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 1-61 were canceled.
Claims 76 and 77 are canceled.
Claims 62-81 are pending.
Claims 65, 68 and 69 are withdrawn.
Claims 62-64, 66-67, 70-75, and 78-81 are examined on the merit.
Priority
The instant application is the National Stage entry of PCT/US2019/068084, International Filing Date: 12/20/2019, which claims priority to US Provisional Application 62/783,914, filed 12/21/2018. As such, the effective filing date assigned to each of claims 62-64, 66-67, and 70-81 is 12/21/2018.
In this action, all claims are examined as though they had an effective filing date of 12/21/2018. 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).
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 06/15/2026.
The 35 U.S.C. 112(b) rejections to claims 66 and 67 in the office action filed 01/15/2026 is withdrawn in view of a clarifications received in 06/15/2026 Remarks (pg. 10-11) and changing the dependency of claims 66 and 67 to claims 65 and 66, respectively.
The 35 U.S.C. 101 rejection to claims 62-64, 66-67, and 70-81 in the office action filed 01/15/2026 is withdrawn in view of the analysis Step 2A, 2nd prong, 1st consideration relating to an improvement in the functioning of a computer or an improvement to other technology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), the improvement to other technology applied to the field of HLA class II-specific epitope prediction, in this instance comprising using mono-allelic data acquired via an affinity tag to to train a model, which reduces false positives during peptide analysis. In this regard, Applicant's 06/15/2026 remarks at pp. 13 support withdrawal of the rejection.
The 35 U.S.C. 102(a)(1) rejections to claims 62-64, 72-75, and 78-81 in the office action filed 01/15/2026 is withdrawn in view of amendments received 06/15/2026 specifically by amending claim 62 to recites “wherein the training cells express a protein encoded by a class II HLA allele of the human subject, wherein the protein encoded by the class II HLA allele comprises an affinity tag and wherein the training data is mono-allelic data acquired via the affinity tag”. A new round of art rejections is applied.
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 Interpretation
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“An input module configured to receive amino acid sequence information” in claims 62 and 63.
“A processing module operably linked to the input module” in claims 62 and 64.
“An output module configured to display the plurality of presentation predictions”, in claim 64.
Claims 62, 63, and 64 recite means (or an equivalent, nonce term, here "module") and function and/or result (here "input" and “output”), but the recitation does not invoke 112/f because it is interpreted as a well-known process, e.g. the process of "inputting amino acid sequences" MPEP 2181.I.A, 3rd para. pertains with analogy to structures having "sufficiently definite meaning," such as "filters" and "brakes."
Claims 62 and 64 recite means (or an equivalent, nonce term, here "module") and function and/or result (here "processing”), but the recitation does not invoke 112/f because it is interpreted as reciting sufficient structure (in this instance in the form of prediction model and subsequent algorithmic steps) not to invoke. MPEP 2181.I.C pertains.
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 62-64, 72-75, and 79-81 are rejected under 35 U.S.C. 103 as being unpatentable over Boucher (US20210113673A1; as previously cited in the form 892 dated 01/15/2026) in view of Keskin (US20210382068A1 as cited in the attached form 892).
Regarding claim 62, Boucher discloses method for generating an output for constructing a personalized cancer vaccine by identifying one or more neoantigens from one or more tumor cells of a subject that are likely to be presented on a surface of the tumor cells using a computer processor (claim 1); reading on limitations of a system for selecting one or more peptide sequences for preparing a pharmaceutical composition, the system comprising a computer processor.
Boucher discloses that the computer is adapted to execute computer program modules for providing functionality and that the term “module” refers to computer program logic used to provide the specified functionality. Boucher further discloses that a module can be implemented in hardware, firmware, and/or software and are stored on the storage device, loaded into the memory, and executed by the processor. Boucher further discloses an input interface linked to the processor [0547-0548].
Boucher further discloses obtaining at least one of exome, transcriptome, or whole genome nucleotide sequencing data from the tumor cells and normal cells of the subject, wherein the nucleotide sequencing data is used to obtain data representing peptide sequences of each of a set of neoantigens (claim 1); reading on limitations of (a) an input module configured to receive amino acid sequence information of a set of candidate peptide sequences expressed by cells of a human subject, wherein each candidate peptide sequence of the plurality of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a human subject, or a pathogen or a virus in the human subject.
Boucher further discloses inputting the numerical vectors, using a computer processor, into a deep learning presentation model to generate a set of presentation likelihoods for the set of neoantigens, each presentation likelihood in the set representing the likelihood that a corresponding neoantigen is presented by one or more class II MHC alleles on the surface of the tumor cells of the subject (claim 1); reading on limitations of (b) a processing module operably linked to the input module, the processing module comprising an executable code comprising a trained machine learning class II HLA- peptide presentation prediction model, wherein the trained machine learning class II HLA-peptide presentation prediction model is configured to generate output peptide sequences with a plurality of presentation predictions, wherein each presentation prediction of the plurality of presentation predictions is indicative of a presentation likelihood that a peptide sequence of the set of candidate peptide sequences is presented by one or more proteins encoded by a class II HLA allele of a cell of the human subject.
Boucher further discloses the deep learning presentation model comprising: a plurality of parameters identified at least based on a training data set comprising: labels obtained by mass spectrometry measuring presence of peptides bound to at least one class II MHC allele identified as present in at least one of a plurality of samples; training peptide sequences encoded as numerical vectors including information regarding a plurality of amino acids that make up the peptide sequence and a set of positions of the amino acids in the peptide sequence; and at least one HLA allele associated with the training peptide sequences; and a function representing a relation between the numerical vector received as an input and the presentation likelihood generated as output based on the numerical vector and the parameters; reading on limitations of wherein the trained machine learning class II HLA-peptide presentation prediction model comprises;(i) a plurality of parameters identified at least based on training data comprising: (1) sequences of training peptides, (2) an identity of a protein encoded by an HLA class II allele associated with the training peptide sequences, and (3) an observation by mass spectrometry that one or more of the training peptides was presented by the protein encoded by the HLA class II allele in training cells; and (ii) a function representing a relation between the amino acid sequence information received as input and the presentation likelihood generated as an output based on the amino acid sequence information and the plurality of parameters.
Boucher further discloses selecting a subset of the set of neoantigens based on the set of presentation likelihoods to generate a set of selected neoantigens; and generating the output for constructing the personalized cancer vaccine based on the set of selected neoantigens (claim 1); reading on limitations of wherein each peptide sequence of a subset of the output peptide sequences is for preparing a therapeutic composition for the human subject based on the presentation likelihood generated as the output of the peptide sequence being in a complex with the one or more proteins encoded by a class II HLA allele of a cell of the human subject.
Boucher further compares performance results for example presentation models trained and tested using datasets. Boucher showed that for each set of model features of the example presentation models, FIG. 13C depicts a PPV value at 10% recall and that the presentation models achieved a PPV value at 10% recall varying from 14% up to 29% (Fig. 13C) [0495]; reading on limitations of the trained machine learning class II HLA-peptide presentation prediction model has a positive predictive value (PPV) of at least 0.2 according to a presentation PPV determination method.
Boucher further discloses that the trained statistic regression or nonlinear deep learning models that jointly model peptide-allele mappings as well as the per-allele motifs for peptide of multiple lengths, sharing statistical strength across peptides of different lengths [0008]. Boucher further discloses allele-specific methods for detecting alleles [0188].
Further regarding claim 62, Boucher does not teach that the protein encoded by the class II HLA allele is mono-allelic data acquired via the affinity tag. This limitation is taught by Keskin.
Keskin teaches predicting HLA binding using allele-specific datasets to train machine-learning models (abstract). Keskin further teaches that the HLA alleles are HLA class II alleles [0087-0088]. Keskin further teaches that HLA-peptide complexes are isolated from cells using standard immunoprecipitation techniques known in the art; HLA class Il-peptide complexes can be isolated using HLA class II specific antibodies such as the M5/114.15.2 monoclonal antibody [0093]. Keskin further teaches attaching affinity tags for protein purification [0181].
Regarding claim 63, Boucher discloses that the nucleotide sequencing data is used to obtain data representing peptide sequences of each of a set of neoantigens identified by comparing the nucleotide sequencing data from the tumor cells and the nucleotide sequencing data from the normal cells, and wherein the peptide sequence of each neoantigen comprises at least one alteration that makes it distinct from the corresponding wild-type, peptide sequence identified from the normal cells of the subject (claim 1). Boucher further discloses that samples comprise human cell lines from patients (claim 12); reading on limitations of the input module is configured to receive an identity of one or more proteins encoded by a class II HLA allele of a cell of the human subject.
Regarding claim 64, Boucher discloses the computer system includes a processor and a display coupled to the graphic adaptor to display images and other information (for example, presentation information) [0546-0547] and that the presentation identification system includes presentation information to be displayed (FIG. 1D, 2A., and 14) [0321]; reading on limitations of the processing module is linked to an output module configured to display the plurality of presentation predictions.
Regarding claim 72, Boucher discloses that the methods are for generating an output for constructing a personalized cancer vaccine by identifying one or more neoantigens from one or more tumor cells of a subject that are likely to be presented on a surface of the tumor cells [0094]; reading on limitations of each peptide sequence of the set of candidate peptide sequences is associated with a cancer.
Regarding claim 73, Boucher discloses the steps of obtaining at least one of exome, transcriptome, or whole genome nucleotide sequencing data from the tumor cells and normal cells of the subject, wherein the nucleotide sequencing data is used to obtain data representing peptide sequences of each of a set of neoantigens identified by comparing the nucleotide sequencing data from the tumor cells and the nucleotide sequencing data from the normal cells, and wherein the peptide sequence of each neoantigen comprises at least one alteration that makes it distinct from the corresponding wild-type, peptide sequence identified from the normal cells of the subject [0094]; reading on limitations of each peptide sequence of the set of candidate peptide sequences (i) comprises a mutation,(ii) is expressed in a cancer cell of the subject, and(iii) is not encoded by a genome of a non-cancer cell of the human subject.
Regarding claim 74, Boucher discloses receiving mass spectrometry data comprising data associated with a plurality of isolated peptides eluted from major histocompatibility complex (MHC) [0175] and that the neoantigen nucleotide sequence for MHC Class II peptides has a length 6-30, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 amino acids [0204]; reading on limitations of each sequence of the one or more of the training peptides sequences observed by mass spectrometry to be presented by the protein encoded by the HLA class II in training cells has a length of at least 15 amino acids.
Regarding claim 75, Boucher discloses that the encoding module encodes MHC class II alleles, for example, DR allele type associated with the peptide sequence [0379] [0501-0507]; reading on limitations of the training cells comprise training cells expressing a single MHC class II complex or a protein encoded by a single allelic variant of a class II HLA locus selected from the group consisting of DR, DP, and DQ, wherein the single MHC class II complex or a protein encoded by the single allelic variant of a class II HLA locus is expressed by a cell of the subject.
Regarding claim 78, Boucher discloses that 19 samples of the 39 total samples contained the HLA class II molecule allele HLA-DRB4*01:03 [0507]; reading on limitations of the protein encoded by a class II HLA allele is selected from the group consisting of: HLA-DPB1*01:01/HILA- DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA- DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA- DPA1*01:03, HLA-DPB1*06:01/HILA-DPA1*01:03, HLA-DRB1*01:01, HLA- DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA- DRB 1*04:02, HLA-DRB 1*04:03, HLA-DRB 1*04:04, HLA-DRB 1*04:05, HLA- DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA- DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA- DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA- DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA- DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA- DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA- DRB4*01:01, HLA-DRB5*01:01, HLA-DRB1*01:01, HLA-DRB1*01:02, HLA- DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, HLA- -5-DRB1*04:05, HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA- DRB1*08:03, HLA-DRB1*09:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA- DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA- DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02, HLA- DRB1*15:03, HLA-DRB1*16:02, HLA-DRB3*01:01, HLA-DRB3*02:01, HLA- DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB4*01:03, HLA- DRB5*01:01; HLA-DPB1*01:01, HLA-DPB1*02:01, HLA-DPB1*02:02, HLA- DPB1*03:01, HLA-DPB1*04:01, HLA-DPB1*04:02, HLA-DPB1*05:01, HLA- DPB1*06:01, HLA-DPB1*11:01, HLA-DPB1*13:01, HLA-DPB1*17:01, HLA- DQA1 *01:01/HLA-DQB1 *05:01, HLA-DQA1 *01:02/HLA-DQB1 *06:02, HLA- DQA1 *01:02/HLA-DQB1 *06:04, HLA-DQA1 *01:03/HLA-DQB1 *06:03, HLA- DQA1 *02:01/HLA-DQB1 *02:02, HLA-DQA1 *02:01/HLA-DQB1 *03:03, HLA- DQA1 *03:01/HLA-DQB1 *03:02, HLA-DQA1 *03:03/HLA-DQB1 *03:01, HLA- DQA1*05:01/HLA-DQB1*02:01, HLA-DQA1*05:05/HLA-DQB1*03:01, and any combination thereof.
Regarding claim 79, Boucher discloses that neoantigenic peptide or polypeptide can have an IC50 of at least less than 5000 nM, at least less than 1000 nM, at least less than 500 nM, at least less than 250 nM, at least less than 200 nM, at least less than 150 nM, at least less than 100 nM, at least less than 50 nM or less [0211]; reading on limitations of each peptide sequence of the subset of the output peptide sequences binds to a protein encoded by a class II HLA allele of a cell of the human subject with an IC50 of 500 nM or less, or a predicted IC50 of 500 nM or less.
Regarding claim 80, Boucher discloses that neoantigens can include nucleotides or polypeptides and that neoantigens useful in vaccines can therefore include nucleotide sequences or polypeptide sequences. Boucher further discloses that isolated peptides comprise tumor specific mutations identified by the methods disclosed herein, peptides that comprise known tumor specific mutations, and mutant polypeptides or fragments thereof identified by methods disclosed herein [0202-0203] Boucher further discloses compositions comprising at least two or more neoantigenic peptides. In some embodiments the composition contains at least two distinct peptides. At least two distinct peptides can be derived from the same polypeptide [0213]; reading on limitations of each peptide sequence of the subset of the output peptide sequences is for preparing a therapeutic composition for the human subject that comprises one or more polypeptides comprising at least two peptide sequence of the subset of the output peptide sequences or one or more polynucleotides encoding at least two of peptide sequence of the subset of the output peptide sequences.
Regarding claim 81, Boucher discloses obtaining at least one of exome, transcriptome, or whole genome nucleotide sequencing data from the tumor cells of human subject (claim 1); reading on limitations of wherein each candidate peptide sequence of the plurality of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a human subject with cancer.
Rationale for combining Boucher and Keskin:
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Boucher and Keskin, the examiner concludes that the combination of Boucher and Keskin represents using of known technique to improve similar methods. Both Boucher and Keskin are directed to generate prediction models of peptide to MHC-II binding using machine learning. Boucher disclosed training a machine learning model to predict HLA class II-specific epitopes and that the model maps per-allele data (see [0008] and [0188]). In the same field of research, Keskin provided that the protein encoded by the class II HLA allele is mono-allelic data acquired via the affinity tag. Combining the HLA class II epitope prediction of Boucher with the known technique of attaching affinity tag to mono-allelic data of Keskin would have improved prediction of HLA-peptide binding. One ordinary skilled in the art before the effective filing data of the claimed invention would have been capable of applying the known technique of Keskin to the known base method of Boucher and the results would have been predictable to one ordinary skilled in the art. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 66-67 and 70-71 are rejected under 35 U.S.C. 103 as being unpatentable over Boucher in view of Keskin, as applied to claims 62-64, 72-75, and 78-81 above, and further in view of Barra (Footprints of antigen processing boost MHC class II natural ligand predictions, Genome Medicine, published: 16 November 2018, Volume 10, article number 84, (2018); as previously cited in the form 892 dated 01/15/2026).
Claims 66-67, 70-71 and 76, depend on claim 62. Limitations of claim 62 have been taught in the above rejections.
Regarding claim 66, Boucher discloses the data management module that identifies peptide sequences that are not presented by MHC alleles to generate the training data (generates decoys/negatives) and that the data management module identifies source proteins from which presented peptide sequences originated from (hit), and identifies a series of peptide sequences in the source protein that were not presented on MHC alleles of the tissue sample cells (decoy). [0373]. Boucher further discloses artificially generating large amounts of peptides with random sequences of amino acids and identify the generated sequences as peptides not presented on MHC alleles (decoys) [0374] [0380] (FIG. 4). Boucher further discloses that the prevalence of presented peptides in the test set was approximately 1/2400 [0499]. As stated above, claim 66 is indefinite (claim 62, does not recite any hit or decoy limitations); reading on limitations of i) the at least one hit peptide sequence comprises at least 10 hit peptide sequences, and(ii) the at least 499 decoy peptide sequences comprise at least 4990 decoy peptide sequences.
Further regarding claim 66, Barra discloses a prediction model of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets (Abstract). Barra further teaches that peptide binding to MHC II is the most selective step in antigen presentation to CD4+ T cells and highlighted its role for the development of cancer immunotherapies (pg. 12, col. 1, section: Discussion, para. 1). Barra further discloses evaluating the model on a test set where they assessed PPV where data is highly unbalanced (pg. 2, col. 2, para. 2). Barra further discloses using test sets with thousands of decoys to distinguish true ligands from the background proteome (see “Random” column in Table 1. For example, 38,115 random negative sequences/decoys). Barra further discloses evaluating model performance at PPV thresholds that required distinguishing a handful of hits from a vast background of decoys. Boucher further discloses that integrating processing rules boosted the PPV to at least 0.2 (often higher, for example, 0.7) (Tables 1-3, page. 3-7). Boucher further discloses that PPV is calculated by sorting all predictions and estimating the fraction of true positives with the top N predictions, where N is the number of positives/hits in the benchmark data set. Barra further discloses that PPV represents a good metric to benchmark on highly unbalanced data sets like MS-derived elution data, where we have approximately ten times more negatives than positives (pg. 4, col. 1, para. 4).
Regarding claim 67, Boucher discloses that the encoding module represents residue-level annotations of the source protein for peptide pi by including an indicator variable, that is equal to 1 if peptide pi overlaps with a helix motif and 0 otherwise, or that is equal to 1 if peptide pi is completely contained with within a helix motif in the allele-noninteracting variables wi [0406].
Barra further discloses that all data with a 9mer overlap between training and evaluation sets (FIG. 7, pg. 7, col. 3, first para.). As stated above, claim 67 is indefinite (claim 62, does not recite any hit or decoy limitations); reading on limitations of any nine contiguous amino acid subsequences of any of the at least one hit peptides does not overlap with any nine contiguous amino acid sub-sequences of the at least 4990 decoy peptide sequences.
Regarding claims 70 and 71, Boucher discloses that in FIG. 13C, for each set of model features of the example presentation models, a PPV value at 10% recall that was identified when the features in the set of model features were classified as allele interacting features is shown on the left side, and a PPV value at 10% recall that was identified when the features in the set of model features were classified as allele non-interacting features is shown on the right side. Boucher further notes that the feature of peptide sequence was always classified as an allele interacting feature for the purposes of FIG. 13C. Boucher further discloses achieving a PPV value at 10% recall varying from 14% up to 29%, which are significantly (approximately 500-fold) higher than PPV for a random prediction [0495]. Boucher further discloses that their models are capable of achieving significantly more accurate presentation predictions than the current best-in-class prior art model, the NetMHCII 2.3 model [0541].
Barra discloses that high accuracy prediction models for peptide MHC II interaction can be constructed from the MS-derived MHC II eluted ligand data, that the accuracy of these models can be improved by training models integrating information from both binding affinity and eluted ligand data sets, and that these improved models can be used to identify both eluted ligands and T cell epitopes in independent data sets at an unprecedented level of accuracy (pg. 12, col. 2, para. 1).
Rationale for combining Boucher, Keskin, and Barra:
Applying the KSR standard to Boucher, Keskin, and Barra, the examiner concludes that this combination represents a work that is available in one field of endeavor, design incentives and other market forces can prompt variations of it. Boucher, Keskin, and Barra are directed to generate prediction models of peptide to MHC-II binding using machine learning. Boucher disclosed a 1/2400 hit to decoy ratio with best performing presentation model achieving a %29 PPV and up to %0.3 PPV at %10 recall. In the same field of research, Barra disclosed that high accuracy prediction models for peptide MHC II interaction can be constructed from the MS-derived MHC II eluted ligand data, that the accuracy of these models can be improved by training models integrating information from both binding affinity and eluted ligand data sets. Combining the tumor-specific rules of Boucher (for example, tumor microenvironment/antigen-presenting cells (APCs)) with processing footprints of Barra (for example, protease cleavage signatures) would have allowed a model to filter out "false positives" that bind well but are never processed, maintaining high Positive Predictive Value (PPV) even as the recall rate increases to capture more potential targets. One ordinary skilled in the art before the effective filing data of the claimed invention would have had incentives to vary Boucher’s method to incorporate specific signatures of Barra in a predictable manner to result in the claimed invention. This combination would have been expected to have provided a more stability at higher recall rate and would have improved accuracy for a more accurate MHC-II peptide prediction. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Response to Arguments regarding the 103 rejections
Applicant's 6/15/2026 arguments have been fully considered but are not yet persuasive. The 6/15/2026 amendments to claim 62 necessitated a new 103 rejection, in which the new combination of Boucher and now Keskin teaches all limitations of claim 62, including the use of mono-allelic training data.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 62-64, 66-67, 70-75, and 78-81 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-5, 7-8, 9-10, and 16-17 of U.S. Patent No. 11183272 in view of Boucher (US20210113673A1; as previously cited in the 892 form dated 01/15/2026) in view of Keskin (US20210382068A1; as cited in the attached 892 form dated 01/15/2026), as applied to claims 62-64, 72-75, and 78-81 above, and further in view of Barra et al. (Footprints of antigen processing boost MHC class II natural ligand predictions, Genome Medicine, Published: 16 November 2018, Volume 10, article number 84, (2018)). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Instant Application
Patent # 11183272
Claim 62: (a) an input module configured to receive amino acid sequence information of a set of candidate peptide sequences expressed by cells of a human subject, wherein each candidate peptide sequence of the plurality of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a human subject, or a pathogen or a virus in the human subject;
(b) a processing module operably linked to the input module, the processing module comprising an executable code comprising a trained machine learning class II HLA- peptide presentation prediction model, wherein the trained machine learning class II HLA-peptide presentation prediction model is configured to generate output peptide sequences with a plurality of presentation predictions, wherein each presentation prediction of the plurality of presentation predictions is indicative of a presentation likelihood that a peptide sequence of the set of candidate peptide sequences is presented by one or more proteins encoded by a class II HLA allele of a cell of the human subject, and wherein the trained machine learning class II HLA-peptide presentation prediction model comprises;(i) a plurality of parameters identified at least based on training data comprising:(1) sequences of training peptides,(2) an identity of a protein encoded by an HLA class II allele associated with the training peptide sequences, and(3) an observation by mass spectrometry that one or more of the training peptides was presented by the protein encoded by the HLA class II allele in training cells; and(ii) a function representing a relation between the amino acid sequence information received as input and the presentation likelihood generated as an output based on the amino acid sequence information and the plurality of parameters; wherein the training cells express a protein encoded by a class II HLA allele of the human subject, wherein the protein encoded by the class II HLA allele comprises an affinity tag and wherein the training data is mono-allelic data acquired via the affinity tag, wherein each peptide sequence of a subset of the output peptide sequences is for preparing a therapeutic composition for the human subject based on the presentation likelihood generated as the output of the peptide sequence being in a complex with the one or more proteins encoded by a class II HLA allele of a cell of the human subject, and wherein the trained machine learning class II HLA-peptide presentation prediction model has a positive predictive value (PPV) of at least 0.2 according to a presentation PPV determination method.
Claim 1: (a) inputting amino acid information of a set of candidate peptide sequences using a computer processor, into a trained machine learning class II HLA-peptide presentation prediction model to generate a plurality of presentation predictions, wherein each candidate peptide sequence of the set of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a subject, or a pathogen or a virus in the subject; wherein the plurality of presentation predictions comprises an HLA presentation prediction for each candidate peptide sequence of the set of candidate peptide sequences, wherein each presentation prediction of the plurality of presentation predictions is indicative of a presentation likelihood that a peptide sequence of the set of candidate peptide sequences is presented by one or more proteins encoded by a class II HLA allele of a cell of the subject, and wherein the trained machine learning class II HLA-peptide presentation prediction model comprises (i) a plurality of parameters identified at least based on training data comprising:(1) sequences of training peptides, (2) an identity of a protein encoded by an HLA class II allele associated with the training peptide sequences, and (3) an observation by mass spectrometry that one or more of the training peptides was presented by the protein encoded by the HLA class II allele in training cells; and (ii) a function representing a relation between the amino acid sequence information received as input and the presentation likelihood generated as an output based on the amino acid sequence information and the plurality of parameters[[:]];(b) selecting, based at least on the plurality of presentation predictions, a subset of peptide sequences of the set of candidate peptide sequences to generate a set of selected peptide sequences, wherein the selected subset of peptide sequences (i) are encoded by a genome, transcriptome, or exome of a subject, or a pathogen or a virus in the subject, and (ii) are predicted to be presented by the protein encoded by the HLA class II allele of a cell of the subject; and (c) administering to the human subject a pharmaceutical composition comprising: wherein the trained machine learning HLA-peptide presentation prediction model has a positive predictive value (PPV) of at least 0.2 at a recall rate of 10% according to a presentation PPV determination method.
Claim 4: the training data comprises training data obtained by deconvolution.
Claim 5: wherein the training cells express a protein encoded by a class II HLA allele of a cell of the subject, wherein the protein encoded by a class II HLA allele comprises an affinity tag.
Claim 63: wherein the input module is configured to receive an identity of one or more proteins encoded by a class II HLA allele of a cell of the human subject.
Claim 64: wherein the processing module is linked to an output module configured to display the plurality of presentation predictions.
Claim 66: wherein:(i) the at least one hit peptide sequence comprises at least 10 hit peptide sequences, and(ii) the at least 499 decoy peptide sequences comprise at least 4990 decoy peptide sequences.
Claim 7: wherein: (i) the at least one hit peptide sequence comprises at least 10 hit peptide sequences, and(ii) the at least 499 decoy peptide sequences comprise at least 4990 decoy peptide sequences
Claim 67: wherein any nine contiguous amino acid subsequences of any of the at least one hit peptides does not overlap with any nine contiguous amino acid sub-sequences of the at least 4990 decoy peptide sequences.
Claim 8: wherein any nine contiguous amino acid subsequences of any of the at least one hit peptides does not overlap with any nine contiguous amino acid sub-sequences of the at least 4990 decoy peptide sequences.
Claim 70: wherein the trained machine learning class II HLA-peptide presentation prediction model has a PPV of at least 0.3 according to a presentation PPV determination method.
Claim 71: wherein the trained machine learning class II HLA-peptide presentation prediction model has a PPV of at least 0.3 at a recall rate of 20% according to a presentation PPV determination method.
Claim 72: wherein each peptide sequence of the set of candidate peptide sequences is associated with a cancer.
Claim 9: wherein each peptide sequence of the set of candidate peptide sequences is associated with a cancer.
Claim 73: wherein each peptide sequence of the set of candidate peptide sequences (i) comprises a mutation, (ii) is expressed in a cancer cell of the subject, and(iii) is not encoded by a genome of a non-cancer cell of the human subject.
Claim 10: wherein each peptide sequence of the set of candidate peptide sequences (i) comprises a mutation, (ii) is expressed in a cancer cell of the subject, and (iii) is not encoded by a genome of a non-cancer cell of the subject.
Claim 74: wherein each sequence of the one or more of the training peptides sequences observed by mass spectrometry to be presented by the protein encoded by the HLA class II in training cells has a length of at least 15 amino acids.
Claim 2: wherein each sequence of the sequences of training peptides observed by mass spectrometry to be presented by the protein encoded by the HLA class II in training cells has a length of at least 15 amino acids.
Claim 75: wherein the training cells comprise training cells expressing a single MHC class II complex or a protein encoded by a single allelic variant of a class II HLA locus selected from the group consisting of DR, DP, and DQ, wherein the single MHC class II complex or a protein encoded by the single allelic variant of a class II HLA locus is expressed by a cell of the subject.
Claim 4: wherein: (i) the training cells comprise training cells expressing a single MHC class II complex or a protein encoded by a single allelic variant of a class II HLA locus selected from the group consisting of DR, DP, and DQ, wherein the single MHC class II complex or a protein encoded by the single allelic variant of a class II HLA locus is expressed by a cell of the subject.
Claim 78: wherein the protein encoded by a class II HLA allele is selected from the group consisting of: HLA-DPB1*01:01/HILA- DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA- DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA- DPA1*01:03, HLA-DPB1*06:01/HILA-DPA1*01:03, HLA-DRB1*01:01, HLA- DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA- DRB 1*04:02, HLA-DRB 1*04:03, HLA-DRB 1*04:04, HLA-DRB 1*04:05, HLA- DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA- DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA- DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA- DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA- DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA- DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA- DRB4*01:01, HLA-DRB5*01:01, HLA-DRB1*01:01, HLA-DRB1*01:02, HLA- DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, HLA- -5-DRB1*04:05, HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA- DRB1*08:03, HLA-DRB1*09:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA- DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA- DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02, HLA- DRB1*15:03, HLA-DRB1*16:02, HLA-DRB3*01:01, HLA-DRB3*02:01, HLA- DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB4*01:03, HLA- DRB5*01:01; HLA-DPB1*01:01, HLA-DPB1*02:01, HLA-DPB1*02:02, HLA- DPB1*03:01, HLA-DPB1*04:01, HLA-DPB1*04:02, HLA-DPB1*05:01, HLA- DPB1*06:01, HLA-DPB1*11:01, HLA-DPB1*13:01, HLA-DPB1*17:01, HLA- DQA1 *01:01/HLA-DQB1 *05:01, HLA-DQA1 *01:02/HLA-DQB1 *06:02, HLA- DQA1 *01:02/HLA-DQB1 *06:04, HLA-DQA1 *01:03/HLA-DQB1 *06:03, HLA- DQA1 *02:01/HLA-DQB1 *02:02, HLA-DQA1 *02:01/HLA-DQB1 *03:03, HLA- DQA1 *03:01/HLA-DQB1 *03:02, HLA-DQA1 *03:03/HLA-DQB1 *03:01, HLA- DQA1*05:01/HLA-DQB1*02:01, HLA-DQA1*05:05/HLA-DQB1*03:01, and any combination thereof.
Claim 11: wherein the protein encoded by a class II HLA allele is selected from the group consisting of: HLA-DPB 1* 01:01/HLA-DPA 1* 01:03, HLA-DPB 1* 02:01/HLA- DPA1*01:03, HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03, HLA- DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03, HLA-DRB1*01:01, HLA- DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA- DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA- DRB1*08:01, HLA-DRiB1*08:02, HLA-DRiB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01, HLA- DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA- DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01, HLA- DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA- DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01, HLA-DRB1*01:01, HLA- DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:04, HLA- DRB1*04:05, HLA-DRiB1*07:01, HLA-DRiB1*08:01, HLA-DRB1*08:02, HLA-DRB1*08:03, HLA- DRB1*09:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA- DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA- 50401-735.301 Amendment 3.12 to Claims After Allowance (FINAL) 4828-0077-1838 v.1.doexDRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:02, HLA-DRB3*01:01, HLA-DRB3*02:01, HLA- DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB4*01:03, HLA-DRB5*01:01; HLA- DPB1*01:01, HLA-DPB1*02:01, HLA-DPB1*02:02, HLA-DPB1*03:01, HLA-DPB1*04:01, HLA- DPB1*04:02, HLA-DPB1*05:01, HLA-DPB1*06:01, HLA-DPB1*11:01, HLA-DPB1*13:01, HLA- DPB1*17:01, HLA-DQA1*01:01/HLA-DQB1*05:01, HLA-DQA1*01:02/HLA-DQB1*06:02, HLA- DQA1*01:02/HLA-DQB1*06:04, HLA-DQA1*01:03/HLA-DQB1*06:03, HLA-DQA1*02:01/HLA- DQB 1*02:02, HLA-DQA1*02:01/HLA-DQB 1*03:03, HLA-DQA1*03:01/HLA-DQB1*03:02, HLA- DQA1*03:03/HLA-DQB1*03:01, HLA-DQA1*05:01/HLA-DQB1*02:01, HLA-DQA1*05:05/HLA- DQB 1* 03:01, and any combination thereof.
Claim 79: wherein each peptide sequence of the subset of the output peptide sequences binds to a protein encoded by a class II HLA allele of a cell of the human subject with an IC50 of 500 nM or less, or a predicted IC50 of 500 nM or less.
Claim 16: wherein each of the peptide sequences of the set of selected peptide sequences binds to a protein encoded by a class II HLA allele of a cell of the subject with an IC50 of 500 nM or less, or a predicted IC50 of 500 nM or less.
Claim 80: wherein each peptide sequence of the subset of the output peptide sequences is for preparing a therapeutic composition for the human subject that comprises one or more polypeptides comprising at least two peptide sequence of the subset of the output peptide sequences or one or more polynucleotides encoding at least two of peptide sequence of the subset of the output peptide sequences.
Claims 1 and 17: wherein the pharmaceutical composition comprises one or more polypeptides comprising at least two of the selected peptide sequences or one or more polynucleotides encoding at least two of the selected peptide sequences. (i) a polypeptide with one or more of the selected subset of peptide sequences, (ii) a polynucleotide encoding the polypeptide of (i),
Claim 81: wherein each candidate peptide sequence of the plurality of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a human subject with cancer.
Claims 1 and 9: wherein each candidate peptide sequence of the set of candidate peptide sequences is encoded by a genome, transcriptome, or exome of a subject. wherein each peptide sequence of the set of candidate peptide sequences is associated with a cancer.
Reference Patent Application No. 11183272 does not teach the limitations of claim 1 regarding an input module and a processing module operably linked to the input module, limitations of claim 63 regarding the input module is configured to receive an identity of one or more proteins encoded by a class II HLA allele of a cell of the human subject, limitations of claim 64 regarding the processing module is linked to an output module configured to display the plurality of presentation predictions, limitations of claim 70 regarding the trained machine learning class II HLA-peptide presentation prediction model has a PPV of at least 0.3 according to a presentation PPV determination method, limitations of claim 71 regarding the trained machine learning class II HLA-peptide presentation prediction model has a PPV of at least 0.3 at a recall rate of 20% according to a presentation PPV determination method, wherein the training cells express a protein encoded by a class II HLA allele of the human subject, wherein the protein encoded by the class II HLA allele comprises an affinity tag and wherein the training data is mono-allelic data acquired via the affinity tag.
Boucher discloses that the computer is adapted to execute computer program modules for providing functionality and that the term “module” refers to computer program logic used to provide the specified functionality. Boucher further discloses that a module can be implemented in hardware, firmware, and/or software and are stored on the storage device, loaded into the memory, and executed by the processor. Boucher further discloses an input interface linked to the processor [0547-0548].
Boucher discloses that the nucleotide sequencing data is used to obtain data representing peptide sequences of each of a set of neoantigens identified by comparing the nucleotide sequencing data from the tumor cells and the nucleotide sequencing data from the normal cells, and wherein the peptide sequence of each neoantigen comprises at least one alteration that makes it distinct from the corresponding wild-type, peptide sequence identified from the normal cells of the subject (claim 1). Boucher further discloses that samples comprise human cell lines from patients (claim 12); reading on limitations of the input module is configured to receive an identity of one or more proteins encoded by a class II HLA allele of a cell of the human subject.
Boucher discloses the computer system includes a processor and a display coupled to the graphic adaptor to display images and other information (for example, presentation information) [0546-0547] and that the presentation identification system includes presentation information to be displayed (FIG. 1D, 2A., and 14) [0321]; reading on limitations of the processing module is linked to an output module configured to display the plurality of presentation predictions.
Boucher discloses that in FIG. 13C, for each set of model features of the example presentation models, a PPV value at 10% recall that was identified when the features in the set of model features were classified as allele interacting features is shown on the left side, and a PPV value at 10% recall that was identified when the features in the set of model features were classified as allele non-interacting features is shown on the right side. Boucher further notes that the feature of peptide sequence was always classified as an allele interacting feature for the purposes of FIG. 13C. Boucher further discloses achieving a PPV value at 10% recall varying from 14% up to 29%, which are significantly (approximately 500-fold) higher than PPV for a random prediction [0495]. Boucher further discloses that their models are capable of achieving significantly more accurate presentation predictions than the current best-in-class prior art model, the NetMHCII 2.3 model [0541].
Keskin teaches predicting HLA binding using allele-specific datasets to train machine-learning models (abstract). Keskin further teaches that the HLA alleles are HLA class II alleles [0087-0088]. Keskin further teaches that HLA-peptide complexes are isolated from cells using standard immunoprecipitation techniques known in the art; HLA class Il-peptide complexes can be isolated using HLA class II specific antibodies such as the M5/114.15.2 monoclonal antibody [0093]. Keskin further teaches attaching affinity tags for protein purification [0181].
Barra discloses that high accuracy prediction models for peptide MHC II interaction can be constructed from the MS-derived MHC II eluted ligand data, that the accuracy of these models can be improved by training models integrating information from both binding affinity and eluted ligand data sets, and that these improved models can be used to identify both eluted ligands and T cell epitopes in independent data sets at an unprecedented level of accuracy (pg. 12, col. 2, para. 1).
One would have had a reasonable expectation of success in combining these methods because they are drawn to the related field of prediction models for peptide MHC II interaction, and one ordinary skilled in the art could have design incentives and other market forces to prompt these variations and the design incentives or market forces could have prompted one ordinary skilled in the art to vary the prior art in a predictable manner to result in the claimed invention.
Response to Arguments regarding double patenting
Applicant's arguments filed 06/15/2026 have been fully considered but they are not yet persuasive. Applicant states that the amendments to the claims would not be obvious over combination of US 11183272, Boucher and Barra.
Claims 62-64, 66-67, and 70-81 would have been obvious over the combination of US 11183272, Boucher, Keskin, and Barra, as stated above. Therefore, the non-statutory double patenting rejection is maintained.
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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