DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-15 are pending and under consideration in this action.
Priority
The instant application is 371 of PCT/IB2021/000686, filed 10/05/2021, which claims priority to U.S. Provisional Application number 63/088,332, filed 10/06/2020, as reflected in the filing receipt mailed 09/12/2023. The claim for domestic benefit for claims 1-15 is acknowledged. As such, the effective filing date of claims 1-15 is 10/06/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/30/2023 and 03/13/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s have been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference characters 400 and 499 in Fig. 4 are not mentioned in the Specification.
Reference characters 500 and 599 in Fig. 5 are not mentioned in the Specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 9 are objected to because of the following informalities:
Claim 1 recites the phrase “for one or more of the proteoform values identifying a likelihood…” in lines 4-5 of the claim, which should be corrected to add a comma after “values”, “for one or more of the proteoform values, identifying a likelihood…”, for clarity.
Claim 9 is missing a period at the end of the claim, which should be corrected for clarity.
Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the phrase “for one or more of the proteoform values identifying a likelihood the proteoform corresponds to a particular microbe species” in lines 4-5 of the claim. There is insufficient antecedent basis for the proteoform in the claim, since there is no prior mention of this phrase earlier in the claim. This rejection can be overcome by amendment of claim 1 to recite “for one or more of the proteoform values identifying a likelihood the proteoform value corresponds to a particular microbe species”. Claims 2-15 are also rejected due to their dependency on claim 1.
The term “informative” in claim 1 is a relative term which renders the claim indefinite. The term “informative” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The instant Specification (see at least Para. [0051]) discloses that for multi-marker problems, it is often the case that some proteoform markers are more informative than others. Weeding out less informative and potentially noisy and confounding proteoform markers can substantially improve the performance of the classifier. However, it does not provide a definition of how “informative” values are determined.
Claim 1 recites the limitation “wherein the proteoform value belongs to a subset of informative proteoform values for the candidate microbe species”. There is insufficient antecedent basis for the candidate microbe species in the claim, since there is no mention of this phrase earlier in the claim. This rejection can be overcome by amendment of claim 1 to recite “wherein the proteoform value belongs to a subset of informative proteoform values for a candidate microbe species”, or similar.
The term “best match” in claim 1 is a relative term which renders the claim indefinite. The term “best match” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The instant Specification (see at least Para. [0048]) discloses that the microbe species and/or strain that has the highest conditional probability computed from equation 2, amongst all microbe entries in the library, as the most likely candidate for the unknown microbe. Interpretation application then outputs the identification as microbe data which may also include other information such as the conditional probability of the best candidate microbe. Therefore, it appears that the “best match” is the candidate species with the highest conditional likelihood. However, it is unclear if this is the intended interpretation.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-14 are directed towards a method and claim 15 is directed towards a system, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claims 1 and 15 recites a mathematical concept (i.e., calculation of a likelihood/probability; see at least Specification Para. [0042]) in “for one or more of the proteoform values identifying a likelihood the proteoform corresponds to a particular microbe species”; a mental process (i.e., an evaluation of informative proteoform values) in “wherein the proteoform value belongs to a subset of informative proteoform values for the candidate microbe species”; a mathematical concept (i.e., calculation of a conditional likelihood/probability; see Specification Para. [0042]-[0044]) in “determining a conditional likelihood for a plurality of candidate microbe species using the identified likelihoods for each proteoform”; and a mental process (i.e., an evaluation of likelihood values to determine the best match) in “identifying the conditional likelihood of the candidate microbe species that is a best match to the unknown microbe species”.
Claim 2 recites a mental process (i.e., an evaluation of the informative proteoform values) in “wherein, the subset of informative proteoform values is determined using the proteoform values from a plurality of training samples”.
Claim 4 recites a mental process (i.e., an evaluation of the training samples) in “wherein the training samples comprise samples from different candidate microbe species”.
Claim 5 recites a mental process (i.e., an evaluation of the training samples) in “wherein the training samples comprise a replicate sample from at least one of the candidate microbe species”.
Claim 6 recites a mathematical concept (i.e., determining a variance) in “determining a variance value for each proteoform over all of the training samples”; a mathematical concept (i.e., performing an F statistical test) in “ranking the variances of the proteoform values using an F statistical test”; and a mental process (i.e., an evaluation of the ranking to determine informative values) in “selecting the subset of informative proteoform values from the ranking”.
Claim 7 recites a mathematical concept (i.e., performing an analysis of variance) in “wherein the F statistical test comprises an analysis of variance test”.
Claim 8 recites a mental process (i.e., an observation of the sample) in “wherein the sample comprises a complex mixture”.
Claim 9 recites a mental process (i.e., an observation of the sample) in “wherein the complex mixture comprises a cell lysate”.
Claim 10 recites a mental process (i.e., an evaluation of the proteoform value) in “wherein the proteoform value comprises a mass value”.
Claim 11 recites a mental process (i.e., an evaluation of the mass value) in “wherein the mass value comprises a monoisotopic mass value”.
Claim 12 recites a mental process (i.e., an observation of the unknown microbe species) in “wherein the unknown microbe species are selected from the group consisting of bacteria, yeast, and fungi”.
Claim 14 recites a mental process (i.e., an evaluation of the output identification) in “wherein the identification comprises a score”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
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, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. 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.
Specifically, claims 1 and 15 involve nothing more than identifying a likelihood a proteoform corresponds to a particular species, determining a conditional likelihood for a candidate microbe, and identifying a best match for an unknown microbe species. The steps reciting identifying a likelihood a proteoform corresponds to a particular species and determining a conditional likelihood for a candidate microbe are, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0042]-[0044]) discloses that likelihood estimates are mathematically represented as P(M|B) (e.g., in Bayesian terms P(M|B) represents the conditional probability of observing molecular weight M given that it is microbe species B). Additionally, since there are no specifics in the methodology, identifying a best match for an unknown microbe species, is something that under BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
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)). 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 following independent claims recite limitations that equate to additional elements:
Claims 1 and 15 recite “determining a plurality of proteoform values from spectral information derived from mass spectral analysis of a sample comprising an unknown microbe species”.
Regarding the above cited limitation in claims 1 and 15 of (i) determining a plurality of proteoform values from spectral information derived from mass spectral analysis of a sample comprising an unknown microbe species. This limitation equates to insignificant, extra-solution activity of mere data gathering because these limitations gather data before the recited judicial exceptions of identifying a likelihood a proteoform corresponds to a particular species, determining a conditional likelihood for a candidate microbe, and identifying a best match for an unknown microbe species (see MPEP § 2106.04(d)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claim 3 further limits the experimental conditions for the mass spectral analysis; and claim 13 recites an insignificant extra-solution step of generally outputting a result (see MPEP § 2106.05(g)). As such, claims 1-15 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitation in claims 1 and 15 of (i) determining a plurality of proteoform values from spectral information derived from mass spectral analysis of a sample comprising an unknown microbe species. This limitation, when viewed individually and in combination, is a well-understood, routine, and conventional (WURC) limitation as taught by Dupré et al. (Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination. J. Proteome Res. 20(1): 202-211 (2021); published 9/15/2020; cited in the IDS dated 3/30/2023). Dupré et al. discloses a liquid chromatography (LC)-MS/MS top-down proteomics platform, which aims at discriminating closely related pathogenic bacteria through the identification of specific proteoforms (Abstract). Dupré et al. further discloses the analysis of the MS data using the Proteome Discoverer software, which includes spectral deconvolution and deisotoping. The spectra were then searched using a three-tier search tree with searches against the appropriate databases to match mass values (Pg. 204, Col. 2, Para. 2). This analysis was used to discriminate pathogens from twelve different bacterial strains of Salmonella, Shigella, and E. coli species (limitation (i)) (Pg. 208, Col. 1, Para. 5).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-15 are not patent eligible.
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.
1. Claims 1-5 and 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dupré et al. (Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination. J. Proteome Res. 20(1): 202-211 (2021); published 9/15/2020; cited in the IDS dated 3/30/2023) in view of LeDuc et al. (The C-Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics. J Proteome Res. 13(7): 3231-3240 (2014); published 6/12/2014; cited in the IDS dated 03/30/2023).
Regarding claim 1, Dupré et al. teaches a liquid chromatography (LC)-MS/MS top-down proteomics (TDP) platform, which aims at discriminating closely related pathogenic bacteria through the identification of specific proteoforms. Using Escherichia coli as a model, all steps of the workflow were optimized: protein extraction, on-line LC separation, MS method, and data analysis. They used this platform for the discrimination of enterobacterial pathogens undistinguishable by MALDI-TOF (i.e., a method for identifying a microbe species) (Abstract). Dupré et al. further teaches that an Orbitrap Fusion Lumos mass spectrometer fitted with a nano-electrospray ionization source was used for all experiments (Pg. 204, Col. 1, Para. 2). All data were processed with ProSight PC v4.1 and Proteome Discoverer v2.4. Spectral data were first deconvoluted and deisotoped using the cRAWler algorithm. The spectra were then searched using a three-tier search tree with searches against the appropriate Uniprot XML database to match mass values (Pg. 204, Col. 2, Para. 2). The analysis was used to discriminate pathogens from twelve different bacterial strains of Salmonella, Shigella, and E. coli species (i.e., determining a plurality of proteoform values from spectral information derived from mass spectral analysis of a sample comprising an unknown microbe species) (Pg. 208, Col. 1, Para. 5).
Regarding claims 8 and 9, Dupré et al. teaches that in order to develop the workflow dedicated to bacterial proteome analysis, they optimized the MS parameters for intact proteins using an E. Coli K12 lysate. Different options for LC separation were then studied (i.e., wherein the sample comprises a complex mixture and wherein the complex mixture comprises a cell lysate) (Pg. 204, Col. 2, Para. 3).
Regarding claim 10, Dupré et al. teaches that proteoform analysis includes examining the “type” of proteoforms identified by ProSight. The analysis of their data revealed that many proteoforms obtained correspond to truncated sequences. Looking at the proteoform molecular mass distribution, they clearly observed that smaller size species were identified with specific buffers, in line with the high number of truncated proteoforms (i.e., wherein the proteoform value comprises a mass value) (Pg. 208, Col. 1, Para. 2).
Regarding claim 11, Dupré et al. teaches an example of the total ion chromatograms (TICs) obtained for the top-down proteomics (TDP) analysis of E. coli with the six LC conditions. Corresponding extracted ion chromatogram (XIC) of six randomly chosen proteoforms are plotted in black dash line with stars of different colors. The XIC are performed at 5 ppm using the three most intense isotope ions of the three most charge state protein ions (i.e., wherein the mass value comprises a monoisotopic mass value) (Supporting Information, Pg. S6-S7, Fig. S1).
Regarding claim 12, Dupré et al. teaches that the platform was used to evaluate its capacity to discriminate pathogens. Specifically, twelve different bacterial strains of Salmonella, Shigella, and E. coli species were chosen for analysis (i.e., wherein the unknown microbe species are selected from the group consisting of bacteria, yeast, and fungi) (Pg. 208, Col. 1, Para. 5).
Regarding claim 13, Dupré et al. teaches that the specific proteoforms clearly show that the platform can be used to discriminate closely related bacterial pathogens that cannot be differentiated with MALDI-TOF MS. This important result is also highlighted in the phylogenetic tree built from all TDP data, in which Salmonella, E. coli, and Shigella species are separated, with E. coli and Shigella being more closely related than Salmonella. These data appoint that the platform can not only be used as an identification method but also as a powerful tool for microbial proteogenomics (i.e., providing an identification of the candidate microbe species that is the best match to a user) (Pg. 208, Col. 2, Para. 5 – Pg. 209, Col. 1, Para. 1; and Pg. 206, Fig. 4).
Regarding claim 14, Dupré et al. teaches the clustering of the twelve enterobacteria using the identified proteoforms and associated proteoform identification scores (i.e., wherein the identification comprises a score) (Pg. 206, Fig. 4).
Regarding claim 15, Dupré et al. teaches that the TDP platform performs the four main steps of the workflow: sample preparation (in particular lysis buffer), online LC separation of intact proteins, MS/MS analysis, and database search for proteoform identification (i.e., a system for carrying out the method of claim 1) (Pg. 209, Col. 1, Para. 2).
Dupré et al. does not teach for one or more of the proteoform values identifying a likelihood the proteoform corresponds to a particular microbe species, wherein the proteoform value belongs to a subset of informative proteoform values for the candidate microbe species (claim 1); determining a conditional likelihood for a plurality of candidate microbe species using the identified likelihoods for each proteoform (claim 1); identifying the conditional likelihood of the candidate microbe species that is a best match to the unknown microbe species (claim 1); wherein, the subset of informative proteoform values is determined using the proteoform values from a plurality of training samples (claim 2); wherein, the proteoform values from the plurality of training samples are derived under the same experimental conditions as the plurality of proteoform values from the unknown microbe species (claim 3); wherein, the training samples comprise samples from different candidate microbe species (claim 4); and wherein, the training samples comprise a replicate sample from at least one of the candidate microbe species (claim 5).
Regarding claim 1, LeDuc et al. teaches a Bayesian approach to the proteoform identification and characterization (Abstract). LeDuc et al. further teaches that the observed data in a canonical MS/MS experiment includes (a) the neutral precursor mass, which gives the total molecular weight of the proteoform under study, and (b) a set of neutral fragment ion masses, that is, masses of the products resulting from fragmentation of the proteoform. Also required is a database of possible proteoforms, each of which serves as a “hypothesis” that could potentially explain the observed MS/MS data. The database of possible proteoforms was generated by combinatorial expansion of all potential proteoforms using the known modification information in the UniProt Knowledgebase (Pg. 3234, Col. 1, Para. 3). In the collected data set, 295 proteoforms representing a unique proteoform were selected for use (e.g., from a single species and not containing multiple proteins being fragmented simultaneously) (i.e., for one or more of the proteoform values identifying a likelihood the proteoform corresponds to a particular microbe species, wherein the proteoform value belongs to a subset of informative proteoform values for the candidate microbe species) (Pg. 3236, Col. 2, Para. 3). LeDuc et al. further teaches that the problem of inferring which proteoform, from the articulated “prior” list of proteoforms in a database, was observed within the mass spectrometer is well-suited to a Bayesian approach. In this case, Bayes law can be rearticulated as follows:
P
r
(
P
r
o
t
e
o
f
o
r
m
i
|
D
a
t
a
M
S
/
M
S
)
=
Pr
P
r
o
t
e
o
f
o
r
m
i
P
r
(
D
a
t
a
M
S
/
M
S
|
P
r
o
t
e
o
f
o
r
m
i
)
P
r
(
D
a
t
a
M
S
/
M
S
)
, where (1)
P
r
(
P
r
o
t
e
o
f
o
r
m
i
|
D
a
t
a
M
S
/
M
S
)
is read as the probability of the ith Proteoform given the MS/MS data, and is known as the posterior probability of proteoform i given the observed data; (2)
Pr
P
r
o
t
e
o
f
o
r
m
i
is known as the prior probability of proteoform i; (3)
P
r
(
D
a
t
a
M
S
/
M
S
|
P
r
o
t
e
o
f
o
r
m
i
)
is read as the probability of the data given proteoform i, and is known as the likelihood of the data given proteoform i; and (4)
P
r
(
D
a
t
a
M
S
/
M
S
)
is known as the probability of the data. By convention, this can be taken as the sum of all prior probabilities multiplied by their likelihoods across all hypotheses (i.e., determining a conditional likelihood for a plurality of candidate microbe species using the identified likelihoods for each proteoform) (Pg. 3232, Col. 2, Para. 2 – Pg. 3233, Col. 1, Para. 1). LeDuc et al. further teaches that to be precise, they will define the following variables to restate Bayes law. Let
M
O
= observed precursor mass,
m
i
= mass of the ith of n observed fragment ions, so
{
m
i
}
i
=
1
n
is the set of all n observed fragment ions, and
ϕ
q
= the qth of k candidate proteoforms in the database. The posterior probability of hypothesis
ϕ
q
, can be restated as
P
r
(
ϕ
q
|
M
O
,
m
i
)
=
Pr
ϕ
q
P
r
(
M
O
,
m
i
|
ϕ
q
)
P
r
(
M
O
,
m
i
)
, where
Pr
ϕ
q
is the “prior” term and
P
r
(
M
O
,
m
i
|
ϕ
q
)
is the “likelihood” term (Pg. 3233, Col. 1, Para. 2). LeDuc et al. further teaches that it is possible to calculate a posterior probability
P
r
(
ϕ
q
|
M
O
,
m
i
)
for every sequence
ϕ
q
in the database. This posterior probability is proportional to the likelihood
P
r
(
M
O
,
m
i
|
ϕ
q
)
since we assume uniform priors. Therefore, their search for the maximum a posteriori hypothesis
ϕ
q
is equivalent to a maximum likelihood estimation (MLE) search, that is, we report the
ϕ
q
that maximizes
P
r
(
M
O
,
m
i
|
ϕ
q
)
(Pg. 3235, Col. 2, Para. 7). When the data are sufficient to completely characterize a proteoform, the score is often well above 40, indicating hyperconfidence in the characterization power of the underlying data (Pg. 3238, Col. 1, Para. 2). An example of a very high score is shown in Fig. 5A, where the score corresponds to a fully characterized proteoform of protein cytochrome b-c1, O14957 (Pg. 3237. Fig. 5). Additionally, the was successfully able to identify and characterize the correct proteoform from the data set of 295 human test cases, as well as the 136 Pseudomonas test cases (i.e., identifying the conditional likelihood of the candidate microbe species that is a best match to the unknown microbe species) (Pg. 3237, Col. 2, Para. 2).
Regarding claim 2, LeDuc et al. teaches that the human data files used in this analysis were acquired using the following method. Mitochondrial membrane proteins were isolated from HeLa S3 cells and separated using a GELFrEE 8100 Fractionation system. For the analysis at hand, 295 top down experiments from a nanoLC-Velos Orbitrap Elite MS analysis of a GELFrEE fraction containing ∼15−20 kDa proteins were selected for intensive manual interrogation to provide a set of highly curated “known positives” to test parameter sets within the generative models used in development of scoring framework. Additionally, for the Psuedomonas data set, the Pseudomonas aeruginosa were grown on rich media to mid-log phase, were isolated by centrifugation, lysed, separated with GELFrEE, and analyzed with mass spectrometry using the methods referenced above. For the secondary data set, 136 top down experiments were manually curated to select true-positives for analysis (i.e., wherein the subset of informative proteoform values is determined using the proteoform values from a plurality of training samples) (Pg. 3236 , Col. 1, Para. 4 – Col. 2, Para. 2).
Regarding claim 3, LeDuc et al. teaches that the same mass spectrometry analysis methods are used for the human data files as well as the Pseudomonas dataset as described for claim 2 above (i.e., wherein the proteoform values from the plurality of training samples are derived under the same experimental conditions as the plurality of proteoform values from the unknown microbe species) (Pg. 3236, Col. 1, Para. 4 – Col. 2, Para. 2).
Regarding claim 4, LeDuc et al. teaches that the mass spectrometry analysis of human data files and Pseudomonas aeruginosa for training and true positive validation of the scoring method (i.e., wherein, the training samples comprise samples from different candidate microbe species) (Pg. 3236 , Col. 1, Para. 4 – Col. 2, Para. 2).
Regarding claim 5, LeDuc et al. teaches the use of samples for training and validation as “true answers” (Pg. 3236, Col. 1, Para. 4 – Col. 2, Para. 2). LeDuc et al. further teaches that experimental conditions (e.g., overfragmentation, generating internal ions) and the selection of data processing parameters will affect the quality of data and thus the fraction of ions matched for each experiment (Pg. 3236, Col. 2, Para. 2). While LeDuc et al. does not explicitly teach the use of replicate samples in training, it would be obvious to one of ordinary skill in the art to use replicate samples in training, because LeDuc et al. discloses that the selection of data processing parameters will affect both the quality of the data and fraction of ions matched. Using replicate samples (similar to the replicate samples disclosed by Dupré et al., see at least Pg. 208, Col. 1, Para. 1 and 5-6) will improve the quality of the data (i.e., wherein the training samples comprise a replicate sample from at least one of the candidate microbe species).
Therefore, regarding claims 1-5 and 8-15, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of discriminating bacteria using a MS/MS proteomics platform of Dupré et al. with the likelihood calculation and training samples of LeDuc et al. because the likelihood analysis of LeDuc et al. provides a platform for identifying and characterizing proteins by extracting the maximum value from MS-based proteomics platforms in an automated fashion (LeDuc et al., Pg. 3238, Col. 2, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Dupré et al. with LeDuc et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for analyzing and identifying proteoforms. Therefore, regarding claims 1-5 and 8-15, the instant invention is prima facie obvious (MPEP § 2142).
2. Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Dupré et al. in view of LeDuc et al. as applied to claims 1-5 and 8-15 above, and further in view of Xue et al. (Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry. Anal. Chem. 89(17): 9039-9047 (2017); published 7/18/2017).
Dupré et al. in view of LeDuc et al., as applied to claims 1-5 and 8-15 above, does not teach determining a variance value for each proteoform over all the training samples (claim 6); ranking the variances of the proteoform values using an F statistical test (claim 6); selecting the subset of informative proteoform values from the ranking (claim 6); and wherein the F statistical test comprises an analysis of variance test (claim 7).
Regarding claim 6, Xue et al. teaches a method for analyzing mass spectrometry data for peptide arrays (Abstract). Xue et al. further teaches the training of a subset of peptides using the mass spectrometry data (Pg. 9040, Col. 2, Para. 2). Xue et al. further teaches that they compared the variance in replicates of peptides in the top 20% of S/N to those in the bottom 20% (i.e., determining a variance value for each proteoform over all of the training samples) (Pg. 9044, Col. 1, Para. 1). Xue et al. further teaches that a one-sided F-test verified that the top 20% peptides have lower replicate variance than the lower 20% across all three conditions and across both time points (p < 10−10 for all cases) (i.e., ranking the variances of the proteoform values using an F statistical test) (Pg. 9044, Col. 1, Para. 1). Xue et al. further teaches that this finding suggests that peptides with low signal to noise ratio have unrepresentative (or possibly random) signals, and they should be weighted less during analysis to avoid misled conclusions (i.e., selecting the subset of informative proteoform values from the ranking) (Pg. 9044, Col. 1, Para. 1).
Regarding claim 7, Xue et al. teaches that the variance was compared by using a one-sided F-test (i.e., wherein the F statistical test comprises an analysis of variance test) (Pg. 9044, Col. 1, Para. 1).
Therefore, regarding claims 6-7, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of discriminating bacteria using a MS/MS proteomics platform of Dupré et al. in view of LeDuc et al. with the variance based subset selection method of Xue et al. because the variance based analysis of Xue et al. enables values with higher variance to be weighted less, thereby avoid any misled conclusions when using all values as compared to only a selective subset of values (Xue et al., Pg. 9044, Col. 1, Para. 1). One of ordinary skill in the art would be able to combine the teachings of Dupré et al. in view of LeDuc et al. with Xue et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for selecting a subset of training data. Therefore, regarding claims 6-7, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIANA P SANFORD whose telephone number is (571)272-6504. The examiner can normally be reached Mon-Fri 8am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571)272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
Prosecution Insights