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 .
Status of the Claims
The amended claim set received 02 July 2026 has been entered into the application.
Claims 1, 13, 21, and 22 have been amended.
Claims 7-8 are cancelled.
Claims 2-6 and 20 are previously cancelled.
Claim 13 is objected to.
Claim(s) 1, 9-19, and 21-22 are pending.
Priority
This application claims benefit of priority to GB1607521 filed on 29 April 2016.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
The rejection of claims 13 in the Office Action 02 January 2026 is withdrawn in view of the amendments filed 02 July 2026.
Claim 13 is objected to because of the following informalitie(s): “…measurement, or a comination thereof.” The claim should be amended to recite “…measurement, or a thereof.” Appropriate correction is required to address the grammatical correctness of the claim.
Claim Rejections - 35 USC § 112
35 USC § 112(b)
The rejection of claims 7-8 under 35 U.S.C § 112(b) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 02 January 2026 and modified in view of the amendments filed 02 July 2026.
The rejection of claims 7-8 under 35 U.S.C § 101 in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
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, 9-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Following the flowchart of the MPEP 2106
Step I: Process, Machine, Manufacture or Composition
Claims 1 and 9-19 are directed towards a method, so a process.
Claim 21 is directed towards an apparatus, so a machine.
Claim 22 is directed towards a method, so a process.
Step 2A Prong I: Identification of an Abstract Idea
Claim 1 is directed towards a method to train a machine learning model. Claim 21 is drawn to an apparatus that encompasses similar limitations as claim 1. Claim 22 is directed towards a method to train a statistical inference model. Because claims 1 and 21-22 encompass similar limitations, the rejection to claim 1 is applicable to claims 21-22.
Claims 1 and 21-22 recites:
Building a positive data set comprises entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MHC alleles.
This step can be performed in the human mind by organizing information which can be performed in the human or with a paper/pen and is therefore an abstract idea. This step encompasses generating a positive data set by taking existing information (i.e., peptide sequence data), manipulating the data using mathematical functions, and organizing this information into a new form (i.e., positive data set) which encompasses mathematical concepts which reads on abstract ideas. See MPEP 2106.04(a)(I)(A)(iv).
Building a negative data set comprising wherein the negative data set comprises entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes.
This step can be performed in the human mind by organizing information which can be performed in the human or with a paper/pen and is therefore an abstract idea. This step encompasses generating a positive data set by taking existing information (i.e., peptide sequence data), manipulating the data using mathematical functions, and organizing this information into a new form (i.e., negative data set) which encompasses mathematical concepts which reads on abstract ideas. See MPEP 2106.04(a)(I)(A)(iv).
identifying a multiplicity of pairings with each pairing establishing pair between one entry in the positive data set and one entry in the negative data sets that are (i) of equal length, (ii) derived from the same source protein or fragment thereof, and (iii) have similar binding affinities, with respect to an HLA/MHC molecule to which the peptide of the positive counterpart data set is restricted, and (iii).
This step can be performed in the human mind by observing and evaluating entries of (i)-(iii) to identify a multiplicity of parings between one entry of the positive and negative dataset and is therefore an abstract idea.
training the machine learning algorithm/statistical inference model on the multiplicity of pairings.
This step can be performed in the human mind by following instructions to train a machine learning algorithm/statistical inference model on the multiplicity of pairings and is therefore an abstract idea. Here, training the machine learning algorithm (claims 1 and 21) and the statistical inference model (claim 22) are generically recited and therefore read on mathematical/statistical concepts.
Wherein the amino acids at key HLA/MHC-binding anchor positions within the peptide sequences of the positive and negative data sets are removed as features for the machine learning algorithm
This step can be performed in the human mind by observing anchor positions of the positive and negative dataset to remove features from the machine learning algorithm/statistical inference model and is therefore an abstract idea.
following the training of the machine learning algorithm/statistical inference model on the multiplicity of pairings, using the trained machine learning algorithm/statistical inference model to interrogate input data comprising amino acid sequences of peptides and/or proteins, to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
This step can be performed in the human mind by organizing data (i.e., interrogate input data) to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation using either the machine learning algorithm (claims 1-21) or the statistical inference model (claim 22) after training the machine learning algorithm (claims 1-21) and/or the statistical inference model (claim 22) on the multiplicity of pairing and is therefore an abstract idea. This step encompasses taking existing information (i.e., amino acid data, positive and negative datasets of peptide sequence pairings and entries), manipulating the data via mathematical/statistical correlations (i.e., machine learning model/statistical inference model), and organizing the data into a new (i.e., identified peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation) which reads on abstract ideas/mathematical concepts. See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(I)(A)(iv), 2106.04(a)(2)(III)(C)(1-3) and 2106.05(f).
Claims 9-19 are further drawn to limitations that described the abstract ideas performed in claim 1 and are therefore abstract ideas.
Step 2A Prong Two: Consideration of Practical Application
Claims 1 and 21-22 do not recite any additional elements which integrate the recited judicial exception into a practical application. Here, in the instant case, the claim merely sets forth a method of data analysis to identify peptides positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation. As such practicing the claims merely results in the abstract determination (data analysis) of the identification of features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation. Such results only produce new information (i.e., associated peptide and/pr peptide fragments) and does not provide for a practical application in the real-world realm of physical things i.e., the claims do not utilize the results of the information generated by judicial exception to affect a type of change in the real-world realm or improve the function of a computer. Therefore, the claims do not utilize the built data sets, identified multiplicity of pairings, trained machine learning algorithm/statistical inference model and the abstract ideas to construct a practical application such as treating a subject, transformation of matter, or improving upon an existing technology.
Claims 1 and 21-22 recites “creating a trained algorithm to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation”. The limitations do not integrate the recited judicial exception into a practical application because “creating a trained algorithm” is broadly and generically recited and attempts to cover any solution for creating a trained machine learning algorithm to identify peptides with no restriction on how the creation of the machine learning algorithm is accomplished which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See MPEP 2106.05(f).
The recited additional element of using computer processes and components of claims 1 and 21-22 does not integrate the recited judicial exception because using computer elements is tangential to the claimed method.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed methods also recite “additional elements” that are not drawn to an abstract idea.
The recited additional element of using computer processes of claims 1 and 21-22 does add more than the recited judicial exception because using computer elements to analyze nucleic acid sequence data is merely tangential to the claimed methods. See MPEP 2106.05(a) and 2106.05(d)(II).
In conclusion and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments, filed 02 July 2026, have been fully considered and the rejection is maintained.
The Applicant provides a declaration regarding the instant claims’ use of the machine learning models under Ex Parte Desjardins [remarks, page 8]. The Applicant states the claims are patent eligible under Step 2A Prong II [remarks, page 8]. The Applicant points to the declaration paragraphs 21, 3, 5 and 6 for guidance [remarks, page 9]. The Applicant claims the claims "addresses a technical problem in machine-learning systems: dominant confounding signals can cause a model to learn unintended feature relationships and generalize poorly outside the conditions emphasized in the training data." [remarks, page 9]. The Applicant points to the amendments to claim 1 for guidance [remarks, page 10].
The Applicant states the Declaration confirms the technical impact of these claim requirements, explaining that "using a specific matched-pair training architecture" can reduce "confounding effects during training and change[] what the machine learning algorithm is actually learning." [remarks, page 10]. The Applicant points to declaration paragraphs 22, 8, and 10-11 for guidance [remarks, page 10].
The Applicant states claim 1 is not merely matter of organizing data. The Applicant states because binding signals can dominate training, a model trained on conventional positive and negative peptide data may learn binding correlations instead of the processing-related features of interest and claim 1 addresses this training problem [remarks, page 11].
The Applicant states claim 1 addresses the training problem by requiring entry-level positive/negative pairings in which paired entries are controlled for length, source protein, and HLA/MHC-binding affinity. The Applicant states the training process reduces false signal dependence and allows the model to learn processing-related features that would otherwise be obscured [remarks, page 11].
The Applicant states claim 1 recites a technical solution to the identified training problem. The Applicant points to the declaration paragraphs 14-18 for guidance [remarks, pages 11-12].
In response, and with respect to the Applicant’s assertion that the claims are drawn to a technical solution to the identified training problem by “addressing the technical problem in machine-learning systems: dominant confounding signals can cause a model to learn unintended feature relationships and generalize poorly outside the conditions emphasized in the training data", "using a specific matched-pair training architecture" can reduce "confounding effects during training”, “binding signals can dominate training”, and “training process reduces false signal dependence and allows the model to learn processing-related features that would otherwise be obscured”, the argument is not persuasive because the claims do not recite utilizing confounding signals, using match-pair training architecture to reduce confounding effects, binding signals, and training to reduce false signal dependence. Here, the Applicant/Declaration is arguing limitations that are not recited in the claims and that are not supported by specification. As such, claims 1, 21, and 22 are drawn to abstract ideas that are not integrated with any additional elements such that to recite a practical application/improvement to technology because the claims are drawn abstract data analysis using mathematical/statistical modeling (i.e., machine learning models/algorithms) and tangential computer elements to identify proteins (i.e., amino acids). The argument is not further persuasive because the claims are drawn to using tangential computer elements for identifying peptides positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation (i.e., abstract ideas performed in a computing environment) and are drawn to applying the judicial exception. See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(I)(A)(iv), 2106.04(a)(2)(III)(C)(1-3) and 2106.05(f).
The Applicant states the claims satisfy the requirement under Step 2A Prong II of the 101 analyses as the claims provide technological problem and a technological solution [remarks, page 12]. The Applicant states the Declaration establishes that claim 1 addresses a technical problem in machine-learning training, namely that dominant confounding signals can cause a machine-learning model to learn unintended feature relationships rather than the target signal. The Applicant states the Declaration further establishes that claim 1 solves this problem through the claimed matched-entry training method. The Applicant states claim 1 requires constructing a multiplicity of entry-level pairings between positive and negative training examples that control dominant confounding signals before the machine-learning algorithm is trained. The Applicant states the Declaration further confirms that this training method changes the statistical relationships presented during training, reduces false-signal dependence, improves feature learning, improves generalization, and improves predictive discrimination [remarks, page 12].
In response, the argument is not persuasive because both the Applicant and the authors of the declaration are arguing limitations that are not in the recited in the instant claim set and arguing limitations not supported by the specification. The claims do not recite “confounding signals”, “constructing pairings to control dominant cofounding pairings”, and “reduces false-signal dependence, improves generalization, and predictive discrimination”. Thus, the claims do not provide an improvement and are not eligible under Step 2A Prong II of the 101 analyses.
The Applicant states as explained above and supported by the Declaration, claim 1 addresses a technical machine-learning problem (dominant confounding signals can cause a model to learn unintended feature relationships) through a specific matched-entry training architecture that changes the training information presented to the model [remarks, page 13].
The Applicant states Desjardins held that independent claim 1 in that case was eligible because it integrated any abstract idea into a practical application.
The Applicant states Desjardins expressly applied that analysis to all of the pending dependent claims. Some of those dependent claims further limited the invention to classification of data (claim 17 in Desjardin, for example, reads "The method of claim 1, wherein the first and second machine learning tasks each comprise a classification task, and wherein the classification task is processing data to classify the data").
The Applicant states the classification of data is generally considered an abstract idea, Desjardin clearly stands for the proposition that the application of an eligible method for training a machine learning model to an otherwise abstract context does not change the eligibility result for that claim. Similarly, claim 1 here is not ineligible merely because the improved training architecture is used in the context of peptide data. The Applicant states the Declaration establishes the claim improves the way the machine-learning model is trained [remarks, page 13-14].
In response, it is noted the claims do not recite specific or specialized machine learning architecture but recite generic machine learning algorithms and statistical interference models for processing peptide pairing data to identify amino acids. Here, claims 1 and 21-22 merely recite building datasets, identifying pairings, training machine learning algorithm on pairing to create a trained algorithm, and identifying amino acids which does not require or recite a specific machine learning architecture but only requires using machine learning/statistical inference models. As such, and as noted in Step 2A Prong I of the 101 analysis above, the machine learning algorithm and statistical inference models of claims 1 and 21-22 are generically and broadly recited and read on the abstract ideas of organizing information and performing mathematical operations (i.e., machine learning algorithm, statistical inference model, training, creating trained models) which is insufficient to provide a practical application or an improvement to technology. Furthermore, regarding the claims of Desjardins providing a practical application, the claims of Desjardins integrated the abstract ideas (i.e., mathematical concepts) into a practical technological application that improved artificial intelligence technology itself, not mere nucleic acid data analysis (i.e., identifying proteins). Additionally, the claims of Desjardin are rooted in computer-related machine-learning elements which improved computer performance and system efficiency rather than just reciting abstract mathematical concepts. Here, the claims of Desjardins are drawn to an improved method that prevented "catastrophic forgetting"- allowing models to learn new tasks sequentially while retaining memory of past tasks which focuses on the machine learning models/architecture itself as compared to the instant claims utilizing a machine learning algorithm/statistical inference models to determine a biological solution (i.e., processing amino acid data for identifying amino acid/peptide sequences).
Claim Rejections - 35 USC § 103
The instant rejection is maintained for reason for record in the Office Action mailed 02 January 2026 and modified in view of the amendments filed 02 July 2026.
The rejection of claim(s) 1, 7-8, 11, 13-14, 16, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanda et al. (Clin Dev Immunol 2013:2013:263952. doi:10.1155/2013/263952. Epub 2013 Dec 30) in view of Tang et al. (Journal of Immunological Methods, 2015-07, Vol.422, p.22-27; Cited in the Office Action mailed 02 January 2025) in view of Fridman et al. (U.S Patent Pub: US 2006/025944; Patent Pub Date: 16 November 2006) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026 because claims 7-8 of the rejection were cancelled.
The rejection of claim(s) 9-10 under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 7-8, 11, 13-14, 16, and 21-22, and in further view of Bremel et al. (U.S Patent Pub US 2013/0330335; Cited in the Office Action mailed 02 January 2025) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
The rejection claim(s) 17 under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 7-8, 11, 13-14, 16, and 21-22, and in further view of Mei et al. (Biopolymers, 2005, Vol.80 (6), p.775-786; Cited in the Office Action mailed 02 January 2025) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
The rejection of claim(s) 18 under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 7-8, 11, 13-14, 16, and 21-22, and in further view of Zhiliang et al. (Science China. Chemistry, 2008-10, Vol.51 (10), p.946-957; Cited in the Office Action mailed 02 January 2025) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
The rejection of claim(s) 19 under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 7-8, 11, 13-14, 16, and 21-22, and in further view of Wen et al. (Gene, 2014-08, Vol.546 (1), p.25-34; Cited in the Office Action mailed 02 January 2025) in the Office Action mailed 02 January 2026 is withdrawn in view of the amendment received 02 July 2026.
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.
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.
Claim(s) 1, 11, 13-14, 16, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanda et al. (Clin Dev Immunol 2013:2013:263952. doi:10.1155/2013/263952. Epub 2013 Dec 30) in view of Tang et al. (Journal of Immunological Methods, 2015-07, Vol.422, p.22-27; Cited in the Office Action mailed 02 January 2025) in view of Fridman et al. (U.S Patent Pub: US 2006/025944; Patent Pub Date: 16 November 2006).
Claim 1 recites building a positive dataset comprising entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MHC allele.
Claim 1 recites building a negative dataset entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes.
Claim 1 recites identifying a multiplicity of pairings, with each pairing establishing a pair between one entry in the positive data set and one entry in the negative data set, wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are:
Claim 1 recites (i) of equal or similar length.
Claim 1 recites (ii) are derived from the same source protein or fragment.
Claim 1 recites (iii) similar binding affinities, with respect to an HLA/MHC molecule to which the peptide of the positive counterpart data set is restricted.
Claim 1 recites training the machine learning algorithm on the multiplicity of pairings, creating a trained algorithm to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation.
Claim 1 recites the amino acids at key HLA/MHC-binding anchor positions within the peptide sequences of the positive and negative data sets are removed as features for the machine learning algorithm.
Claim 1 recites wherein following the training the machine learning algorithm on the multiplicity of pairings, using the trained machine learning algorithm to interrogate input data comprising amino acid sequences of peptides and/or proteins, to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
Claim 21 recites an apparatus. Claim 22 recites a method for training a statistical inference model. Claims 21-22 encompass the same limitations as claim 1. Therefore, the rejection of claim 1 is applicable to claims 21-22.
Dhanda
Dhanda et al. (Dhanda) teach a data source and data processing of types data sets, positive and a negative dataset. Dhanda teaches the datasets were generated from publicly available immune epitope database (IEDB). Dhanda teaches that extracted experimentally validated MHC class II binding T-helper epitopes. Dhanda teaches peptides having length shorter than 8 residues and longer than 22 residues were removed. Dhanda teaches the data set processing resulted in unique 904 IL4 inducing and 742 noninducing MHC class II peptide sequences. Dhanda teaches these datasets are known as positive and negative sets. Dhanda teaches the dataset was created without any restrictions of host and source of epitopes [page 2 left col section 2 methodology 2.1.1]. Dhanda teaches the positive data set contains IL4 inducing peptides as positive input and IL4 noninducing peptides as negative inputs [page 2 left col methods second 2.3], as in claims 1 and 21-22 building a positive data set comprising entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MH C alleles and building a negative data set comprising entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes.
Dhanda teaches using amino acid lengths between 8 to 22 residues [page 2 left col, section 2 methodology 2.1.1]. Dhanda teaches using different c terminal and n terminal 9-15 amino acids (i.e., peptide(s)) in lengths in the positive and negative datasets [Dhanda, at page 12 Table 3S], as in claims 1 and 21-22 wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are: (i) of equal or similar length.
Dependent claims 11, 14, and 16
Dhanda teaches using amino acid residues from 8 to 22 residues in length [page 2, left col section 2.1.1], as in claim 11.
Dhanda teaches examining physiochemical properties (PCP) (i.e., hydrophilicity, hydrophobicity, charge, steric effect, side bulk, pI, hydropathy, and amphipath) of IL4+ and IL4- peptides [page 3 left col section 2.10], as in claim 14.
Dhanda teaches examining physiochemical properties (PCP) (i.e., hydrophilicity, hydrophobicity, charge, steric effect, side bulk, pI, hydropathy, and amphipath) of IL4+ and IL4- peptides [page 3 left col section 2.10]. Dhanda teaches (iii) average of the sum of PCP of selected residues of N- and C- terminals, after analysis of discriminating residue positions; for example, 1, 2, 3, 5, and 12 positions of N- terminal were selected for hydrophilicity.” [page 3 left col section 2.10], as in claim 16.
Dhanda does not teach
Dhanda does not teach claim 1 identifying a multiplicity of pairings, with each pairing establishing a pair between one entry in the positive data set and one entry in the negative data set. Dhanda does not teach Claim 1 wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are: (ii) are derived from the same source protein or fragment.
Dhanda does not teach claim 1 training the machine learning algorithm on the multiplicity of pairings. Dhanda does not teach claim 1 creating a trained algorithm to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation.
Dhanda does not teach claim 1 wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are: (ii) are derived from the same source. Dhanda does not teach claim 1 wherein following the training the machine learning algorithm on the multiplicity of pairings, using the trained machine learning algorithm to interrogate input data comprising amino acid sequences of peptides and/or proteins, to identify peptides or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
Dhanda does not teach claim 1 wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are: (iii) similar binding affinities with respect to an HLA/MHC molecule to which the peptide of the positive counterpart data set is restricted. Dhanda does not teach claim 1 the amino acids at key HLA/MHC-binding anchor positions within the peptide sequences of the positive and negative data sets are removed as features for the machine learning algorithm.
Dhanda does not teach claim 1 training the machine learning algorithm on the multiplicity of pairings.
Dhanda does not teach claim 1 wherein following the training the machine learning algorithm on the multiplicity of pairings, using the trained machine learning algorithm to interrogate input data comprising amino acid sequences of peptides and/or proteins, to identify peptides or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
Dhanda does not claim 13.
Tang
Tang et al. (Tang) teach one positive dataset data is paired with the five negative sub-datasets with each pair trained with one of the following features: Hidden Markov Model, binary encoding, BLOSUM62 feature, position-specific amino acid composition, and position-specific dipeptide composition [page 23 right col second para]. Tang also teaches using pair of equal or similar length using 8-mers, 9-mers, 10-mers, and 11-mers [page 24 table 2]. Tang teaches using the same source from specific alleles such as A0201, B07002, and B3501, for example [page 24 table 2], as in claims 1 and 21-22 identifying a multiplicity of pairings, with each pairing establishing a pair between one entry in the positive data set and one entry in the negative data set and wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are:(ii) are derived from the same source protein or fragment.
Tang teaches training dataset for each length of allele [page 24 table two]. Tang teaches performances of NIEluter on 8-mer peptides [table 4] and on 10-mer peptides [table 6] [page 24], as in claims 1 and 21-22 training the machine learning algorithm on the multiplicity of pairings.
Tang teaches building NIEluter and comparing the algorithm to MHC-NP and NetMHC3.2 [page 25 left col section 2.5 and fig. 1]. Tang teaches the NIEluter is used for predicting or identifying natural processed peptides (NPP’s) [page 25 right col first para]. Tang teaches one positive dataset data is paired with the five negative sub-datasets with each pair trained with one of the following features: Hidden Markov Model, binary encoding, BLOSUM62 feature, position-specific amino acid composition, and position-specific dipeptide composition [page 23 right col second para] as in claims 1 and 21-22 creating a trained algorithm to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation.
Fridman
Fridman et al. (Fridman) disclose using affinity information derived from T-cell epitopes known to bind to MHC allele of interest, computing binding score for a series of candidate peptides derived from target protein, and ranking the peptides in a list based on numerical value of the predicted binding score wherein peptides having a higher binding score are predicted to be characterized by a higher binding affinity to the MHC allele [Fridman, claims 10 and 17], as in instant claims 1 and 21-22 wherein each pair of said multiplicity of pairings comprises entries for peptide sequences that are: (iii) similar binding affinities with respect to an HLA/MHC molecule to which the peptide of the positive counterpart data set is restricted. Here, although Fridman does not teach pairings, it would be obvious to incorporate the binding affinities of Fridman as pairings of entries for the positive and negative datasets for identifying peptide and/or peptide fragments.
Fridman discloses rules for rejecting T-cell epitope which rejects epitopes if another protein in an appropriate database of reference sequences contains an amino acid sequence which differs by 1 or 2 amino acids from the amino acid sequence of the candidate T-cell epitope and the mismatches are restricted to the MHC anchor residues of the candidate epitope and the reference sequence [Fridman, claims 8-9 and 13-14]. Fridman discloses difference binding affinities for different peptides [pages 22-23 tables 5 and 7-8]. Fridman discloses an immunogenicity enhancement filter to identify anchor-modified analogs that comprise substitutions/mutations that optimize peptide/ MHC binding interactions at the anchor positions [Fridman, page 12 para 0115]. Fridman discloses discarding antigens or alleles of interest when fragments do not pass self-similarity filters, natural processing feature filter, or binding filter [Fridman, page fig 2], as in instant claims 1 and 21-22 the amino acids at key HLA/MHC-binding anchor positions within the peptide sequences of the positive and negative data sets are removed as features for the machine learning algorithm. Therefore, it would be obvious to use the binding, self-similarity, and natural processing filters of Fridman for filtering/removing/discarding entries in positive and negative data sets for identifying multiplicity of pairings between the datasets for identifying peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation.
Dependent claim 13
Fridman discloses an algorithm or also known as a binding filter to predict relative binding affinities [page 8 para 0077]. Fridman discloses example 9 which teaches in vitro binding affinity evaluation [page 25 0197], as in claim 13.
Obvious claimed step:
Tang teaches NIEluter was able predict peptides eluted from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) of 8–11 amino acids [page 26, right col section 4 conclusion]. NIEluter is a machine learning algorithm that predicted/identified peptides eluted from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) [page 25 figure 1]. Dhanda teaches using amino acid lengths between 8 to 22 residues [page 2 left col, section 2 methodology 2.1.1]. Dhanda teaches using different c terminal and n terminal 9-15 amino acids (i.e., peptide(s)) in lengths in the positive and negative datasets [Dhanda, at page 12 Table 3S](i). Tang also teaches using pair of equal or similar length using 8-mers, 9-mers, 10-mers, and 11-mers [page 24 table 2] (i). Tang teaches using the same source from specific alleles such as A0201, B07002, and B3501 [page 24 table 2](ii). Fridman discloses using affinity information derived from T-cell epitopes known to bind to MHC allele of interest, computing binding score for a series of candidate peptides derived from target protein, and ranking the peptides in a list based on numerical value of the predicted binding score wherein peptides having a higher binding score are predicted to be characterized by a higher binding affinity to the MHC allele [Fridman, claims 10 and 17], as in claims 1 and 21-22, wherein following the training the machine learning algorithm on the multiplicity of pairings, using the trained machine learning algorithm to interrogate input data comprising amino acid sequences of peptides and/or proteins, to identify peptides or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation. Here, although Dhanda, Tang, and Fridman do not directly teach training a machine learning algorithm on a multiplicity of pairing using NIEluter, it is obvious that NIEluter can process and can be trained using peptide binding affinities and peptide fragment source and length because NIEluter processes data related to HLA/MHC peptides which are binding peptides and NIEluter is trained using 9-mer of A0201 and B0702 (i.e., similar sources and length).
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang because Tang teaches analyzing multiplicity of pairings with respect to using datasets of naturally processed peptides or natural presented peptides (NPPs) [abstract]. Additionally, one of ordinary skill would recognize that Tang also uses positive datasets that are composed of NPPs (eluted) and the negative datasets are MHC binders that cannot be processed naturally (binder) [page 23 left col section 2.1]. One of ordinary skill in the art would be motivated to combine Dhanda in view of Tang because Tang also teaches building/processing positive and negative data sets [page 23 left col section 2.1] and teaches using machine learning algorithms (i.e., support vector machine) for processing pairing between data sets for predicting peptides eluted from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) of 8–11 amino acids [page 26 right col conclusion]. Thus, there is a reasonable expectation of success to combine the known elements of using positive and negative datasets of Dhanda with the multiplicity of pairing of Tang to yield a predictable method for using a trained machine learning algorithm to interrogate input data to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang in view of Fridman because Fridman discloses a system and method for automated selection of T-cell epitope [title] that are likely to bind to an MHC I class 1 allele by collecting and curating training sets of peptide sequence known to bind to MHC alleles [Fridman, claim 1]. Here, one of ordinary skill in the art would recognize that Fridman teaches constructing training data of peptides that can bind to MHC allele [Fridman, claim 1]. Additionally, one of ordinary skill in the art would further recognize that Fridman discloses a method that filters and rejects candidate T-cell epitopes based on anchoring positions such as proline at P1 or P1’ or leucine positions [Fridman, claims 10-15]. As such, there would be a reasonable expectation of success combining the known of elements of using positive data and negative datasets and using 8-11-mers and source of proteins of Dhanda in view of the multiplicity of pairing using entries of peptides fragments (i.e., 8-11-mer) and HLA allele sources of Tang in view of the training datasets, binding affinities, and filtering of anchoring positions of Fridman to construct a predictable method using a trained machine learning algorithm to interrogate input data to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
Claim(s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 11, 13-14, 16, and 21-22, and in further view of Bremel et al. (U.S Patent Pub US 2013/0330335; Cited in the Office Action mailed 02 January 2025).
Dhanda in view of Tang in view of Fridman teach claims 1, 11, 13-14, 16, and 21-22.
Dhanda in view of Tang in view of Fridman teach a computer-implemented method for training a machine learning model using HLA/MHC/peptide complexes, building positive and negative datasets containing using HLA/MHC/peptide complex data related to length, source, and binding and comparing the datasets to pairings to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing transportation and HLA/major histocompatibility complex (MHC) presentation.
Dhanda in view of Tang in view of Fridman do not teach claims 9-10.
Bremel teaches that haplotype refers to the HLA alleles found on one chromosome and the proteins encoded. Haplotype may also refer to the allele present at any one locus within the MHC. Each class of MHC is represented by several loci e.g., HLA-A (Human Leukocyte Antigen-A), HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-P and HLA-V for class I and HLA-DRA, HLA-DRBl-9, HLA-, HLA-DQAl, HLA-DQBl, HLA-DPAl, HLA-DPBl, HLA-DMA, HLADMB, HLA-DOA, and HLA-DOB for class II [Bremel, disclosure [0107]], as in claims 9-10.
It would have been obvious to one of ordinary in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang in view of Fridman, and in further view of Bremel because Bremel discloses bioinformatic processes for determination of peptide binding [Bremel, abstract]. One of ordinary skill in the art would be motivate to combine Dhanda in view of Tang in view of Fridman, and in further view of Bremel because Bremel discloses methods for identifying ligands by generating amino acid data subsets identified as having a binding affinity for a binding partner [Bremel, claim 1]. One of ordinary skill in the art would expect a reasonable success in combining Dhanda in view of Tang in view of Fridman, and in further view of Bremel because Bremel discloses using binding partners with MHC class I and II binders [Bremel, claims 2-8]. Bremel discloses the “neural network” refers to various configurations of classifiers used in machine learning, including multilayered perceptrons with one or more hidden layer, support vector machines and dynamic Bayesian networks [Bremel, disclosure [0136]]. Therefore, the limitations of Dhanda in view of Tang in view of Fridman, and in further view of Bremel could be utilized to construct, train, and create a machine learning (statistical inference model) algorithm to identify peptide features positively associated with transportation and MHC presentation.
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 11, 13-14, 16, and 21-22, and in further view of Mei et al. (Biopolymers, 2005, Vol.80 (6), p.775-786; Cited in the Office Action mailed 02 January 2025).
Dhanda in view of Tang in view of Fridman teach claims 1, 11, 13-14, 16, and 21-22.
Dhanda in view of Tang in view of Fridman teach a computer-implemented method for training a machine learning model using HLA/MHC/peptide complexes, building positive and negative datasets containing using HLA/MHC/peptide complex data related to length, source, and binding and comparing the datasets to pairings to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing transportation and HLA/major histocompatibility complex (MHC) presentation.
Dhanda in view of Tang in view of Fridman do not teach claim 17.
Mei et al. (Mei) teaches new set of amino acid descriptors, i.e., VHSE (principal components score Vectors of Hydrophobic, Steric, and Electronic properties), is derived from principal components analysis (PCA) on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which are included in total 50 physicochemical variables of 20 coded amino acids [Abstract], as in claim 17.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang in view of Fridman, and in further view of Mei because Mei teaches using a new set of descriptors called principal component score Vectors of Hydrophobic, Steric, and Electronic properties (VHSE) [abstract]. One of ordinary skill in the art would recognize that, although Mei does not teach using MHC molecules, the descriptors can be applied to amino sequences of any origin. One of ordinary skill in the art would expect a reasonable success in combining Dhanda in view of Tang in view of Fridman, and in further view of Mei because Mei teaches using new descriptors derived from PCA analysis of coded amino acids and using vectors which could be implemented into the systems Dhanda, Tang, and Fridman to further describe the amino sequences and their inherent properties in order to identify peptides associated with cellular transportation proteins or HLA/MHC proteins.
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 11, 13-14, 16, and 21-22, and in further view of Zhiliang et al. (Science China. Chemistry, 2008-10, Vol.51 (10), p.946-957; Cited in the Office Action mailed 02 January 2025).
Dhanda in view of Tang in view of Fridman teach claims 1, 7-8, 11, 13-14, 16, and 21-22.
Dhanda in view of Tang in view of Fridman teach a computer-implemented method for training a machine learning model using HLA/MHC/peptide complexes, building positive and negative datasets containing using HLA/MHC/peptide complex data related to length, source, and binding and comparing the datasets to pairings to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing transportation and HLA/major histocompatibility complex (MHC) presentation.
Dhanda in view of Tang in view of Fridman do not teach claim 18.
Zhiliang et al. (Zhiliang) teaches that a new descriptor called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids [abstract]. Zhiliang teaches the VTSA vector was applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modeling (QSARs/QSAMs) [abstract]. Zhiliang teaches modeling by support vector machine (SVM) [page 952 left col].
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang in view of Fridman, and in further view of Zhiliang because Zhiliang teaches methods using new descriptors, called vector of topological and structural information for coded and noncoded amino acids (VTSA), derived by principal component analysis (PCA) [abstract]. One of ordinary skill in the art would recognize that, although Zhiliang does not teach cellular transportation proteins or HLA/MHC, the VTSA can be applied to amino sequences, such as from B-cells, for example. One of ordinary skill in the art would be motivated to combine Dhanda in view of Tang in view of Fridman, and in further view of Zhiliang because Zhiliang teaches VTSA vector was applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modeling’s (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN) and good results were obtained [abstract]. One of ordinary skill in the art would expect a reasonable success in combining Dhanda in view of Tang in view of Fridman, and in further view of using vectors of topological and structural information for coded and noncoded amino acids (VTSA) of Zhiliang to construct a method for training a machine learning algorithm or statistical inference model/algorithm using descriptors that can be utilized as features to identify peptide features positively associated with transportation and MHC presentation.
Claim(s) 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanda in view of Tang in view of Fridman, as applied to claims 1, 11, 13-14, 16, and 21-22, and in further view of Wen et al. (Gene, 2014-08, Vol.546 (1), p.25-34; Cited in the Office Action mailed 02 January 2025).
Dhanda in view of Tang in view of Fridman teach claims 1, 11, 13-14, 16, and 21-22.
Dhanda in view of Tang in view of Fridman teach a computer-implemented method for training a machine learning model using HLA/MHC/peptide complexes, building positive and negative datasets containing using HLA/MHC/peptide complex data related to length, source, and binding and comparing the datasets to pairings to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing transportation and HLA/major histocompatibility complex (MHC) presentation.
Dhanda in view of Tang in view of Fridman do not teach claim 19.
Wen et al. (Wen) teaches the k-mer natural vector is obtained by concatenating the first group of parameters (the frequency of occurrence of each k-mer in the sequence) [page 28]. Wen teaches that the k-mer model of a genetic sequence can be described as follows: consider a genetic sequence s of length L, ‘N1, N2, …NL’, where Ni ∈ {A, C, G, T}, l = 1,2,…,L. A string of consecutive k nucleotides within a genetic sequence is called a k-mer [page 26 right col section 2.1], as in claim 19. Here, it is obvious that k can be equal to 1 or greater.
It would have been obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Dhanda in view of Tang in view of Fridman, and in further view of Wen because Wen teaches describing k-mers and using k-mer vectors for phylogenetic analysis [title]. One of ordinary skill in the art would recognize that, although Wen teaches k-mer natural vector with respect to phylogenetic analysis, the k-mer frequency and k-mer length of Wen could be utilized to analyze general peptides or amino acid sequence data. One of ordinary skill in the art would be motivated to Dhanda in view of Tang in view of Fridman, and in further view of Wen because Wen teaches that the k-mer natural vector method is a very powerful tool for analyzing and annotating genetic sequences (entries of peptide sequences). One of ordinary skill in the art would expect a reasonable success using the tools of Wen with Dhanda in view of Tang in view of Fridman because the tools of Wen can be used to detect similarities of genetic sequences [page 29 section 2.4] and frequencies of k-mer in genetic sequences [page 28 right col the k-mer natural vector para]. Therefore, the combination of Dhanda, Tang, Fridman, and Wen would construct a predictable method step using k-mer frequencies for identifying peptides or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.
Response to Arguments
Applicant’s arguments, filed 02 July 2026, have been fully considered and the rejection is maintained.
The Applicant disagrees that claims are obvious and a skilled person would find no motivation to combine the references of the above 103 rejection [remarks, page 14].
The Applicant states Dhanda is focused on predicting IL4- inducing peptides for Th2 immune responses, using motif and compositional analysis. The Applicant states Tang aims to predict naturally processed peptides (NPPs) eluted from HLA class I molecules, using SVM ensemble models. The Applicant states Fridman is concerned with automated selection and optimization of T-cell epitopes for MHC class I binding, using scoring matrices and filters [remarks, page 14]. The Applicant states there is no logical or technical chain that naturally links the disclosures together, and the skilled person would have no motivation to combine the three approaches in the three documents, except with the benefit of hindsight [remarks, page 14]. The Applicant provides what Dhanda does not teach [remarks, page 15]. The Applicant states nowhere does Fridman describe or suggest removing anchor residues from peptide sequences before feature extraction or machine learning. rather, the anchor residues are included in the scoring and optimization process.
In response, claim 1 and 21-22 recite “the amino acids at key HLA/MHC-binding anchor positions within the peptide sequences of the positive and negative data sets are removed as features for the machine learning algorithm” which only requires anchor positions be “removed” and does not recite or require any special condition(s) for which anchor positions are removed. Here, Fridman discloses an immunogenicity enhancement filter to identify anchor-modified analogs that comprise substitutions/mutations that optimize peptide/ MHC binding interactions at the anchor positions [Fridman, page 12 para 0115]. Fridman discloses discarding antigens or alleles of interest when fragments do not pass self-similarity filters, natural processing feature filter, or binding filter [Fridman, page fig 2]. Therefore, it would be obvious to use the binding, self-similarity, and natural processing filters of Fridman for filtering/removing/discarding entries/anchor residues in positive and negative data sets for identifying multiplicity of pairings between the datasets for identifying peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex (MHC) presentation.
The Applicant states the cited references fails to teach “multiplicity of pairings” of claim 1 [remarks, page 16]. The Applicant states Tang does not teach the claimed entry-level pairings, Tang also does not teach training a machine learning algorithm "on the multiplicity of pairings," as required by claim 1.
In response, with respect to teaching “multiplicity of pairings”, as noted above, Dhanda, Tang and Fridman were utilized to teach the “multiplicity of pairings”. For example, Dhanda teaches using amino acid lengths between 8 to 22 residues [page 2 left col, section 2 methodology 2.1.1]. Dhanda teaches using different c terminal and n terminal 9-15 amino acids (i.e., peptide(s)) in lengths in the positive and negative datasets [Dhanda, at page 12 Table 3S] (i). Tang also teaches using pair of equal or similar length using 8-mers, 9-mers, 10-mers, and 11-mers [page 24 table 2] (i). Tang teaches using the same source from specific alleles such as A0201, B07002, and B3501 [page 24 table 2] (ii). Fridman et al. (Fridman) disclose using affinity information derived from T-cell epitopes known to bind to MHC allele of interest, computing binding score for a series of candidate peptides derived from target protein, and ranking the peptides in a list based on numerical value of the predicted binding score wherein peptides having a higher binding score are predicted to be characterized by a higher binding affinity to the MHC allele [Fridman, claims 10 and 17]. Thus, the teachings of Dhanda, Tang, and Fridman read on said multiplicity of pairings comprises entries for peptide sequences that are: (i) of equal or similar length, (ii) are derived from the same source protein or fragment, and (iii) similar binding affinities, with respect to an HLA/MHC molecule to which the peptide of the positive counterpart data set is restricted of claims 1 and 21-22.
Regarding Tang not teaching the training step, the argument is not persuasive because as noted in the 35 U.S.C §103 rejection above, Dhanda, Tang, and Fridman render the claimed step obvious. It is noted the training step only requires training the machine learning algorithm on the multiplicity of pairings which is suggested by Dhanda, Tangm and Fridman above. Therefore, until the claims are amended, Dhanda, Tang, and Fridman teach the above claims step(s).
Conclusion
Claims 1, 9-19, and 21-22 are rejected.
No claims are allowed.
Finality
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.
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/J.C.P./ Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
