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 .
Claim Status
Claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-39 are currently pending and under examination herein.
Claims 34-35 are canceled.
Claims 3, 5, 7, 6, 9, 11, 19-20, 23-24, and 27-33 were previously canceled.
Claims 38 and 39 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.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are withdrawn in view of the amendments filed 05/19/2026.
All rejections of claims 34 and 35 are withdrawn; their cancelation moots the
rejections.
The 35 U.S.C. 112(b) rejections to claims 17 and 37 in the office action filed 01/28/2026 has been withdrawn in view of amendments received 05/19/2026 (pg. 6) specifically by correcting the antecedent basis in claim 17.
The 35 U.S.C. 103 rejections in the office action filed 01/28/2026 has been withdrawn in view of amendments received 05/19/2026. The amendments necessitated a new round of art rejection.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
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, and 36-39 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 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, and 36-39 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 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 1 further 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 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 35-39 provide further information.
Additionally, claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, 34-35 and 36-39 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 36-39 include the following.
Claim 1 recites providing a denatured 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.
Claim 38 recites nanopore is a solid-state nanopore.
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).
MPEP 2106.04(d). I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, Prong 1 above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, Prong 2, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination, it is not yet the case that any of the Step 2A, Prong 2 considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the first consideration at MPEP 2106.04(d)(1), the record, including for example the specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field. The claims do not yet clearly result in such an improvement.
In conclusion regarding Prong 2, claims 1-2, 4, 8, 10, 12-18, 21-22, 25-26, and 36-39 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, and 36-39 include the following.
Claim 1 recites providing a denatured 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.
Claim 38 recites nanopore is a solid-state nanopore.
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 denatured 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; as cited in 892 form dated 09/18/2024). 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). Taylor further discloses that solid-state and biological nano pores exploit changes in the current through a nanometer sized pore when a molecule passes through it or binds to receptors near the pore (pg. 1103, col. 1, last para.). Taylor further discloses that the protein is denatured protein (pg. 1114, col. 2. Para. 1, Figure 15).
Additionally, Mir (US20210382033A1; as cited in 892 form dated 09/18/2024) discloses methods for determining the identity of individual protein molecules in a complex mixture by unfolding the protein into a polypeptide, tagging selected residues on the polypeptide with selected oligonucleotide sequence tags that recognize selected residues on said polypeptide, and then detecting the oligonucleotide sequence tags that the protein is denatured/unfolded into a polypeptide form and the linear length of the polypeptide is determined and features along its length (abstract). Mir further discloses using plasmonic nanostructures [0108].
Additionally, Restrepo-Perez et al. (Paving the way to single-molecule protein sequencing, Nature Nanotechnology volume 13, pages 786–796, 09/06/2018; as cited in 892 form dated 01/28/2026) 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).
Additionally, Chee (US20250102513A1; as cited in the attached 892 form) discloses methods of using the kits for analyzing macromolecules, including peptides, polypeptides, and proteins, employing nucleic acid encoding to generate molecular interaction and/or reaction information, and/or polypeptide sequence information. The kits may be used in high-throughput, multiplexed, and/or automated analysis, and are suitable for analysis of a proteome or subset thereof (abstract). Chee further discloses sequencing using nanopore, where the nanopore is inorganic solid-state nanopore [2028]. Chee further discloses first, second, and third labels attached to residues lysine, cysteine and methionine [0029-0031] [2102];
Additionally, Calin (Data analysis methods for solid-state nanopores, Nanotechnology 26 (2015) 084003 (7pp), Published 3 February 2015, pages 1-7; as cited in attached 892 form) discloses methods for the detection of the local baseline and propose a new detection algorithm that bypasses some of the classical weaknesses of moving-average detection in solid-state nanopores (abstract). Calin further discloses that iterative operation of this algorithm causes both the moving average of the baseline current and its standard deviation to converge (abstract).
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 05/19/2026 have been fully considered but they are not persuasive.
Applicant states (pg. 2, para. 1):
...while it is true that the instant claim makes use of an abstract idea (machine learning being a mathematical concept) it is not directed to the abstract idea. The invention is well embedded within the technological field of peptide analysis and outputs the identity of the peptide which is a tangible output. The method is therefore not directed to math but merely makes use of it.
It is submitted that these arguments are not yet persuasive. These Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically that whether the claims recite a judicial exception.
Taken as a whole, the instant claims are directed to judicial exception of identifying peptides using a mathematical algorithm. With regards to Applicant stating that the output is tangible, Examiner submits that the output of the machine learning model is identification of a peptide/data, and data is not tangible, in particular not so as to result in something significantly more than the identified judicial exceptions. Therefore, the claims recite one or more judicial exceptions.
Applicant further states (pg. 2, para. 2):
The use of inorganic nanopores with incomplete labeling for protein identification is not routine or well established and was never successfully done by anyone in the art prior to the priority date of the instant application, because inorganic nanopores heretofore transduced the protein too quickly though the pore.
It is submitted that these arguments are not yet persuasive. These Applicant remarks are directed to Step 2B of 101 analyses, specifically evaluating additional elements to determine whether they amount to an inventive concept by considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself.
As interpreted above, 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 that does not provide significantly more; 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 additional elements of providing peptides, denaturing peptides, labeling amino acids, detecting fluorescence, solid-state nanopore, and plasmonic nanostructure amount to well-understood, routine, and conventional methods and systems in nanopore technology. Therefore, all the recites additional elements are well-understood, routine, and conventional.
Applicant further states (pg. 2, para.3):
None of what is taught in the claim can be performed by a human mind. A human mind cannot process a training set of tens of linear temporal traces to find similarities to traces from known proteins. This comparison of linear readouts is beyond human capacity. This can only be done by a machine learning algorithm. A rejection based on a mental process relies on practical feasibility of mental performance, which is absent here.Each trace has tens of data points, and each protein has at least 50 traces. Practically speaking, there is no way a human mind can analyze this data.
It is submitted that these arguments are not yet persuasive. These Applicant remarks are directed to Step 2A Prong One of 101 analysis, specifically whether the claims recite a judicial exception.
As stated in the above rejections, training a machine learning/mathematical algorithm falls into mathematical concept groupings of abstract ideas, NOT mental processes, as noted by the applicant.
Whether the human mind is equipped to perform a task is not linked to the scope of the task. There is not a threshold at which point training a machine learning model/iteratively performing mental and/or mathematical processes graduates from what can be performed by the mind to not performable by the human mind. While this may take a long time, the use of a physical aid, such as a pen-and-paper or computer, may accelerate this process, and this does not negate the mental nature of the limitation. Applicant will further note that complexity of operations does not equate to eligibility. The fact remains that the steps are directed to operations that are mental and/or mathematical as above.
Applicant further states (pg. 2, last para.):
...instant claim 1 is now tied to the performance of a specific assay, inorganic nanopore detection, and greatly improves this assay. The Office Action argues that the use of nanopores for improving protein identification is known and routine and cited Restrepo-Perez and Taylor. However, Applicants assert that even with the use of nanopores in general being known, the improvement in the functioning of this specific technology (inorganic nanopores) renders the claim significantly more than the judicial exception.
Applicant wishes to draw the Examiner's attention to the recent Ex Parte Desjardins decision by the Appeals Review Panel, the Memorandum by the Deputy Commissioner for Patents from December 5th, 2025, and the updates to the MPEP outlined in that Memorandum. In light of these updates, instant claim 1 is directed to more than the judicial exception. In particular, the Office Action's evaluation of Step 2A, Prong two appears to not be in line with these new instructions.
The Ex parte Desjardins decision and the updated MPEP § 2106.04(d)(1) state that the claimed invention should be evaluated for "providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field." (underlined material is the addition to the MPEP). The Office Action does not address whether the claimed invention is "an improvement to other technology" as is now required. Applicants assert that the instant method is a marked improvement in nanopore protein analysis, in particular an improvement in protein identification with inorganic nanopores.
It is submitted that these arguments are not yet persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements improves over the previous state of the technological field.
Taken as a whole, instantly claimed invention is directed to judicial exception of protein identification. It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. See MPEP 2106.05(a) II. Similarly, instant claims might facilitate protein identification (judicial exception) but does not improve computer of technology (for example, inorganic nanopore technology).
Further regarding Applicant referring to Ex parte Desjardins, Examiner submits that in Desjardin the improvement was to how the machine learning model itself operates (improvement to the machine learning architecture), and not, for example, mathematical calculations (abstract ideas).
“The independent claim in Ex parte Desjardins contained specific limitations as to how at least some aspects of the asserted improvements are achieved:
"When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." Ex parte Desjardins, p9
In contrast, the claims do not clearly set forth the link between the data gathered, the initial training of the ML, the structure of the ML, and how training or retraining affects the structure to obtain the desired results or asserted improvement.
Further with regards to Applicant referring to August 2025 Memo, Examiner stated that “An improvement in the judicial exception itself is not an improvement in the technology. For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) (Stanford I), Applicant argued that the claimed process was an improvement over prior processes because it ‘‘yields a greater number of haplotype phase predictions,’’ but the Court found it was not ‘‘an improved technological process’’ and instead was an improved ‘‘mathematical process.’’ The court explained that such claims were directed to an abstract idea because they describe ‘‘mathematically calculating alleles’ haplotype phase,’’ like the ‘‘mathematical algorithms for performing calculations’’ in prior cases. Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible (FR89 no.137, p58137, 7/17/2024).
Therefore, the U.S.C. 101 rejection 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 1-2, 4, 8, 10, 12-15, 17-18, 21-22, 25-26, and 36-39 are rejected under 35 U.S.C. 103 as being unpatentable over Mir (US20210382033A1; as cited in the attached 892 form), in view of Marcotte (US20250164498A1), and further 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, Mir teaches a method for determining the identity of individual protein molecules in a complex mixture by unfolding the protein into a polypeptide, tagging selected residues on the polypeptide with selected oligonucleotide sequence tags that recognize selected residues on said polypeptide, and then detecting the oligonucleotide sequence tags (abstract).
Mir further teaches labeling the proteins at one type of residue with first and second distinct label or tag [0030-0031].
Mir further teaches that in a substantial number of cases the efficiency of labeling may not reach 100% but a sufficient number of labels are achieved per molecule to identify the molecule; reading on limitations of a method of identifying a peptide, comprising: a. providing a denatured peptide wherein at least a portion of a first amino acid is labeled with a first label and at least a portion of a second amino acid is labeled with a second label along said peptide.
Further regarding claim 1, Mir does not expressly disclose that labeling is 90% efficient or less. 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].
Mir further discloses that the length of the polypeptide is analyzed by passing it through a nanopore or nanogap [0003] [0017]. Mir further teaches that solid-state, biological or hybrid nanopores can be used for detection [0103]. Mir further teaches analyzing the linear length comprises translocating the polypeptide through a detection station (e.g., nanopore, nanogap) and making real time recordings of physical phenomena as each residue along the polypeptide comes into the proximity of the station. In some embodiments, the physical phenomena is an optical signal [0016]; reading on limitations of passing said denatured peptide through a nanopore to produce a linear readout representative of at least a portion of said first amino acid and at least a portion of said second amino acid along said peptide, wherein said nanopore does not comprise a protein for facilitating transfer through said nanopore and said linear readout is produced by detecting said first and said second label linearly along said peptide as it passed through said nanopore.
Mir further teaches identifying the protein by comparison of the experimentally derived details or patterns to a database of the expected details or patterns of known proteins and performing computational analysis on each protein to access its length, the location of residues along its length [0022] [0131].
Further regarding claim 1, Mir and Marcotte do not expressly teach that the computational analysis is done with a machine learning model to analyze readouts with at least 90% accuracy. Misiunas discloses using a convolutional neural network for extracting information from nanopore sequencing data for the purpose of protein identification, where the nanopore is solid-state nanopore (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). Misiunas further discloses identifying peptides with more than 90% accuracy (pg. 4044, col. 2, first para.).
Regarding claim 2, 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] (Fig. 5c); reading on limitations of wherein said labeling is at least 60% efficient.
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)); reading on limitations of wherein said machine learning model is trained on a training set comprising linear readouts of a set of denatured peptides, wherein 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 denatured peptide from said set of denatured peptides.
Regarding claim 8, Mir discloses Optical imaging and scanning methods can also be used for detection. Typically, the labels should be fluorescent dyes, particles or other structures or light-scattering particle [0105] [0192] [0204]; reading on limitations of wherein said label comprises a fluorophore and an optical sensor at said nanopore is configured to detect fluorescence at said nanopore, or said label is a bulky group and an electrical sensor at said nanopore is configured to detect electrical current and/or voltage at said nanopore.
Regarding claim 10, Mir discloses that signal enhancement is achieved by proximity to a metal or by plasmonic effects, including those achieved by using plasmonic structures such as a bow-tie or bulls eye [0108]; reading on limitations of wherein said nanopore contains a plasmonic nanostructure, wherein said plasmonic nanostructure is configured to localize electromagnetic excitation below a wavelength of light, to amplify localized fluorescence emission at said nanopore at a plurality of wavelengths, or both.
Regarding claim 12, Mir discloses that the nanopores are typically 10-30 nm in thickness, preferably around 16 nm, and 2-25 nm in diameter [0204]; reading on limitations of wherein said nanopore has a resolution of 50 nm or worse.
Regarding claims 13 and 26, Mir discloses rendering the polypeptide such that the distance between tag sites along the polypeptide is determined via the time elapsed between detection of tags (claim 1). Additionally, Misiunas discloses extraction of information from the time-series signals obtained by nanopore sensors (abstract); reading on limitations of wherein said linear readout is a linear temporal trace of said denatured peptide as it passes through said nanopore.
Regarding claim 14, Mir discloses that native proteins (for example, undigested/unfragmented protein) must first be unfolded into linear one-dimensional strings [0068]; reading on limitations of wherein said peptide is an undigested or unfragmented protein.
Regarding claims 15 and 25, Mir discloses that the label is on one or more bases [0137] and that Three amino acid residues, tryptophan tyrosine and phenylalanine are intrinsically fluorescent [0122]; reading on limitations of wherein said linear readout is further representative of a portion of at least a third amino acid along said denatured peptide.
Regarding claim 17, Mir discloses collecting or acquiring a sample cells, tissues or organisms; in the case of blood, preferably isolating plasma [0027]; reading on limitations of wherein said set of denatured peptides is denatured proteins found in plasma and wherein said peptide is a peptide found in plasma.
Regarding claim 18, Mir discloses dentifying proteins, e.g., proteins present in a complex mixture, such as plasma; inherently disclosing at least 50 linear readouts [0003] [0027]. Mir further discloses that in order to access the least abundant or rarest proteins, the number of proteins that must be analyzed is huge [0134].
Mir further discloses that an experimentally derived pattern of labels or data for label location distance between and/or order of, is compared to one or more in silico generated patterns of known proteins or to the sequence of known proteins [0014].
Misiunas discloses running QuipuNet against simulated data sets (pg. 4044, col. 2, last para.) where the dataset comprises thousands of readouts (Table 1); reading on limitations of wherein said linear readouts of a set of denatured 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.
Regarding claim 21, Mir discloses dentifying proteins, e.g., proteins present in a complex mixture, such as plasma; inherently disclosing at least 50 linear readouts [0003] [0027]. Mir further discloses that in order to access the least abundant or rarest proteins, the number of proteins that must be analyzed is huge [0134].
Mir further discloses that an experimentally derived pattern of labels or data for label location distance between and/or order of, is compared to one or more in silico generated patterns of known proteins or to the sequence of known proteins [0014].
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)); reading on limitations of wherein said training set comprises linear readouts a. of a set of denatured peptides expected to be in a sample and wherein said target peptide is from said sample; b. for at least 15 denatured peptides and at least 50 readouts for each peptide; c. which are simulated linear readouts generated by selecting a known sequence of a peptide and generating a linear representation of at least a portion of said first amino acids and at least a portion of said second amino acids along said peptide; or d. a combination thereof.
Regarding claim 22, Mir discloses that the samples are of high complexity and dynamic range, e.g., the blood proteome [0003]; reading on limitations of wherein said training set comprises linear readouts of all proteins found in plasma.
Regarding claim 36, Mir and Misiunas do not expressly disclose that labeling is less than 80% 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]; reading on limitations of wherein said labeling with said first label and said labeling with said second label is less than 80% efficient.
Regarding claim 37, Mir discloses that the sample is proteomic sample [0049] [0205]. Misiunas discloses identifying peptides with more than 90% accuracy (pg. 4044, col. 2, first para.); reading on limitations of wherein all plasma proteins are identified with at least 90% accuracy.
Regarding claim 38, Mir discloses that the nanopore is a solid-state nanopore [ 0103]; reading on limitations of wherein said nanopore is a solid-state nanopore.
Regarding claim 39, Mir discloses that nanopore translocation monitors molecules by measuring ionic current blockages. Entry increases the blockage, and exit decreases it. Mir further discloses that a polypeptide slows or stalls due to labels or specific physio-chemical properties causing a longer-lasting blockade [0125]; therefore, it would have been obvious that using two or three labels would slow down the translocation to as much as 0.035-0.2 centimeters per second (cm/s) allowing high-resolution single-molecule optical tracking.
Rationale for combining Mir, Marcotte, and Misiunas
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 Mir 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. Since bulk fluorescently labeled nucleic acid building blocks may be poorly incorporated by polymerases due to steric hindrance of the labels during the polymerization process into DNA resulting in low labeling efficiency. There would be a reasonable expectation of success in combining the technique of Marcotte and Mir because they both are identifying proteins using solid-state nanopore technology.
Further, 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 Mir and Marcotte 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 Mir and Marcotte because they are all identifying proteins using solid-state nanopore technology.
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Mir, in view of Marcotte, and further in view of Misiunas, as applied to claims 1-2, 4, 8, 10, 12-15, 17-18, 21-22, 25-26, and 36-38 above, and further in view of Chee (US20250102513A1; as cited in the attached 892 form).
Claim 16 depends on claims 15 and 1. Limitations of said claims are taught in the above rejections.
Regarding claim 16, Mir discloses detection of the occurrences of cysteine and/or lysines [0134]. Mir further discloses analyzing the polypeptide to determine hydrophobicity by analyzing methionine [0114-0115].
Further regarding claim 16, Mir, Misiunas, and Marcotte do not expressly discloses that one of the amino acids are methionine.
Chee discloses methods of using the kits for analyzing macromolecules, including peptides, polypeptides, and proteins, employing nucleic acid encoding to generate molecular interaction and/or reaction information, and/or polypeptide sequence information. The kits may be used in high-throughput, multiplexed, and/or automated analysis, and are suitable for analysis of a proteome or subset thereof (abstract).
Chee further discloses sequencing using nanopore, where the nanopore is inorganic solid-state nanopore [2028]. Chee further discloses first, second, and third labels attached to residues lysine, cysteine and methionine [0029-0031] [2102]; reading on limitations of wherein said first, second and third amino acids are lysine, cysteine and methionine.
Rationale for combining Mir, Marcotte, Misiunas, and Chee
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 Mir and Marcotte to have used methionine as the amino acid to labeling, since methionine provides direct N-terminus identification and is relatively rare in the proteome, and allows attaching bulky tags to effectively detect peptide read-outs. There would be a reasonable expectation of success in combining the technique of Mir, Marcotte, Misiunas, and Chee because they are all identifying proteins using solid-state nanopore technology.
Response to Applicant’s Arguments
Applicant's arguments filed 05/19/2026 have been fully considered but they are not persuasive. Applicant’s amendments necessitated a new round of art rejections. As such, the combination of Mir, Marcotte, Misiunas, and Chee teach all the limitations of the instant clams.
Conclusion
No claims are allowed.
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.
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/G.S./Examiner, Art Unit 1686
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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