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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in
37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/03/2025 has been entered.
Claim Status
Claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 are currently pending and under examination herein.
Claims 20 and 27 are canceled.
Claims 3, 5, 7, 6, 9, 11, 19, 23-24, and 28-33 were previously canceled.
Claims 36-37 are added as new claims.
Priority
The instant application claims the benefit of priority to U.S. Provisional Application No. 62/750,357 filed on 10/25/2018. Accordingly, the effective filing date of the claimed invention is 10/25/2018.
Claim rejection - 35 USC§ 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 17 and 37 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 17 recites " The method of claim 1, wherein said set of peptides is proteins…" in line 1, which has unclear antecedence. Claim 1, does not instantiate “set of peptides”. As such, failing to particularly point out and distinctly claim the subject matter.
Instant claim 37 is rendered indefinite by virtue of its dependency to claim 17.
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 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1 and 20 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 1 recites analyzing linear readout with a machine learning model; the limitation analyzing with a machine learning model is considered a mathematical calculation, since it involves mathematical calculations such as mean and standard deviation and calculating the probability using softmax activation function (specification [0125]), as such, the recited limitation falls within mathematical concepts groupings of abstract ideas.
Claim 1 further recites predicting the identity of peptide; the limitation predicting is considered a mathematical calculation since it involves mathematical calculations of calculating a probability using a softmax activation function, and as such, falls within mathematical concepts groupings of abstract ideas. Also, the said limitation can be practically performed in human mind (mental process), since human mind is capable of predicting based on the result of calculations.
Claim 1 further recites identifying a peptide; the limitation identifying can be practically performed in human mind, since human mind is capable of identifying based on the result of an analysis. As such, the recited limitation falls withing mental processes groupings of abstract ideas.
Claim 4 recites that the machine learning model is trained on linear readouts of a set of peptides;
the limitation training a machine learning model is considered a mathematical calculation, since it involves calculations, such as Learned Common representation (LCR), (specification [0103]). As such, the recited limitation falls within mathematical concepts groupings of abstract ideas.
Claims 2, 8, 10, 12-18, 21-22, 25-27, and 35-37 provide further information.
Additionally, claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 recite a correlation between amino acid sequence and peptide identification, and as such, falls into judicial exception of Laws of nature and natural phenomena. See MPEP 2106(b) I.
The identified claims recite a law of nature, a natural phenomenon (product of nature) or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 include the following.
Claim 1 recites providing a peptide wherein at least a portion of said first amino acid with a first label and at least a portion of said second amino acid with a second label along said peptide, detecting said first and said second label linearly along said peptide as it passed through a nanopore.
Claim 8 recites label comprises a fluorophore and an optical sensor at said nanopore is configured to detect fluorescence at said nanopore.
Claim 10 recites a plasmonic nanostructure to localize electromagnetic excitation below a wavelength of light.
Claim 27 recites at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor perform the method of claim 20.
Claim 34 recites denaturing peptides before labeling.
The additional elements of a system, a processor, a non-transitory computer-readable storage medium, and program instructions are generic computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional elements of providing, detecting, labeling, and using a plasmonic nanostructure serve to collect the information for use by the abstract idea.
Therefore, these additional elements amount to insignificant extra-solution activity, which is not sufficient to integrate the recited judicial exception into a practical application. See MPEP 2106.05(g). Thus, claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The additional elements of claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 include the following.
Claim 1 recites providing a peptide wherein at least a portion of said first amino acid with a first label and at least a portion of said second amino acid with a second label along said peptide, detecting said first and said second label linearly along said peptide as it passed through a nanopore.
Claim 8 recites label comprises a fluorophore and an optical sensor at said nanopore is configured to detect fluorescence at said nanopore.
Claim 10 recites a plasmonic nanostructure to localize electromagnetic excitation below a wavelength of light.
Claim 27 recites at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor perform the method of claim 20.
Claim 34 recites denaturing peptides before labeling.
The additional elements of a system, a processor, a non-transitory computer-readable storage medium, and program instructions are conventional computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements of providing, detecting, labeling, and using a plasmonic nanostructure amount to nothing more than gathering the data necessary to perform the abstract idea, and as such, considered insignificant extra-solution activity. The courts have identified limitations that merely gather data as insignificant extra-solution activity that does not amount to significantly more. See MPEP 2106.05(g).
Furthermore, the additional elements of providing peptides, denaturing peptides, labeling amino acids, detecting fluorescence, and plasmonic nanostructure amount to well-understood, routine, and conventional methods and systems in nanopore technology. This position is supported by Taylor et al. (Single-Molecule Plasmon Sensing: Current Status and Future Prospects, ACS Sensors, Vol 2/Issue 8, August 1, 2017). Taylor reviews recent advances in single molecule detection using plasmonic metal nanostructures as a sensing platform. Taylor further teaches that Single-molecule detection has long relied on fluorescent labeling with high quantum-yield fluorophores. Plasmon-enhanced detection circumvents the need for labeling by allowing direct optical detection of weakly emitting and completely nonfluorescent species (abstract). Taylor further discloses protein denaturing prior to labeling (pg.1114, col. 2, para. 1).
Additionally, Restrepo-Perez et al. (Paving the way to single-molecule protein sequencing, Nature Nanotechnology volume 13, pages786–796, 09/06/2018) discusses advantages and drawbacks of single-molecule protein sequencing techniques and discloses various strategies for "fingerprinting" proteins by labeling a subset of amino acids, such as cysteine and lysine. Restrepo-Perez addresses the hurdle of incomplete labeling (labeling inefficiency), noting that even a small percentage of missed labels can significantly complicate the identification of proteins against a reference database and that a CK fingerprinting method could accurately identify a major percentage (>70–80%) of proteins even when high error rates (20–30%) were considered (abstract; pgs. 786-789, cols. 1 and 2).
Therefore, the additional elements are not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Applicant’s Arguments
Applicant's arguments filed 09/03/2025 have been fully considered but they are not persuasive. Applicant states:
claim 1 is amended to recite the active steps of providing a peptide labeled with two labels wherein the labeling is less than 90% efficient and then passing that labeled peptide through a nanopore and determining the peptide's identity with at least 90% accuracy. The method now recites specific active steps that are not routine and amount to significantly more than the judicial exception.
Instant claim 1 is now tied to the performance of a specific assay, nanopore detection, and greatly improves this assay. As will be discussed with regards to the 103 rejections below, it was heretofore impossible to label proteins with less than 90% efficiency and still produce a prediction that was at least 90% accurate.
This is a substantial improvement in nanopore sequencing technology. As the method now directly ties into improving a physical technology and as the method is not routine the instant claims amount to more than the judicial exception.
In light of these arguments, reconsideration and withdrawal of the rejection under 35 U.S.C. § 101 is respectfully requested.
It is respectfully submitted that this is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception. As stated above, the additional elements of a system, a processor, a non-transitory computer-readable storage medium, and program instructions are generic computer components and/or processes, and thus, do not integrate the judicial exception into a practical application. See MPEP 2106.05(f). Furthermore, the additional elements of providing, detecting, labeling, and using a plasmonic nanostructure serve to collect the information for use by the abstract idea and amounts to necessary data gathering and thus, do not integrate the judicial exception into a practical application. See MPEP 2106.05(g)(3). These additional elements are considered insignificant extra-solution activities and does not integrate the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept. See MPEP 2106.05(g)(3).
Taken as a whole, the instant claims are directed to judicial exception of identifying peptides using a mathematical algorithm. It is important to note, the judicial exception alone cannot provide the improvement (See MPEP 2106.04(d) III). The improvement must be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).
Additionally, with respect to applicant submitting that “it was theretofore impossible to label proteins with less than 90% efficiency and still produce a prediction that was at least 90% accurate”, examiner submits that although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973. As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
Therefore, the rejection of claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-37 under U.S.C 101 is maintained.
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, 4, 13-14, 18, 21, 26 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Yao et al. (Single-molecule protein sequencing through fingerprinting: computational assessment, 2015 Aug 12;12(5):055003) from the 05/18/2023 IDS form, in view of Misiunas et al. (QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing, May 30th 2018, published by Nano Lett. 2018 Jun 13; 18(6): 4040–4045) from the 05/18/2023 IDS form.
Regarding claim 1, Yao discloses a method of identifying a peptide (…computationally demonstrating that it suffices to measure only two types of amino acids to identify proteins (abstract)).
Yao further discloses receiving a linear readout representative of at least a portion of a first amino acid and at least a portion of a second amino acid along said peptide (a 2-bit fingerprinting scheme in which only two types of amino acids are labeled (figure 1), pg. 1, col. 2).
Yao further discloses providing a peptide wherein at least a portion of said first amino acid with a first label and at least a portion of said second amino acid with a second label along said peptide (We chose to label two highly nucleophilic amino acids, lysine (K) and cysteine (C) as they are frequent (pg. 1, col. 2, para. 1)) and detecting said first and said second label linearly along said peptide to produce said readout (the order of C’s and K’s are detected (figure. 1); Proteins are sequenced using FRET (Förster resonance energy transfer). The translocase is labeled with a donor dye. FRET occurs between the donor on the translocase and the two distinct acceptor dyes on a substrate when the substrate passes through the nanomachine. The FRET signals report the order of the labeled amino acids (figure 2).
Yao further discloses that in an ideal situation with no experimental error, detection precision P is 90% (figure 3(a), blue). Yao further discloses that at α = 10%, half of the sequences are correctly and uniquely retrieved (figure 3(a), blue, see also, 0% mislabeling, 5% mislabeling, 10% mislabeling, 15% mislabeling, 20% mislabeling and 30% mislabeling); reading on limitations of wherein said labeling with said first label and said labeling with said second label is 90% efficient or less.
Yao further discloses that detecting comprises passing said labeled peptide though a nanopore, wherein said first and second labels are uniquely detectable as each label passes through said nanopore (passing labeled peptides through protein translocase of figure 2; see also, fingerprinting using nanopores (pg. 2, col.1, Ls. 1-3)).
Yao further discloses analyzing said linear readout and thereby identifying a peptide (Search algorithm as computational assessment to correctly identify proteins with reference to proteomic databases (pgs. 2-5)).
Yao further discloses a dynamic-based programing for alignment and search (pgs. 2-4), thereby implicitly teaches one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor.
Further regarding claim 1, Yao discloses that at α= 10%, P increased to 85% with the distance included. Yao further discloses determinizing a set of HRSV and TB proteins contain a unique CK fingerprint and thus can be detected at α as high as 15–20% and be potentially used as markers for HRSV and TB (supplementary figure 6) (pg. 2, col. 2, para. 1-3). Yao further discloses Detection Precision of more than 90% at 10% error rate (supplementary figure 5 (a-b)).
Further regarding claim 1, Yao does not expressly disclose using machine learning model to analyze readouts. Misiunas discloses using a convolutional neural network for extracting information from nanopore sequencing data for the purpose of protein identification (abstract). Misiunas further disclose a method of using a predictive convolutional neural network (CNN) as the machine learning approach because of their suitability for detecting local patterns (pg. 4041, Methods; col. 1, para. 1). Misiunas further discloses training, testing, and adjusting the model (pg. 4040, col. 2, last para., Table 1).
Misiunas further discloses developing a convolutional neural network (CNN) for the fully automated extraction of information from the time-series signals obtained by nanopore sensors, thereby teaches one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor (abstract).
Regarding claim 4, Yao disclose that each linear readout represents at least a portion of said first amino acid and at least a portion of said second amino acid along a peptide from said set of peptides (a 2-bit fingerprinting scheme in which only two types of amino acids are labeled (figure 1), pg. 1, col. 2)
Further regarding claim 4, Misiunas discloses that machine learning model is trained on linear readouts of a set of peptides (we use the previously mentioned multiplexed protein sensing data set.1 The data set contains separate control measurements for each specific barcode, without other bit permutations present in the solution. This automatically provides labeled data to train the supervised learning model (pg. 4040, col. 2, para. 2)).
Regarding claims 13 and 26, Yao discloses that linear readout is a linear temporal trace of said peptide as it passes through said nanopore (donor photobleaching can be determined from SM time traces, pg. 3 col. 2, L.10). Additionally, Misiunas discloses extraction of information from the time-series signals obtained by nanopore sensors (abstract).
Regarding claim 14, Yao peptide is an undigested or unfragmented protein (individual proteins are translocated, figure 2).
Regarding claim 18, Yao discloses that linear readouts of a set of peptides comprise at least 50 linear readouts representative of each peptide from said set, are simulated linear readouts based on a known sequence for each peptide wherein at least a portion of said first amino acid and a portion of said second amino acid are represented in said simulated readout or both (We simulated 2000 read-outs, each for a different protein. The proteins are randomly picked from the database and thus contain random amino acids and fingerprint lengths. Next, to assess the robustness of the method against inaccuracies that are expected from actual experiments, errors are iteratively introduced for each read-out up to the error level we want to investigate (pg. 3, col. 1, Error Simulation)).
Regarding claim 21, Yao discloses a canonical human proteome database based on Uniprot. They simulated 2000 different read-outs, searched for each of them in the database and measured the detection precision (pg. 2, col. 1, para. 1). Yao further discloses simulating 2000 read-outs, each for a different protein (pg. 3, col. 1, Error Simulation)
Further regarding claim 21, Misiunas discloses that in nanopore-based DNA sequencing, a recurrent neural network improves the precision of DNA sequencing by generating large amounts of training data …peak localization in noisy data sets can be trained using DNA with known modification positions. Also, running QuipuNet against simulated data sets (generated classically or with generative adversarial networks) could guide the design of the DNA structures in order to maximize the information density or readout accuracy (pg. 4044, col. 2, last para.); also, use a previously published data set on multiplexed single-molecule protein sensing (abstract)).
Regarding claim 34, Yao discloses denaturing peptides before labeling with first and second label (Figure 2, (b)).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao to have used the machine learning model for protein identification from amino acid readouts, as shown by Misiunas (pg. 3, Results and Discussion; Fig. 1) for fully automated extraction of information to achieve an improved predictive accuracy, as stated by Misiunas (abstract). There would be a reasonable expectation of success in combining the technique of Misiunas to the method of Yao because they are all identifying proteins using nanopore technology.
Claims 2 and 35-36 are rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, and further in view of Marcotte et al. (US-20250164498-A1).
Claim 2 depends from claim 1. The limitations of claim 1 have been taught in the above rejections.
Regarding claims 2 and 35-36, Yao discloses that at α= 10%, P increased to 85% with the distance included. Yao further discloses determinizing a set of HRSV and TB proteins contain a unique CK fingerprint and thus can be detected at α as high as 15–20% and be potentially used as markers for HRSV and TB (supplementary figure 6) (pg. 2, col. 2, para. 1-3). Yao further discloses Detection Precision of more than 90% at 10% error rate (supplementary figure 5 (a-b)).
Yao and Misiunas do not expressly disclose that labeling is at least 60% and at least 70% efficient. Marcotte discloses a method of identifying a protein or peptide [0068] using nanopore sequencing [0098], where the results showed that even under a 90% efficient labeling regime their strategy can still identify a substantial portion of the proteome [0128]. Marcotte further discloses that the IPL simulations demonstrating that the vast majority of a proteome can be resolved even under suboptimal labeling efficiencies (below those observed in practice), for example at least 60% and/or 70%, 80% labeling efficiency, are directly applicable to this technique [0142].
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and Misiunas to have used the labeling efficiency of Marcotte, based on the finding that the prior art contained a comparable method that was improved the same way as the invention for fully automated extraction of information to achieve an improved predictive accuracy. There would be a reasonable expectation of success in combining the technique of Marcotte to the method of Yao and Misiunas because they are all identifying proteins using nanopore technology.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, and further in view of Joo et al. (US20150185199A1).
Claim 8 depends on claim 1. The limitations of claim 1 have been taught in the above rejection.
Regarding claim 8, Yao discloses fingerprinting using fluorescence; the translocase is labeled with a donor dye. FRET occurs between the donor on the translocase and the two distinct acceptor dyes on a substrate when the substrate passes through the nanomachine. The FRET signals report the order of the labeled amino acids (Figure 2).
Yao and Misiunas do not expressly disclose an optical sensor at said nanopore is configured to detect fluorescence at said nanopore. However, Joo discloses donor and acceptor fluorophores and that with K and C residues labeled with two different colors of acceptor dyes respectively (Cy5 and Cy7), the acceptor molecules can be probed by scanning with a Cy3 donor molecule and measuring FRET of Cy5 and Cy7 fluorescence signals with Cy3 [0027]. Joo further discloses that the order of labeled amino acids and their distance can be determined from the sensor signal [0051] and imaging the translocation process using an electron-multiplying CCD camera [0044].
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and Misiunas to have used a at least %60 of amino acid, as shown by Joo ([0016, [0019], and [0066]]) to have enough amino acids to identify protein [0016]. There would be a reasonable expectation of success in combining the technique of Joo to the method of Yao and Misiunas because they all use amino acid read outs to identify proteins.
Claims 10, 17, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, and further in view of Mir (US20170227520A1).
Claims 10 and 17 are dependent on claim 1. The limitations of claim 1 have been taught in the above rejections.
Regarding claim 10, Yao discloses that the CK fingerprinting technique can be expanded to a three-colored fluorescence measurement, for example, glycosylated amino acids can be labeled with a third acceptor dye using hydrazide–aldehyde coupling chemistry, which is orthogonal to the labeling methods for lysine and cysteine residues. Phosphorylated serine and threonine can be labeled with a third acceptor using another coupling scheme.
Yao and Misiunas do not expressly disclose details about the nanopore. However, Mir discloses using plasmonic structure to achieve signal enhancement [0108]. Mir further discloses adding a stain or intercalating dye to a pore comprising DNA origami or nanostructure, which emit light at a higher wavelength than that at which they are excited [0103 - 0104].
Regarding claim 17, Misiunas discloses that the set of peptides is a set of peptides selected from: a. a set of peptides with known sequences (in nanopore-based DNA sequencing, a recurrent neural network improves the precision of DNA sequencing by generating large amounts of training data …peak localization in noisy data sets can be trained using DNA with known modification positions. Also, running QuipuNet against simulated data sets (generated classically or with generative adversarial networks) could guide the design of the DNA structures in order to maximize the information density or readout accuracy (pg. 4044, col. 2, last para.); also, use a previously published data set on multiplexed single-molecule protein sensing (abstract)).
Yao and Misiunas do not expressly disclose that set of peptides is proteins found in plasma. Mir discloses Collecting or acquiring a sample cells, tissues or organisms; in the case of blood, preferably isolating plasma [0026].
Regarding claim 37, Yao discloses reading only two amino acid types, we could correctly identify proteins with reference to proteomic databases. When this entirely new SM protein sequencing approach is achieved, it will become a proteomics tool that complements MS and opens up new avenues in global, high-throughput protein analysis (pg. 2, col. 2 last para.).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and, Misiunas to have used a plasmonic structure to localize electromagnetic excitation below a wavelength of light, and to amplify emission at a plurality of wavelengths, as shown by Mir [0103 - 0108]) to achieve signal enhancement. There would be a reasonable expectation of success in combining the technique of Mir to the method of Yao and Misiunas because they all use fluorescence emission to identify proteins.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, in view of Soskine et al. (Tuning the size and properties of ClyA nanopores assisted by directed evolution, J Am Chem Soc. 2013 Sep 11; 135(36): 13456–13463, Published online 2013 Aug 27), and further in view of Wendell et al. (Translocation of double-stranded DNA through membrane-adapted phi29 motor protein nanopores, nature nanotechnology, published online 27 September, 2009).
Claim 12 depends on claim 1. The limitations of claim 1 have been taught in the above rejections.
Regarding claim 12, Yao and Misiunas do not expressly disclose that nanopore has a resolution of at least 100 nm. However, Soskine discloses that because proteins have a compact folded structure, the diameter of the nanopore should be similar to that of the protein (pg. 2, para. 1). Additionally, Wendell discloses that the giant liposome/connector complex must be extruded using a polycarbonate membrane with pore sizes of 200 nm or 400 nm (pg. 771, col. 1, para.1).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and Misiunas to have used a pore size of at least 100 nm, as shown by Soskine (pg. 2, para. 1) and Wendell (pg. 771, col. 1, para.1) to accommodate translocation of different size proteins. There would be a reasonable expectation of success in combining the technique of Soskine and Wendell to the method of Yao and Misiunas because they all use nanopore technology.
Claims 15, 16, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, in view of Swaminathan et al. (A Theoretical Justification for Single Molecule Peptide Sequencing, PLOS Computational Biology | DOI: 10.1371/journal.pcbi.1004080 February 25, 2015).
The limitations of claims 1 and 20 have been taught in the above rejections.
Regarding claim 15 and 25, Yao discloses that there is a fingerprinting scheme that is based on multiple labels (pg. 1, col. 2 last para. - pg. 2, col. 1, first para.)).
Yao and Misiunas do not expressly disclose that the linear readout is further representative of a portion of at least a third amino acid along said peptide. However, Swaminathan discloses a strategy, termed fluorosequencing, for sequencing peptides in a complex protein sample at the level of single molecules. In the proposed approach, millions of individual fluorescently labeled peptides are visualized in parallel, monitoring changing patterns of fluorescence intensity as N-terminal amino acids are sequentially removed, and using the resulting fluorescence signatures (fluorosequences) to uniquely identify individual peptides (abstract). Swaminathan further disclose labeling multiple amino acids (Fig. 1).
Regarding claim 16, Yao discloses that the first and second amino acids are lysine and cysteine (We chose to label two highly nucleophilic amino acids, lysine (K) and cysteine (C) as they are frequent (pg. 2, col. 2, last para.; Figure 2)). Yao and Misiunas do not expressly disclose the third amino acid is methionine.
However, Swaminathan discloses using multiple fluorosequences to identify individual peptides (abstract). Swaminathan further discloses labeled methionine (Met) alongside other labeled residues for identifying proteins (Figure 2).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and Misiunas to have used a third amino acid read out, as shown by Swaminathan (abstract; pg. 2, col. 2, last para.; Figure 2) to improve protein identification. There would be a reasonable expectation of success in combining the technique of Swaminathan to the method of Yao and Misiunas because they all use fluorescence to identify peptides.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Yao and Misiunas, as applied to claims 1, 4, 13-14, 18, 21, 26 and 34 above, in view of Tran et al. (Protein identification with deep learning: from abc to xyz, arXiv: 1710.02765, 08 October 2017), and further in view of Mir (US20170227520A1).
Claim 22 depends on claim 21. The limitations of claim 21 have been taught in the above rejection.
Regarding claim 22, Yao and Misiunas do not expressly disclose that training set comprises linear readouts of all proteins found in plasma, or all proteins found in a proteome. However, Tran discloses a comprehensive protocol of DeepNovo for protein identification, including training neural network models, dynamic programming search, database querying, estimation of false discovery rate, and de Bruijn graph assembly. Training and testing data, model implementations (abstract). Tran further discloses that the dataset is a dataset of Saccharomyces cerevisiae proteome (pg. 8, Results).
Further regarding claim 22, Yao and Misiunas and Tran do not expressly disclose that set of peptides is proteins found in plasma. Mir discloses Collecting or acquiring a sample cells, tissues or organisms; in the case of blood, preferably isolating plasma [0026].
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Yao and Misiunas to have used a training set with linear readouts of all proteins found in plasma, or all proteins found in a proteome., as shown by Tran (pg. 8, Results) since proteome is much more dynamic (abstract) and use proteins in plasma, as stated by Mir. There would be a reasonable expectation of success in combining the technique of Tran and Mir to the method of Yao and Misiunas because they all analyze peptides for protein identification.
Response to Applicant’s Arguments
Applicant's arguments filed 09/03/2025 have been fully considered but they are not persuasive. Applicant states:
while it is true that Yao tests poor labeling, Yao cannot produce the level of accuracy recited by the claim when poor labeling occurs. Instant claim 1 recites that the peptide can be predicted with at least 90% accuracy. As is clear in Figure 3a, Yao cannot achieve this level of accuracy when the labeling has an error of at least 10%. For the CK method, a 10% error rate produces an accuracy of only about 60%, well below the threshold required by claim 1. When the CK-distance method is used a 10% error rate still only produces an accuracy of about 80%. Still below the 90% accuracy required by instant claim 1.
No combination of the art teaches at least 10% mislabeling and still achieving 90% accuracy. Since the art does not teach each and every aspect of claim 1 it is non-obvious.
It can further be argued that this level of accuracy is a wholly unexpected result. A skilled artisan would not have predicted this level of accuracy was possible with this level of mislabeling. The results shown in Figures 4D, 6A and 6B are very surprising when compared to Figure 3a of Yao. These surprising results are a secondary consideration that renders instant claim 1 non-obvious.
It is respectfully submitted that this is not persuasive. As stated above, Yao in supplementary data figure 5 a-b discloses detection precision above 90% at 10% error level.
As such, the rejections of claims under U.S.C 103 is maintained.
Conclusion
No claims are allowed.
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/G.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686