim 15) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
The additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-21 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-21 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1-21 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of wherein the information relating to effects of chemical treatments on analyte production comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments (Claim 8), wherein the spectra of unknown analytes relating to chemical treatments comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments (Claim 14) are conventional and part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). Evidence for conventionality is shown by Azzollini et al. which uses LCMS to identify metabolites (abstract)
The additional elements of Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 1), Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 9) a method for recommending usage of chemical treatments, the method comprising (Claim 15) are conventional fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Therefore, when taken alone, all additional elements in claims 1-21 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-21 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-5, 7-21 are rejected under 35 U.S.C. 103 as being unpatentable over Azzollini et al. (Azzollini et al. Dynamics of Metabolite Induction in Fungal Co-Cultures by Metabolomics at Both Volatile and Non-Volatile Levels. Frontiers in microbiology, 2018, 9, 72.) in view of US-10573406-B2 in further view of Grindrod et al. (Grindrod, P.; Parsons, M. C.; Higham, D. J.; Estrada, E. Communicability across Evolving Networks. Physical Review E 2011, 83 (4)) in further view of Dembélé et al. (Dembélé, D.; Kastner, P. Fold Change Rank Ordering Statistics: A New Method for Detecting Differentially Expressed Genes. BMC Bioinformatics 2014, 15 (1). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, 2, 7, 8, 9, 14, 15, 16, 20, 21, Azzollini et al. teaches the solid media samples were lyophilized and extracted by a dichloromethane-methanol solvent mixture for subsequent liquid chromatography high resolution mass spectrometry (LC-HRMS) analysis of the non-volatile fraction. (Results and Discussion) Fungal co-cultivation has emerged as a promising way for activating cryptic biosynthetic pathways and discovering novel antimicrobial metabolites. For the success of such studies, a key element remains the development of standardized co-cultivation methods compatible with high-throughput analytical procedures. To efficiently highlight induction processes, it is crucial to acquire a holistic view of intermicrobial communication at the molecular level. To tackle this issue, a strategy was developed based on the miniaturization of fungal cultures that allows for a concomitant survey of induction phenomena in volatile and non-volatile metabolomes. Fungi were directly grown in vials, and each sample was profiled by head space solid phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS), while the corresponding solid culture medium was analyzed by liquid chromatography high resolution mass spectrometry (LC-HRMS) after solvent extraction. This strategy was implemented for the screening of volatile and non-volatile metabolite inductions in an ecologically relevant fungal co-culture of Eutypa lata (Pers.) Tul. & C. Tul. (Diatrypaceae) and Botryosphaeria obtusa (Schwein.) Shoemaker (Botryosphaeriaceae), two wood-decaying fungi interacting in the context of esca disease of grapevine. For a comprehensive evaluation of the results, a multivariate data analysis combining Analysis of Variance and Partial Least Squares approaches, namely AMOPLS, was used to explore the complex LC-HRMS and GC-MS datasets and highlight dynamically induced compounds. A time-series study was carried out over 9 days, showing characteristic metabolite induction patterns in both volatile and non-volatile dimensions. Relevant links between the dynamics of expression of specific metabolite production were observed. In addition, the antifungal activity of 2-nonanone, a metabolite incrementally produced over time in the volatile fraction, was assessed against Eutypa lata and Botryosphaeria obtusa in an adapted bioassay set for volatile compounds. This compound has shown antifungal activity on both fungi and was found to be co-expressed with a known antifungal compound, O-methylmellein, induced in solid media. This strategy could help elucidate microbial inter- and intra-species cross-talk at various levels. Moreover, it supports the study of concerted defense/communication mechanisms for efficiently identifying original antimicrobials. The treatment is a co-cultivation of fungus as a person of ordinary skill in the art as co-cultivation is equivalent to a treatment. (accessing information relating to effects of chemical treatments on analyte production (Claim 1, Claim 15) wherein the analyzing the bipartite network includes at least one of analyzing the bipartite network via a direct route to identify known and putative secondary metabolites and analyzing the bipartite network via an auxiliary route to identify untargeted and unknown analytes of interest. (Claim 2, Claim 16) wherein the operations follow an auxiliary route approach, and wherein the analyzing the bipartite network includes analyzing the bipartite network to identify untargeted and unknown analytes of interest ((Claim 7, Claim 20), accessing spectra of unknown analytes relating to chemical treatments ((Claim 9, Claim 21) identifying secondary metabolites from among the selected one or more unknown analytes ((Claim 9, Claim 21) wherein the spectra of unknown analytes relating to chemical treatments comprises liquid chromatography mass-spectroscopy (LCMS) spectra of the analytes corresponding to the chemical treatments. (Claim 14)
Azzollini et al. does not explicitly teach
building, based on the accessed information, a bipartite network comprising chemical treatment nodes and analyte nodes, wherein the bipartite network quantitatively represents the effects of chemical treatments to trigger production of analytes; (Claim 1, Claim 15)
defining two bipartite sets of nodes, one of the bipartite sets of nodes including chemical treatments and the other bipartite sets of nodes including analytes (Claim 4 Claim 18,
generating a treatment graph connecting all analyte nodes to a single node representing a treatment type associated with the treatment-specific FCROS matrix (Claim 11, contingent limitation is not needed if it is not significant. If there are no significant metabolites then there is no treatment.
outputting the identified dominant chemical treatments and the identified secondary metabolites (Claim 1, Claim 15)
wherein the operations follow a direct route approach such that the analyte nodes of the built bipartite network are either known secondary metabolites or putative secondary metabolites or both, (Claim 3, Claim 17)
building a bipartite network using unknown analytes with statistically significant p-values and f-values ((Claim 9, Claim 21)
assigning positive or negative sign to each edge for visualization of metabolite upregulation or metabolite downregulation ((Claim 4, Claim 18)
represent edges between nodes and treatment type by fold change (Claim 11)
wherein the analyzing the bipartite network analysis comprises: computing a plurality of network centrality measures of the bipartite network including ((Claim 5, Claim 19)
analyzing the bipartite network to identify dominant chemical treatments among the chemical treatments and identify secondary metabolites among the analytes; and (Claim 1, Claim 15)
and wherein the analyzing the bipartite network analysis comprises identifying the most influenced secondary metabolites from among the known or putative secondary metabolites (Claim 3, Claim 17),
broadcasting rank for each chemical treatment; and ((Claim 5, Claim 19)
receiving rank for each analyte. ((Claim 5, Claim 19)
unioning the treatment graphs to generate a full union of all graphs and a network of similar treatments.(Claim 11)
wherein the selecting one or more unknown analytes by degrees indicative of production of an unknown analyte (Claim 13)
out-degrees for each chemical treatment in-degrees for each analyte; and ((Claim 5, Claim 19),
selecting one or more unknown analytes by fold change or edge degree; and ((Claim 9, Claim 21)
applying fold change rank order statistics (FCROS) to the matrix to determine a p-value and an f-value for each unknown analyte ((Claim 9, Claim 21)
wherein the applying fold change rank order statistics (FCROS) to the matrix comprises: repeatedly, for all combinations of controls and treatments: selecting a control sample and a treatment sample; computing a fold change for each analyte; ranking analytes in increasing order to obtain an associated rank with each analyte; computing an average of ranks for each analyte; using the mean and variance of the average of ranks to generate a normal distribution to associate a probability with each rank; and defining two cutoff values to identify up- and down-regulated analytes, wherein an analyte is downregulated if below a first cutoff value and an analyte is upregulated if above a second cutoff value (Claim 10)
wherein the building the bipartite network comprises: repeatedly, for each treatment: selecting a treatment-specific FCROS matrix in response to an analyte in the matrix having significant f-value and p-value (Claim 11)
wherein the selecting one or more unknown analytes by fold change or edge degree includes scoring the one or more analytes by at least one of: degree connected to a singular treatment; upregulation value; downregulation value; and shared analytes between similar treatments. (Claim 12)
Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 1)
Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 9)
A method for recommending usage of chemical treatments, the method comprising: (Claim 15)
With respect to the limitations of Claims 1, 3, 4, 5, 9, 11, 18, 19, 21, US10573406B2 teaches bipartite networks connecting a node representing an experiment or statistical comparison to nodes representing either (1) biochemicals, (2) metabolic pathway maps, (3) pathway ontologies, or (4) keyword ontologies, may be generated (specification, building, based on the accessed information, a bipartite network comprising chemical treatment nodes and analyte nodes, wherein the bipartite network quantitatively represents the effects of chemical treatments to trigger production of analytes; (Claim 1, Claim 15) defining two bipartite sets of nodes, one of the bipartite sets of nodes including chemical treatments and the other bipartite sets of nodes including analytes (Claim 4 Claim 18, generating a treatment graph connecting all analyte nodes to a single node representing a treatment type associated with the treatment-specific FCROS matrix (Claim 11, contingent limitation is not needed if it is not significant. If there are no significant metabolites then there is no treatment) the ranked determined biochemical pathways may be displayed (e.g., on a computer monitor or display) as being distinguished from each other by a magnitude of the category enrichment ratio for each determined biochemical pathway (specification, outputting the identified dominant chemical treatments and the identified secondary metabolites (Claim 1, Claim 15) A method is provided for analyzing metabolite data in a sample, comprising analyzing a sample to determine a first number of metabolites, and amount of each metabolite, included in the sample, and a second number of the first number of metabolites that are regulated. Biochemical pathways are determined, each having a third number of the first number of metabolites that are included in the sample and in the determined biochemical pathway. For each of the determined biochemical pathways having the third number of metabolites, a fourth number of the second number of metabolites that are included in the sample and in the determined biochemical pathway that are regulated metabolites is determined. Each of the third number of metabolites within one of the determined biochemical pathways is displayed and distinguished by the amount of each corresponding metabolite included in the sample. An associated apparatus and computer program product are also provided.(abstract) Metabolites in a library, both known and unknown, that are identified in these studies are associated with certain “metabolite metadata” (Specification, wherein the operations follow a direct route approach such that the analyte nodes of the built bipartite network are either known secondary metabolites or putative secondary metabolites or both, (Claim 3, Claim 17) building a bipartite network using unknown analytes with statistically significant p-values and f-values ((Claim 9, Claim 21) The size of the circle represents the magnitude of the measured change (i.e., fold-change) of the metabolite level relative to the reference level (i.e., the larger the circle, the larger the measured difference in metabolite level compared to the reference level). The same visualization is taught applicant just made an aesthetic design choice. (Specification, constructing directional, weighted edges between nodes using log2fold change of an analyte by a chemical treatment; and ((Claim 4, Claim 18) For each experiment or statistical comparison (i.e., analysis of the sample), a threshold may be applied to a numerical quantification value and/or a statistical significance probability value. The threshold can be adjusted and is used to set the criterion value to determine if an attribute for each biochemical/metabolite is differentially present or “responsive” (e.g., increased or decreased, regulated or un-regulated) in the experiment or statistical comparison. In one example, a responsive biochemical attribute may be used to differentiate a case condition from a control condition. In another example, the case condition may be a disease sample(s) and the control condition may be a normal sample(s). In some instances, a biochemical attribute may be regulated or responsive, if differentially present as compared to a defined reference standard (e.g., a reference range, reference sample). The threshold can be dynamically manipulated by the investigator for each experiment or statistical comparison. (Specification, assigning positive or negative sign to each edge for visualization of metabolite upregulation or metabolite downregulation ((Claim 4, Claim 18) represent edges between nodes and treatment type by fold change (Claim 11) graph indices may be available to characterize each network and quickly and readily identify hubs (i.e., nodes with a high vertex degree) or appropriate neighborhoods (e.g., metabolites that interact with at least two receptor proteins and another biological data type defined by a user) (Specification, wherein the analyzing the bipartite network analysis comprises: computing a plurality of network centrality measures of the bipartite network including ((Claim 5, Claim 19)
With respect to the limitations of Claims 1, 3, 5, 9, 11, 13, 17, 19, 21, Grindrod et al. teaches many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about “who phoned who” or “who came into contact with who” arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time’s arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction (abstract). This identifies nodes with the highest ability to “broadcast”. Identify nodes with the highest ability to receive. This reference identifies every node producing a complete ordering within each group. The reference teaches that nodes have two distinct types flow out or flow in. Mapping it onto claimed invention every edge runs from a treatment to an analyte. Treatments have outbound edges only and metabolites have inbound edges only. The reference broadcast score would automatically rank the treatments. (analyzing the bipartite network to identify dominant chemical treatments among the chemical treatments and identify secondary metabolites among the analytes; and (Claim 1, Claim 15) and wherein the analyzing the bipartite network analysis comprises identifying the most influenced secondary metabolites from among the known or putative secondary metabolites (Claim 3, Claim 17), broadcasting rank for each chemical treatment; and ((Claim 5, Claim 19) receiving rank for each analyte. ((Claim 5, Claim 19) unioning the treatment graphs to generate a full union of all graphs and a network of similar treatments.(Claim 11) wherein the selecting one or more unknown analytes by degrees indicative of production of an unknown analyte (Claim 13) in the limit a→ 0 the centrality measures reduce to multiples of the aggregate out and in degrees, shifted by unity (pg. 4, cols 1-2) out-degrees for each chemical treatment in-degrees for each analyte; and ((Claim 5, Claim 19), selecting one or more unknown analytes by fold change or edge degree; and ((Claim 9, Claim 21)
With respect to the limitations of Claims 1, 9, 10, 11, 12, 15, 21, Dembele et al. teaches method based on fold change rank ordering statistics (FCROS). We exploit the variation in calculated FC levels using combinatorial pairs of biological conditions in the datasets. A statistic is associated with the ranks of the FC values for each gene, and the resulting probability is used to identify the DE genes within an error level. The FCROS method is deterministic, requires a low computational runtime and also solves the problem of multiple tests which usually arises with microarray datasets. This is good for finding differential expressed metabolites and therefore it (abstract). The exact steps are discussed in the methods section. Given microarray data having m 1 control and m 2 test samples, perform k ≤ m 1 m 2 pairwise comparisons and compute FCs for genes (test/control). These FCs are sorted in increasing order and their corresponding ranks are associated to genes. Compute a robust average of rank for each gene (i = 1,2,…,n) using its k values. This can be done using a trimmed mean. Sort values of by increasing order to get where Compute sample mean and sample variance The minimum average rank is , and the maximum average rank is. Compute differences between consecutive terms of and then derive an estimate for parameter δ as the mean of the obtained differences: as parameters of a normal distribution and associate probabilities to genes through their values. Since a p-value refers to the probability associated with a hypothesis testing statistic, we call probabilities associated to fold change ranks ordering statistics f-values. A f-value close to 0.5 corresponds to an equally expressed (EE) gene, while down- and up-regulated genes have f-values close to 0 and 1, respectively.Set error levels, α 1 and α 2, for down- and up-regulated genes to select the DE genes. We use standardized ranks, i.e. each component in r i is divided by n. Hence, the mean and standard deviation in step 3 of the algorihm above should be divided by n. In the FCROS algorithm, necessary parameters are computed from the dataset except the trimmed mean percentage parameter noted trim. Theorem 1 gives theoretical values for many parameters, more precisely and For the ideal situation (a = δ = 1, b = n) theoretical mean and variance are and, respectively. Let us examine the role of parameters k, δ and trim. Parameter k The size of the integer k allows to fulfill the conditions to apply the central limit theorem, higher values for k being optimal. The maximum value m1m2 for k is determined by the number of control and test samples in the dataset. Parameter δ Parameter δ takes its value in the interval [0,1]. The ideal value δ = 1 is unlikely to be obtained. A small value of the parameter δ leads to a small variance This will happen when the difference between upper and lower bounds of the ordered a.o.r becomes smaller, i.e., if the observed changes in the ranks associated with genes are large, so that the a.o.r will tend to move away from the ideal bounds 1 and n. We can consider the parameter δ as a fraction of the dataset size range: where β = b/n and α = a/n. From this point of view, a value of δ equal to 0.98 can correspond to (b = 0.99n,a = 0.01n) and is better than a value for δ equal to 0.66 which can correspond to the bounds () which are more distant from n and 1. We provide numerical values for δ in Additional file 1: Figures S3 and S5 using synthetic and real microarray datasets. Parameter trim To have a robust estimation of the o.a.r we use a fraction of ranks associated to gene i. Parameter trim allows to delete some ranks from each end (small and high ranks) before computing the mean. Thus, a value for trim equal to 0.1 means that 80% of the ranks for gene i are used to calculate. We consider a two conditions microarray experiment where n probes (genes) are used with m1 control and m2 test samples. The number n of probes is generally greater than 10,000 except for few species like yeast. Values for m1 and m2 are however small, most often lower than 100. We note ) the values for the gene i (i = 1,2,…,n) for the control samples () and the test samples (), respectively. For a single color microarray, values () are log2 levels, while they are log2 ratios for a two-color microarray. Here are examples of log2 transformed data for two genes (MACF1 and TREM2) taken from an experiment using Agilent microarrays (SurePrint, design 028004_D_F_20101102), with one color hybridization. Data for MACF1 are: = (11.1435, 11.2860, 11.2249, 11.1258, 11.0325, 11.1108, 11.3377, 11.1821, 11.0675, 11.2381), = (11.0375, 11.0792, 10.9673, 11.0367, 11.1054, 10.9261, 11.0433, 10.9484, 10.9412, 10.8385); data for TREM2 are: = (6.2856, 6.4891, 5.7799, 6.1081, 6.3129, 6.3208, 6.4826, 6.2005, 5.8922, 6.2148), = (11.6792, 8.1128, 6.6253, 6.8334, 7.6417, 7.5133, 5.9633, 7.4631, 6.5666, 7.6020). There are m1 = 10 control and m2 = 10 test samples. The FC and the Student t-test p-value for MACF1 and TREM2 are (0.8806, 0.000248) and (6.2570, 0.01259), respectively. These results lead to the following two observations: a) a small Student t-test p-value is not necessary associated to a high FC, b) a high Student t-test p-value can be associated to a high FC. Indeed, the Student t-test statistic is calculated as , where and are average levels of the control and test samples respectively, is the combined variance from those of the control and test samples: , ( and are variances of xt and xc). For the same average difference (), a small can lead to high t (small p-value), on the other hand, a large can lead to a small t (high p-value). Hence, a small (high) average difference can have a small (high) Student t-test p-value. The variances of data for genes MACF1 and TREM2 given above are 0.008 and 1.26, leading to t-statitics equal to 4.549 and 2.711 respectively. These observations are highlighted by Xiao et al. and correspond to the SFSV (small fold change, small variance) and the LFLV (large fold change, large variance), respectively. For the proposed method, the probability of the statistic obtained is close to zero (one) for down-(up)regulated genes. Using the method described below, the probabilities associated to the statistics obtained for MACF1 and TREM2 are 0.12105 and 0.9964, respectively. These values mean that MACF1 does not change and that TREM2 is up-regulated. Being given expression values for n genes in m1 control and m2 test samples, we perform k ≤ m1m2 pairwise comparisons and compute FCs for each gene (test/control). In each comparison, the n FCs obtained are sorted in increasing order and their corresponding ranks are associated to genes. Hence, for gene i, we get a vector r i = (ri1ri2 … r ij …,r ik ) where r ij corresponds to the rank of the FC for gene i in the j comparison (j = 1,…,k). The ranks are integers that belong to the set { 1,2,…,n}. To deal with ties, the rank values are adjusted in such a way that their sum reaches the same total as that reached if there is no tie. By construction, knowledge of one component of the vector r i does not allow to predict the another ones. This leads to an independence of the ranks associated to pairwise comparisons. Hence, the components of the vector r i can be considered as samples of the true unknown rank associated to gene i. Ideally, the same rank should be assigned to each gene in the k comparisons. The probability of this event is
and is unlikely to happen. Hence, the averages of ranks (a.o.r), i = 1,2,…,n, will vary between a minimum and a maximum is an average of components in r i . We can order all the a.o.r from the minimum to the maximum and write: where scalars δ i (i = 1,…,n - 1) are the differences between consecutive ordered a.o.r, and is a vector with all Without loss of generality, let us assume that the differences δ i have the same value which is approximated by their mean: . Hence, the ordered a.o.r , i = 1,2,…,n, can then be writen as:. Our method is based on the behavior of the ordered a.o.r and we have the following theorem. Theorem 1. When the number k of the pairwise comparisons grows, the ordered averages of ranks (a.o.r) have a normal distribution. The mean of this distribution is and its variance is , where a and b are the minimum and the maximum of the observed a.o.r , respectively. δ is an average difference between consecutive ordered a.o.r. Proof. We note the average of the components in r i . Let us note the expectation and the variance of the ranks in vector r i by E{r i } = R i and . Using the central limit theorem (, page 259) it follows that the quantity converges to a normal distributed variable having a mean of zero and a variance of one when k is high. Hence, we obtain n normal distributed variables R i (method, .(applying fold change rank order statistics (FCROS) to the matrix to determine a p-value and an f-value for each unknown analyte ((Claim 9, Claim 21) wherein the applying fold change rank order statistics (FCROS) to the matrix comprises: repeatedly, for all combinations of controls and treatments: selecting a control sample and a treatment sample; computing a fold change for each analyte; ranking analytes in increasing order to obtain an associated rank with each analyte; computing an average of ranks for each analyte; using the mean and variance of the average of ranks to generate a normal distribution to associate a probability with each rank; and defining two cutoff values to identify up- and down-regulated analytes, wherein an analyte is downregulated if below a first cutoff value and an analyte is upregulated if above a second cutoff value (Claim 10) wherein the building the bipartite network comprises: repeatedly, for each treatment: selecting a treatment-specific FCROS matrix in response to an analyte in the matrix having significant f-value and p-value (Claim 11) wherein the selecting one or more unknown analytes by fold change or edge degree includes scoring the one or more analytes by at least one of: degree connected to a singular treatment; upregulation value; downregulation value; and shared analytes between similar treatments. (Claim 12) Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 1) Memory encoding instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising (Claim 9) A method for recommending usage of chemical treatments, the method comprising: (Claim 15)
A person having ordinary skill in the art would be motivated to combine Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al. as each work is directed towards data analysis. Therefore a person having ordinary skill in the art would come across all of them when finding ways to improve data analysis pipelines. Azzollini et al. teaches the underlying experiment acquiring and identifying metabolite data via LCMS. Dembele et al. teaches method based on fold change rank ordering statistics as well as the resulting F values which a person of ordinary skill in the art would understand how to put it together. US10573406B2 teaches building a bipartite network data connecting treatment/ experiment node to metabolite nodes; fold change representation, differential expression direction (up/ down), rank ordering entities by fold change and a ranked display output. Grindrod et al. provides broadcasting and receiving. Each part works independently, and applicant is just putting together known analytical methods to produce a analysis pipeline. Each part works independently therefore there is a reasonable expectation of success when they are put together.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al. as applied to claims 1-5 and 7-21 above in further view of Ermann et al. (Ermann et al, Towards two-dimensional search engines) The italicized text corresponds to the instant claim limitations. Arxiv, 3/12/2012)
The limitations of claims 1-5 and 7-21 have been taught by Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al. above.
Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al. does not explicitly teach
wherein the broadcasting ranks and receiving ranks are normalized PageRank measures (Claim 6)
However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Ermann et al. teaches
With respect to the limitations of Claims 6, Ermann et al. teaches the use of PageRank to order nodes (abstract, wherein the broadcasting ranks and receiving ranks are normalized PageRank measures (Claim 6)
A person having ordinary skill in the art would be motivated to combine Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al. with Ermann et al. to gain an additional ranking algorithm. Ermann teaches the use of PageRank to order nodes. A person having ordinary skill in the art would understand to use it for ranking a bipartite network. PageRank algorithm is not being changed therefore there is a reasonable expectation of success it will work when combined with Azzollini et al. in view of Dembele et al. in view of US10573406B2 in view of Grindrod et al.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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