DETAILED ACTION
The amendment filed on 03/11/2026 has been entered and fully considered. Claims 1-4, 6-9 and 11-20 are pending, of which claim 1 and 6-7 are amended.
Response to Amendment
In response to amendment, the examiner modifies rejection over the prior art established in the previous Office 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 .
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-4, 6-9, 11, 15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orsburn et al. (BioRxiv, 2020) (Orsburn).
Regarding claim 1, Orsburn teaches a method for selecting a combination of peptides to identify one or more disease states in a subject using mass spectrometry (abstract), the method comprising:
selecting the one or more disease states (flow chart on page 2; page 4, par 3);
collecting information on proteins associated with the one or more disease states being present within the subject (flow chart on page 2; page 4, par 3; page 8, par 1);
in silica digesting individual proteins associated with the one or more disease state to obtain possible workflow peptides (theoretical trypsin digest) (flow chart on page 2; page 6, par 1);
collecting filtering data associated with the possible workflow peptides, wherein the filtering data comprises physicochemical data (page 4, par 1-3);
(flow chart on page 2,
applies multi-stage filtering including:
• removing peptides with homology to human proteome (page 4, par 3)
• chemical characteristic similarity filters (Summary)
• microbiome background abundance (page 7, par 0, page 14, par 3)
• similar-symptom pathogens (page 7, par 0)
All explicitly disclosed in Orsburn’s four-stage elimination pipeline);
analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution (flow chart on page 2; page 10, par 1); and
selecting the combination of peptides from the possible workflow peptides based on the analyzing step
(flow chart on page 2, “unique in one or more of… m/z, peptide sequence, or hydrophobicity… distinguish between even highly similar peptides… including sequences… chemically modified” (page 3, par 5, page 4, par 0),
“The resulting final list contains the 24 peptides most unique and diagnostic of SARS-CoV-2 infections” (Summary)).
Orsburn expressly teaches selecting peptides for mass spectrometry based on physicochemical properties affecting detectability, including chromatographic behavior and detectability constraints (page 4, par 1-2).
Orsburn teaches that: “Peptide retention times can be calculated based on amino acid hydrophobicity…” (page 4, par 1).
Further, Orsburn’s peptide selection pipeline evaluates peptides for mass spectrometry suitability (page 4, par 2), which inherently includes ionization efficiency, as ionization efficiency directly impacts peptide detectability in MS-based workflows. Selection of peptides for MS detection necessarily requires consideration of whether peptides can be ionized and detected with sufficient signal.
Thus, even if Orsburn does not explicitly use the exact phrase “ionization efficiency,” the reference teaches selecting peptides based on properties that directly correspond to ionization and detectability, which would have been understood by a person of ordinary skill in the art to include ionization efficiency.
Moreover, combining retention time prediction with detectability/ionization considerations represents nothing more than the routine combination of known MS optimization parameters, which are commonly jointly considered in peptide selection workflows.
Therefore, the limitation of:
“filtering data comprises ionization efficiency and retention time data”
is either expressly taught or at least obvious over Orsburn in view of the general knowledge in the art.
Regarding claim 2-3, Orsburn teaches selecting peptides that are diagnostic of a disease state (COVID-19). To avoid false positives from other diseases with similar clinical presentation, Orsburn removes peptides that overlap with multiple additional disease states, including:
• Influenza
• Rhinovirus
• Pneumonia-causing pathogens
• Respiratory syncytial virus (RSV)
• Staphylococcus aureus
• Streptococcus species
(“pathogens with similar clinical presentation… Influenza, Middle East Respiratory, Pneumoniae, Respiratory Syncytial Virus, Rhinovirus, Staphylococcus aureus, Streptococcus reviewed”) (page, 15, par 1)
Thus, Orsburn necessarily considers and analyzes peptides in the context of multiple disease states.
A person of ordinary skill in the art would have been motivated to select more than one disease state to:
(i) increase diagnostic specificity,
(ii) avoid cross-reactivity, and
(iii) allow proper classification among multiple diseases causing similar symptoms,
with a reasonable expectation of success. (KSR v. Teleflex, 550 U.S. 398 (2007)).
Orsburn explicitly evaluates peptides against multiple pathogens with similar clinical presentation (e.g., influenza, RSV, rhinovirus, pneumonia-causing organisms) to ensure diagnostic specificity.
Even if Orsburn ultimately selects peptides for a primary disease (COVID-19), the reference nonetheless:
• analyzes peptides across multiple disease states, and
• uses that analysis to inform peptide selection (page 4, par 3)
A person of ordinary skill in the art would have recognized that such analysis can be extended to simultaneous identification or discrimination among multiple disease states, since the underlying dataset and filtering already include those disease states.
Accordingly, modifying Orsburn to explicitly select peptides for two or more disease states would have been an obvious extension motivated by:
improving diagnostic breadth,
enabling differential diagnosis, and
reducing assay redundancy,
with a reasonable expectation of success (KSR).
Orsburn teaches:
• aligning sequences from multiple viral isolates
• identifying variable regions
• removing peptides that are not conserved across variants (page 1)
Thus, Orsburn inherently performs peptide selection across multiple variants of the same disease state.
Even if framed as “removal of variable peptides,” this still requires:
evaluating two or more variants of the disease state, and
selecting peptides based on that multi-variant analysis.
Therefore, the claimed limitation of selecting based on two or more variants is explicitly suggested by Orsburn and, at minimum, obvious.
Regarding claim 4, Orsburn teaches evaluating multiple variants of the SARS-CoV-2 disease state by aligning protein sequences from numerous distinct viral isolates, identifying variant positions, and selecting peptides that remain conserved across variants to ensure diagnostic reliability:
• “aligning the protein sequences of SARS-CoV-2 field isolates deposited to date” (Summary)
• “identify peptides for removal due to their presence in highly variable regions…” (Summary)
• “multiple phosphorylation sites observed… further strengthening our confidence…” (page 11, par 1)
• Figures 3A and 3B illustrate identification of variant-impacted regions
These passages show that Orsburn explicitly teaches:
selects peptides based on disease-variant conservation
evaluates multiple sequence variants within one disease state (COVID-19)
eliminates peptides that fail across variants
Thus, Orsburn considers two or more variants of the SARS-CoV-2 disease state when selecting the diagnostic peptide panel. It would have been obvious to one of ordinary skill in the art to select two or more variants of the one or more disease states for consideration.
Orsburn’s entire purpose is to generate a panel (set/combination) of diagnostic peptides for disease detection. The reference explicitly describes producing a final list of peptides suitable for diagnostic workflows.
A “panel,” “set,” or “list” of peptides constitutes a combination of peptides as claimed.
Therefore, this limitation is expressly taught by Orsburn.
Regarding claim 6, Orsburn teaches that wherein the filtering data comprises homology data (page 4, par 3).
Regarding claim 7, Orsburn teaches that wherein the filtering data comprises abundance of peptide data (frequency%) (page 4, par 3).
Regarding claim 8, Orsburn teaches that wherein the filtering data comprises data associated with the one or more disease states (page 7, par 2).
Regarding claim 9, Orsburn teaches that wherein the filtering data comprises physicochemical data and abundance of peptide data (page 4, par 1-3).
Regarding claim 11, Orsburn teaches that wherein the physicochemical data comprises one or more of (a) length of possible workflow peptides (page 6, par 2); (b) MRM transition data on co-eluting or close eluting possible workflow peptides (page 10, par 1-2; page 11, par 0); and (c) amino acid sequences contained within the possible workflow peptides (page 6, par 2).
Regarding claim 13, Orsburn teaches that the method further comprising eliminating possible workflow peptides using the filtering data prior to analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution (page 10, par 2).
Regarding claim 14, Orsburn teaches that the method further comprising eliminating possible workflow peptides after analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution using filtering data (page 10, par 2).
Regarding claim 15, Orsburn teaches that wherein analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution comprises applying a statistical approach (determine the protein targets with the highest abundance as well as the highest likelihood of being diagnostic in different assays) (page 4, par 3; page 6, par 1).
Regarding claim 18, Orsburn teaches that wherein analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution comprises analyzing coverage for a yes/no result for the disease state (clearly unique and diagnostic of SARS-CoV-2) (page 7, par 2).
Regarding claim 19, Orsburn teaches that wherein analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution comprises analyzing coverage for a determination of a particular variant of the disease state (page 14, par 2).
Regarding claim 20, Orsburn teaches that wherein analyzing the possible workflow peptides for coverage of the one or more disease states and for mass spectrometry detection and resolution comprises analyzing coverage for a determination of a particular disease state from a group of possible disease states (page 7, par 2).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orsburn et al. (BioRxiv, 2020) (Orsburn) in view of Suzuki et al. (Scientific Reports) 2016) (Suzuki).
Regarding claim 12, Orsburn does not specifically teach that wherein the filtering data comprises data on methionine being present within amino acid sequences contained within the possible workflow peptides. However, Suzuki teaches that methionine sulfoxides in serum proteins is a potential clinic biomarker for disease (title). Suzuki teaches that “Methionine (Met) oxidation is a mechanism by which proteins perceive oxidative stress and function in redox signaling8. Met residues are highly susceptible to modification by mild oxidants and can be oxidized spontaneously during common experimental procedures. In vitro Met oxidation is a reversible process, and is dependent upon solvent accessibility, and structural determinants. Met oxidation can also modify the physicochemical properties of the whole protein and therefore modulate its function.” (page 1, par 2). Thus, it would have been obvious to one of ordinary skill in the art to include peptides containing methionine in their sequence as the possible work flow peptides, in order to target diseases caused by oxidation stress.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orsburn et al. (BioRxiv, 2020) (Orsburn) in view of Li et al. (Journal of Computational Biology, 2009) (Li).
Regarding claim 16, Orsburn does not specifically teach that wherein the statistical approach comprises Bayesian inference. However, Li teaches the statistical approach comprises Bayesian inference (abstract). Li teaches that “The protein inference problem represents a major challenge in shotgun proteomics. In this article we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification” (abstract). It would have been obvious to one of ordinary skill in the art to include Bayesian inference in the workflow peptide filtering, in order to improve the peptide detectabilities.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Orsburn et al. (BioRxiv, 2020) (Orsburn) in view of Kou et al. (Journal of Proteome Research, 2019) (Kou).
Regarding claim 17, Orsburn does not specifically teach that wherein the statistical approach comprises a Markov Chain Monte Carlo algorithm. However, Kou teaches statistical approach comprises a Markov Chain Monte Carlo algorithm (abstract). Kou teaches that “we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry.” (abstract). It would have been obvious to one of ordinary skill in the art to include Markov Chain Monte Carlo algorithm in in silico digestion and workflow peptide filtering, in order to find best matching peptides.
Response to Arguments
Applicant's arguments filed 03/11/2026 have been fully considered but they are not persuasive.
I. Arguments Directed to Amended Claim 1 (Filtering Data Limitation)
Applicant argues that Orsburn is “silent with respect to filtering data comprising both ionization efficiency and retention time data.”
This argument is not persuasive.
Orsburn expressly teaches selecting peptides for mass spectrometry based on physicochemical properties affecting detectability, including chromatographic behavior and detectability constraints (page 4, par 1-2).
Orsburn teaches that:
“Peptide retention times can be calculated based on amino acid hydrophobicity…” (page 4, par 1).
Further, Orsburn’s peptide selection pipeline evaluates peptides for mass spectrometry suitability (page 4, par 2), which inherently includes ionization efficiency, as ionization efficiency directly impacts peptide detectability in MS-based workflows. Selection of peptides for MS detection necessarily requires consideration of whether peptides can be ionized and detected with sufficient signal.
Thus, even if Orsburn does not explicitly use the exact phrase “ionization efficiency,” the reference teaches selecting peptides based on properties that directly correspond to ionization and detectability, which would have been understood by a person of ordinary skill in the art to include ionization efficiency.
Moreover, combining retention time prediction with detectability/ionization considerations represents nothing more than the routine combination of known MS optimization parameters, which are commonly jointly considered in peptide selection workflows.
Therefore, the limitation of:
“filtering data comprises ionization efficiency and retention time data”
is either expressly taught or at least obvious over Orsburn in view of the general knowledge in the art.
II. Arguments Directed to Claims 2–3 (Multiple Disease States / Variants)
Applicant argues that Orsburn:
Does not select peptides to identify two or more disease states, and
Only uses other disease states for exclusion purposes.
These arguments are not persuasive.
A. Multiple Disease States (Claim 2)
Orsburn explicitly evaluates peptides against multiple pathogens with similar clinical presentation (e.g., influenza, RSV, rhinovirus, pneumonia-causing organisms) to ensure diagnostic specificity.
Even if Orsburn ultimately selects peptides for a primary disease (COVID-19), the reference nonetheless:
• analyzes peptides across multiple disease states, and
• uses that analysis to inform peptide selection (page 4, par 3).
A person of ordinary skill in the art would have recognized that such analysis can be extended to simultaneous identification or discrimination among multiple disease states, since the underlying dataset and filtering already include those disease states.
Accordingly, modifying Orsburn to explicitly select peptides for two or more disease states would have been an obvious extension motivated by:
improving diagnostic breadth,
enabling differential diagnosis, and
reducing assay redundancy,
with a reasonable expectation of success (KSR).
B. Variants of Disease States (Claim 3)
Applicant’s arguments do not address that Orsburn explicitly evaluates sequence variability across multiple viral isolates.
Orsburn teaches:
• aligning sequences from multiple viral isolates
• identifying variable regions
• removing peptides that are not conserved across variants (page 1)
Thus, Orsburn inherently performs peptide selection across multiple variants of the same disease state.
Even if framed as “removal of variable peptides,” this still requires:
evaluating two or more variants of the disease state, and
selecting peptides based on that multi-variant analysis.
Therefore, the claimed limitation of selecting based on two or more variants is explicitly suggested by Orsburn and, at minimum, obvious.
III. Arguments That Orsburn Does Not Teach “Selecting a Combination of Peptides”
Applicant repeatedly argues that Orsburn does not teach:
“selecting a combination of peptides to identify one or more disease states…”
This argument is not persuasive.
Orsburn’s entire purpose is to generate a panel (set/combination) of diagnostic peptides for disease detection. The reference explicitly describes producing a final list of peptides suitable for diagnostic workflows.
A “panel,” “set,” or “list” of peptides constitutes a combination of peptides as claimed.
Therefore, this limitation is expressly taught by Orsburn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOYUN R XU, Ph. D. whose telephone number is (571)270-5560. The examiner can normally be reached M-F 8am-5pm.
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/XIAOYUN R XU, Ph.D./ Primary Examiner, Art Unit 1797
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