DETAILED ACTION
Applicant's Remarks, filed 11/14/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied in view of instant application amendments. They constitute the complete set presently being applied to the instant application. Herein, ““the previous Office action" refers to the Non-Final rejection of 08/15/2025.
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, 7-8, 11-13, 18-20, 23-27, 29-37, 39, 59-60, 63, 69, 73-74 are pending.
Claims 1, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74 were elected without traverse.
Claims 20, 30-32, 39, 59-60, and 69 are withdrawn from consideration as being drawn to a nonelected species.
Claims 2-6, 9-10, 14-17, 21-22, 28, 38, 40-58, 61-62, 64-68, and 70-72 were cancelled (05/10/2024).
Claims 1, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74 are under exam.
Claims 1, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74 are rejected.
This application is examined under Track One status (renewed petition approved 07/11/2025).
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the 11/14/2025 amendments.
The objection to claims 20, 30-32, and 39 for claim listing informalities is withdrawn, consistent with 10/24/2024 restriction/species election (see MPEP 714).
Priority
As previously recited in the previous Office Action, all claims 1, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74 are examined for an effective filing date of 04/13/2023.
In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
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, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74, are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture, or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
With respect to step (1): yes, the claims are directed to a method and system for de-novo peptide backbones for custom biologic design, therefore the answer is "yes".
With respect to step (2A)(1), the claims recite abstract ideas. To determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon, MPEP at 2106.03 teaches abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships, and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (see MPEP 2106.04(a)(2)). In the instant application, the claims recite the following limitations that equate to an abstract idea with mental steps and mathematical concepts.
With respect to the instant claims, under the step (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular steps for generating a protein design) and mathematical concepts (in particular mathematical relationships for protein modelling).
The claims directing to abstract ideas are as follows:
Mental processes:
Claims 1 and 63: create using the set of final values a generated scaffold model representing the de-novo peptide backbone…provid[ing] the generated scaffold model.
Claim 8: determining initial starting values for the position and/or orientation components of each of the plurality of feature vectors… creating the generated scaffold model representing the de-novo peptide backbone.
Claim 11: each iteration corresponds to one of a plurality of time-points, and (i) the initial velocity field is determined based at least in part on an initial time-point corresponding to the first iteration; each current velocity field associated with and determined at a particular subsequent iteration is determined based at least in part on a particular subsequent time-point corresponding to the subsequent iteration.
Claim 25: value identifies a particular one of a set …classifies protein function…
Claim 27: identifies a particular type… identifies a polarity and/or charge of an amino acid site; classifies and/or measures an extent … classifying a particular amino acid site…classifies and/or measures a secondary structure motif…
Claim 33: populating…the generated scaffold model and a target model/molecule.
Claim 34: determining…sequence velocity fields and using the one or more sequence velocity fields to generate predicted sequence data representing an amino acid sequence of a protein and/or peptide having the de-novo peptide backbone.
Claim 35: determining…side chain geometry velocity fields and using the one or more side chain geometry velocity fields to generate a prediction of a three- dimensional side chain geometry for an amino acid side chain at each of at least a portion of amino acid sites of the de-novo peptide backbone.
Claim 37: each SSE value associated with a particular position (e.g., amino acid site) within a polypeptide chain of the custom biologic and having a value encoding a particular type of secondary structure at the particular position.
Claim 73 and 74: value categorizes/classifies and/or measures protein thermostability/provoking an immune response.
Mathematical concepts:
Claims 1 and 63: generating… a seed set comprising a plurality of feature vectors… a particular backbone site…position and/or orientation…of particular peptide backbone site… determining…one or more velocity fields…updating… values of the position and/or orientation components of the plurality of feature vectors according to the one or more velocity fields, …evolving the values of …feature vectors from a set of initial starting values into a set of final values representing positions and/or orientations of each backbone site…machine learning model.
Claim 7: determining the one or more velocity fields and updating the values of the position and/or orientation components...
Claim 8: determining…using the machine learning model, an initial velocity field based on the initial starting values … updating values … using the updated values from a prior iteration as current values…determining, using the machine learning model, a current velocity field… updating values… using the updated values
Claim 12: generates, as output, the current velocity field.
Claim 13: generates, as output, a set of prospective final values … and the current velocity field is determined based on the set of prospective final values and the time point corresponding to the current iteration.
Claim 18: the machine learning model is or comprises a transformer-based model.
Claim 19: the machine learning model operates on an input graph representation… nodes and edges… representing a particular backbone site/relating two nodes…transformer-based edge retrieval layer … self-attention head(s)… attention weights…determine values of retrieved edge feature vectors.
Claim 23: conditioning generation of the one or more velocity fields according to a set of one or more desired peptide backbone features.
Claim 24: machine learning model receives, as input, and conditions generation of the one or more velocity fields on values one or more global property variables…
Claim 29: conditioning generation of the one or more velocity fields according to a representation of at least a portion of a target molecule and/or one or more particular sub-regions thereof, thereby creating a de-novo peptide backbone suitable for binding to the target molecule.
Claim 36: conditioning generation of the one or more velocity fields according to one or more protein fold representation(s).
Hence, the claims explicitly recite elements that, individually and in combination, constitute abstract ideas.
With respect to step (2A), under the broadest reasonable interpretation (BRI), the instant claims are a method and system for de-novo peptide backbones for custom biologic design. Instant claims are therefore directed to the judicial exceptions of abstract groupings, both mathematical (determining using machine learning model, graph representation, transformer-based model, vectors, velocity fields, weights/self-attention heads, values iterat[ions], condition generation…compute loss FIG 2A, FIG 13) and mental processes (generating… corresponding… identifying… categorizes/classifies… providing…) which can be performed with the human mind with pen and paper.
Because the claims do recite judicial exceptions, direction under step (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim is said to fail to integrate into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements considered for practical application:
Claims 1 and 63: receive and/or generate a seed set…
Claim 12: receives, as input, (i) the current values of the feature vectors and (ii) the time point corresponding to the particular iteration…
Claim 13: receives, as input, (i) the current values of the feature vectors and (ii) the time point corresponding to the particular iteration…
Claim 19: receives on an input graph representation
Claim 24: receives, as input, and
Claim 26: receives, as input …node property variables… particular property/amino acid site.
Claim 29: de-novo peptide backbone
Claim 33: interface designer module, processor, the generated scaffold model and a target model/molecule.
Claims 73 and 74: protein thermostability/immunogenicity variable
Claims 1 and 63: system, processor, computing device, memory
Said steps that are “in addition” to the recited judicial exception in the instant claims represent those of mere instructions or field of use limitations (receiving…a seed set/an input graph representation… receives, as input… using the generated scaffold model as input … protein fold representation(s) are or comprise a set of secondary structure element (SSE) values) to implement in the recited judicial exception and do not impart meaning to said recited judicial exception, such that is applied in a practical manner. Further with respect to the additional elements in the instant claims, these steps direct to mere data gathering and handling (receiving…a seed set/an input graph representation… receives, as input… using the generated scaffold …) to carry out the abstract idea without imposing any meaningful limitation on the abstract idea. Thereby these steps are insignificant extra-solutions activity steps and are insufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further steps herein directed to additional non-abstract elements of computer components (Claims 1 and 63: system, processor, computing device, memory, interface module…) do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than generic computer elements used as a tool to perform the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (FIG 10-11) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer. (see MPEP 2106.05(f)). None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application.
As such, the claims are lastly evaluated using the step (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. The judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering, instructions, and field of use limitations described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant recitations, the claims recite the following additional elements considered for inventive concepts:
Claims 1 and 63: receive and/or generate a seed set…
Claim 12: receives, as input, (i) the current values of the feature vectors and (ii) the time point corresponding to the particular iteration…
Claim 13: receives, as input, (i) the current values of the feature vectors and (ii) the time point corresponding to the particular iteration…
Claim 19: receives on an input graph representation
Claim 24: receives, as input, and
Claim 26: receives, as input …node property variables… particular property/amino acid site.
Claim 29: de-novo peptide backbone
Claim 33: interface designer module, processor, the generated scaffold model and a target model/molecule.
Claims 73 and 74: protein thermostability/immunogenicity variable
Claims 1 and 63: system, processor, computing device, memory
These additional elements do not contribute significantly more to well-known and conventional steps to obtain biochemical data, received from routine laboratory steps, and analyzed with a generic computer by one with ordinary skill in the art as of the effective filing date. These limitations equate to well-understood, routine and conventional activities as further evidenced by Madani A et al. (US20210249105A1: Systems and methods for language modeling of protein engineering; PTO 892 cited, herein Madani) who discloses protein modelling with Natural language models and Guo X et al (2021: Generating tertiary protein structures via interpretable graph variational autoencoders. Bioinformatics Advances, 1(1); PTO 892 cited; herein Guo) who teaches interpretable machine learning graph variational autoencoders to model tertiary protein structures. There is no active step beyond collecting and manipulating data, which are unconventional. Data (seed sets/values/vectors/velocities) are merely manipulated data to be used in the judicial exception. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination, as evidenced by the cited references teaching the combination of elements as well as the individual elements themselves, that transforms the claimed judicial exception into a patent-eligible application of the judicial exception.
With respect to the instant claims, the steps (analyzing biochemical values for protein modeling) and additional elements (protein/peptide backbone representations, chemical property variables, generic computer components, interface design module) involving mathematical relationships and automated mental steps do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 7-8, 11-13, 18-19, 23-27, 29, 33-37, 63, and 73-74 are not patent eligible.
Response to Remarks: 35 USC § 101
Applicant's Remarks (p.12-20), filed 11/14/2025, have been fully considered and are not persuasive regarding the previously stated reasons of record. Any newly applied portion is necessitated by instant application amendment or Applicant Remarks. Applicant asserts:
[p14 -17] Step 2A Prong 2 steps (c) and (d) of claims 1 and 63 are not mental processes: (precedential) Appeals Review Panel (APR) decision (Ex parte Desjardins) at 9 ("Yet, under the panel's reasoning, many AI innovations are potentially unpatentable ... because the panel essentially equated any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation… steps are computer implemented ones, performed by a processor. And these two steps involve creating and storing a scaffold model, which the specification defines as a computer representation [0232]. …performed on a computer…could practically be performed in the human mind… contrary to the claim language itself and the definitions and descriptions provided in the specification.
However, it is respectfully submitted that Applicant’s assertion is not persuasive as independent claims 1 and 63 lack the specificity required by Desjardins in claiming the minimally sufficient steps to achieve the asserted technological improvement of “a highly accurate and robust framework for generating peptide backbones of polypeptide chains of custom biologics.” Independent claims 1 and 63 are extremely broad and claims only the generic traits of a machine learning model: data input (peptide component seeds), a machine environment (generating machine learning model on a processor hardware), which iterates to teach the ML model (evolving the values of the position and/or orientation components) to achieve desired output (using, the set of final values, a generated scaffold model representing the de-novo peptide backbone).
Further, merely asserting [p15] or disclosing the claim steps are performed on processor, or in a computer environment (specification defines as a computer representation [0232]), does not preclude the steps from being performed in the human mind of a biochemist (data is input into the human brain which can direct iteratively direct drawing out multiple possible scaffold representations aided by pen and paper, until a preferred peptide scaffold is selected and output) and thereby, is still a mental process. Merely replacing a biochemist with a generic computer additional element, and further, a generic AI additional element using conventional feature vector math on protein data, does not constitute a practical integration without specific features which transform the generic AI into a specific AI architecture, rather than an “apply it” (it being AI) limitation.
Applicant should consider incorporating into the independent claims 1 and 63 such transforming structural architecture, model steps, layer-integrated mathematical steps, or limitations of the peptide product as found in dependent claims. Importantly, specifics of internal AI architecture as in claim 18 (a transformer-based model), specifics of input/processor layer/mathematical flow-matching/loss functions/weightings as in claim 19 (the machine learning model operates on an input graph representation… nodes and edges… representing a particular backbone site/relating two nodes…transformer-based edge retrieval layer … self-attention head(s)… attention weights…determine values of retrieved edge feature vectors), flow-matching FIGs 1A-C, 57 [0005, 0197-201], [0662-0664: the neural network model comprised Node Encoder, Equivariant Graph Neural Network, and Velocity Predictor. The model was trained end-to-end (e.g., the model learns everything from the beginning to end, not sequentially). The particular form of the model takes into account invariance and equivariance of 3D data with respect to the action of the special Euclidean group which is the semidirect product SE(3) =R3x SO(3)], specifics of conditioning generation as in claims 23, 24, 29, 36, and 37, and specifics of output as in claims 24, 33-34, and 73-74 (global property variable representing a desired property of a protein or peptide [output]… protein thermostability or immunogenicity) [0073: target binding limitations, side chain position/orientation determinations [0685] torsion angle, amino acids]. As in McRO, claiming in independent claims 1 and 63 the certain rules (what type of machine learning model, what weighted parameters types which modify the generic AI and define the feature vectors, what conditions generating the particular desired trait possessed by the peptide output of torsion angles/thermostability, etc. which determines when the MLM/AI stops iterating/evolving) will provide the steps necessary for one of ordinary skill in the art to achieve the asserted improvement to technology. Not only can such amendments rooted in the specification (e.g. FIG 7; [0022-0031 and 0034-0041]) advance prosecution in terms of 101 patent eligibility, but they also reduce potential 112 a and 112b issues that may arise for enablement (any de novo peptide backbone) and infringement.
[p16-17] None of the recited elements are mathematical formulas or equations, mathematical relationships, or mathematical calculations…There is a distinction between claims that recite an exception and claims that merely USPTO's August 2025 Memorandum contrasted two published USPTO examples. The memorandum explained that the hypothetical claim language "training the neural network" in example 39 does not recite a judicial exception because it did not expressly set forth or describe mathematical relationships, calculations, formulas, or equations such as specific algorithms like the backpropagation algorithm or gradient descent algorithm that were recited in published example 47.
However, this is not persuasive as the cited example 39 is a different fact pattern from the instant application
First of all, there is agreement with Applicant assertion [p18] that practical integration through improvement does not need to manifest in a physical way. The Office Action merely explained the McRO improvement manifested steps directly leading to a distinct quality change in the resulting images (which happened to be displayed physically) which distinguished it from the state of the art.
Instant claims 1 and 63 do correlate with Example 39, reciting a neural network environment (generating…machine learning model) and is trained with a mathematical concept velocity fields from an algorithm [0036]. As previously discussed, the mathematical concepts of velocity fields ( Wikipedia “in continuum mechanics the flow velocity in fluid dynamics…is a vector field used to mathematically describe the motion of a continuum. The length of the flow velocity vector is scalar, the flow speed…also called velocity field; “the current velocity field is determined based on the set of prospective final values and the time point corresponding to the current iteration (e.g., generating a current estimate of xi as output of the machine learning model and computing the current velocity field based on xi and the current time point) [0017]) is a judicial exception/abstract idea, and so, cannot serve as an additional element or an inventive concept.
[p20] The Office Action has acknowledged the novelty and inventiveness of the claims and withdrew its previous rejections under 35 U.S.C. §§ 102 and 103. Accordingly, the claims include elements that are not well- understood, routine and conventional..
However, it is respectfully submitted that Applicant’s assertion is not persuasive. With respect to the arguments related to the novelty of the improvement: "Even assuming [the claimed invention is novel], it does not avoid the problem of abstractness." Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1263 (Fed. Cir. 2016). That is because the inventive concept must be significantly more than the abstract idea itself. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) ("[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”)
Relevant Prior Art
Huang teaches a joint geometric-neural networks approach for comparing, deforming, and generating 3D protein structures as 3D open curves, using Square Root Velocity Function (SRVF) representation and leverage its suitable geometric properties along with Deep Residual Networks (ResNets) for a joint registration and comparison [Huang et al. (2021: G-vae, a geometric convolutional VAE for protein structure generation. arXiv preprint arXiv:2106.11920; PTO 892 document, herein Huang], as evidenced by Guo teaches novel deep generative models with a graph variational autoencoder framework to model the structural complexity of protein molecules as physically realistic secondary structures (alpha helices, beta sheets and coils) [Guo X et al. (2021: Generating tertiary protein structures via interpretable graph variational autoencoders. Bioinformatics Advances, 1(1); PTO 892 cited, herein Guo].
Madani teaches in protein generation language model using target protein properties tokens prepended to amino acid sequences [Madani et al. US20210249105A1: Systems and methods for language modeling of protein engineering; PTO 892 cited, herein Madani).
Laniado teaches node/edge approach to amino acid/backbone geometries [Laniado et al. (US20230040576A1: Systems and methods for artificial intelligence-based prediction of amino acid sequences at a binding interface; PTO 892 cited, herein Laniado).
Luo teaches a transformer-based NN generative model for antibody sequence and structure design with a given backbone structure optimized for binding affinity [Luo S. et al. (2022). Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures, Advances in Neural Information Processing Systems35 (NeurIPS 2022),14 pages; PTO 892 cited, herein Luo].
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. 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.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting following form via EFS-Web or Central Fax (571-273-8300): PTO/SB/439. Applicant is encouraged to do so as early in prosecution as possible, so as to facilitate communication during examination.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
Papers related to this application may be submitted to Technical Center 1600 by facsimile transmission. Papers should be faxed to Technical Center 1600 via the PTO Fax Center. The faxing of such papers must conform to the notices published in the Official Gazette, 1096 OG 30 (November 15, 1988), 1156 OG 61 (November 16, 1993), and 1157 OG 94 (December 28, 1993) (See 37 CFR § 1.6(d)). The Central Fax Center Number is (571) 273-8300.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vy Rossi, whose telephone number is (703) 756-4649. The examiner can normally be reached on Monday-Friday from 8:30AM to 5:30PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached on (571) 272-2249. Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to (571) 272-0547.
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/VR/
Examiner
Art Unit 1685
/MARY K ZEMAN/Primary Examiner, Art Unit 1686