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 10-11, 14-17, 19-21, 25-26, 28-30, 32, 35-38, and 40 are pending.
Claims 26, 28-32, 35-38, and 40 are withdrawn.
Claims 1-9, 12-13, 18, 22-24, 27, 31, 33-34, 39, and 41-47 are canceled.
Claims 10-11, 14-17, 19-21, and 25 are examined on the merits.
Priority
Applicant's claim for the benefit of a prior-filed U.S. Provisional Application No. 6 63/013,480 field 04/21/2020 is acknowledged. Accordingly, the effective filing date of the claimed invention is 04/21/2020.
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 06/24/2026.
The U.S.C. 101 rejections are withdrawn at least in view of the analysis Step 2A, 2nd prong, 1st consideration relating to an improvement to the functioning of a computer or improvement to another technology or technical field integrating possible judicial exceptions into a practical application (MPEP 2106.04(d) and (d)(1)), the improvement to the field of computational immunology, in this instance comprising reliable identification of TCR-pMHC binding events from noisy, high-throughput dextramer binding data, and the generation of a structured TCR- pMHC binding affinity map that enables the rapid identification of specific peptides predicted to bind a given TCR sequence. Instant claims achieve this improvement through an ordered combination of data processing and computational steps to ensure that training data reflects the biological signal. In this regard, Applicant's 06/24/2026 remarks at pp. 17-23 support withdrawal of the rejection.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Rejections - 35 USC § 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 10, 14-15, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fischer et al. (Predicting antigen specificity of single T cells based on TCR CDR3 regions, bioRxiv, August 14, 2019, pages 1-26), as evidence by 10X Genomics (A new way of exploring immunity: linking highly multiplexed antigen recognition to immune repertoire and phenotype, 10xgenomics, 2019, pages 1-16) in view of Yamashita (US20210012858A1; as cited in the IDS form dated 02/09/2026).
Regarding claim 10, Fischer teaches a high throughput single-cell, multiple sequence-data deep learning method to assign TCR to epitope specificity (abstract). Fischer further teaches a data set based on single-cell pMHC capture in which paired ɑ and β-chain reconstructed for 10,000s of cells and binding-specificity measured for 44 distinct pMHC complexes. Fischer further teaches receiving dextramer sequencing data as evidenced by 10X Genomics (Section: Results: pg. 2, para. 2); reading on limitations of receiving single cell sequencing data comprising single cell sequence data, dextramer sequence data, and single cell T-Cell Receptor (TCR) sequence data.
Fischer further teaches removing putative doublets from the data set (Section: Cell-specific covariates improve binding event prediction, pg. 2, para. 3); reading on limitations of filtering, from the dextramer sequence data, based on the single cell sequence data, data associated with low-quality cells
Fischer further discloses that an alternative interpretation of the improved performance of multi-task models is their ability to learn better de-noised low-dimensional representations of TCR sequence (pg. 4, para. 2); reading on limitations of adjusting, based on a measure of background noise, the dextramer sequence data.
Fischer further discloses designing a model to predict TCR-antigen binding based on ɑ- and β-chain sequences and cell-specific covariates accounting for the variability found in data including chains (Fig. 1b) (Results). Fischer further discloses a feedforward neural network to predict an antigen specificity of a T-cell based on the alpha and beta chain sequences, which “filters” for the specific amino acid sequences within the CDR3 regions of alpha and beta chains of T-cell receptor (Figure 1, pg. 8, para. 1). Fischer further discloses using paired TCR ɑ- and β-chain while determining the T-cell specificity to train multiple deep learning (pg. 1, last para.; see also, pg. 2 Results). Fischer further discloses identifying TCR sequences after applying filters and by focusing on beta chain and CDR3 sequences (pg. 23, L494-500). Fischer further discloses removing all cellular barcodes that contain more than one ɑ- or β-chain as mature CD8+ T cells are expected to only have a single functional ɑ- and β-chain allele (pg. 22, L. 455-460); reading on limitations of filtering, from the dextramer sequence data, based on the single cell TCR- data, data according to a presence or an absence of an a-chain or a b-chain by removing data associated with cells having one or more alpha chains, one or more beta chains, or multiple alpha or beta chains.
Fischer further discloses that a newly developed single-cell technology that enables the simultaneous sequencing of the paired TCR ɑ- and β-chain while determining the T-cell specificity to train multiple deep learning architectures modeling the TCR-pMHC interaction including both chains (Introduction, first page, last para.); reading on limitations of identifying data remaining in the filtered dextramer sequence data as associated with reliable TCR-pMHC binding events.
Fischer further discloses identifying TCR sequences after applying filters and by focusing on beta chain and CDR3 sequences and using such data as training data set (pg. 23, L494-503). Fischer further discloses predicting antigen specificity based on TCR sequences from single-cell data (pg. 5, L. 160-163); reading on limitations of identifying, based on data remaining in in the filtered dextramer sequence data, plurality of TCR sequences.
Fischer further discloses models fit on both the CDR3 loop of ɑ- and β-chain of the TCR (Fig. 1b) and models fit on the CDR3 loop of the β-chain and the antigen sequence (Fig. 2a). Fischer further discloses that the input vector comprises V and J gene segment sequence (using 10x Genomics dataset inherently includes VDJ junction information; see 10x Genomics, pg. 3, col. 2: “The Chromium Single Cell Immune Profiling workflow with Feature Barcode technology generates Single Cell 5’Gene Expression, V(D)J, and Cell Surface Protein libraries… Chromium Single Cell V(D)J enriched libraries, 5’ Gene Expression libraries, and Cell Surface Protein libraries were quantified, normalized, and sequenced according to the User Guide”). Fischer further discloses integrating two sequences and using separate sequence-embedding layer stacks for each sequence (all models presented in Fig. 1 and models indicated as “separate” in Fig. 2) or by appending the two padded sequences and using a single sequence-embedding layer stack (models indicated as “concatenated” in Fig. 2) (pg. 20, L. 378-386). Fischer further discloses using one-hot encoding and using 1 by 1 convolution, to represent sequences numerically and dimensionality reduction of multi-dimensions (Supp. Figure 3). Fischer further discloses training antigen specificity models based on TCR sequences from single-cell data (pg. 5, L. 142-155 and 160-170). Fischer further discloses padding each CDR3 sequence to a length of 40 amino acids and concatenated these padded chain observations to a sequence of length 80 for models that were trained on both chains (pg. 22, L. 464-466). Fischer further discloses generating negative samples for both training and test set separately by generating unobserved pairs of TCR and antigens (pg. 23, L. 498-500); reading on limitations of generating a one-dimensional input vector comprising an encoded paired alpha-beta chain CDR3 amnio acid sequence and generating a training dataset and training a model.
Fischer further discloses that an alternative interpretation of the improved performance of multi-task models is their ability to learn better de-noised low-dimensional representations of TCR sequences, through the integration of more diverse training data (pg. 4, L. 120-124); Fischer further discloses predicting a binding events and binding affinity based on TCR CDR3 sequences and pMHC count (Fig. 2, P.9, Pg. 3, last para.). Fischer further discloses TCR-pMHC modeling including both chains (pg. 1, last para.); reading on limitations of training, based on the training data set, a machine learning model to predict one or more TCR-pMHIC binding events: presenting, to the trained machine learning model, a TCR sequence; and generating, by the trained machine learning model and based on the TCR sequence, a TCR-pMHIC binding affinity map.
Further regarding claim 10, Fischer discloses predicting binding affinities (pg. 11, col. 2, para. 3). Fischer does not expressly disclose a binding affinity map comprising, for the TCR sequence, a plurality of peptides and a predicted binding likelihood for each peptide of the plurality of peptides; and identifying, based on the TCR-pMHC binding affinity map, one or more peptides that the TCR sequence is predicted to bind. Yamashita discloses a method for classifying immunological entities (abstract) and teaches a machine learning method of antigen specific clustering of TCRs [0262]. Yamashita further teaches taking TCR sequences as input [0264] and performing preprocessing/feature extractions based on V or J genes and CDR3 [0271-0277]. Yamashita further teaches predicting binding affinity by a machine learning model [0279-0287]. Yamashita further teaches that as a result of learning; machine learning returns the probability of a given immunological entity pair binding to the same binding mode/epitope/antigen [0103]. Yamashita further teaches that the auto-encoder extracts a feature and projects the feature onto a high dimensional vector space. The feature directly becomes a high dimensional vector space element. Projection can be interpreted as including identity mapping [0059].
Regarding claim 14, Fischer teaches a data set based on single-cell pMHC capture in which paired ɑ- and β-chains could be successfully reconstructed for 10,000s of cells and binding-specificity measured for 44 distinct pMHC complexes. Fischer utilizes the dataset as described in Fischer at page 2, para. 2 with reference to the 10x dataset. Fischer further teaches the dextramer is sorted (Figure 1 of 10x Genomics) and that the panel contains 6 dCODE Dextramer® reagents with irrelevant negative control peptides to assist in the detection of non-specific binding events (pg. 3, col. 1, first para. Of 10X Genomics). Fischer further teaches that after the final centrifugation, the cells were resuspended and cell suspension was reserved for a non-sorted cell population, as evidenced by 10X Genomics (pg. 3, col. 1, last para.); reading on limitations of determining, based on the dextramer sequence data, sorted dextramer sequence data wherein the sorted dextramer sequence data comprises sorted test dextramer sequence data and negative control dextramer sequence data and unsorted dextramer sequence data, wherein the unsorted dextramer sequence data comprises unsorted test dextramer sequence data.
Regarding claim 15, Fischer discloses setting a threshold such that a specific binding event required a UMI count greater than 10 that was also greater than five times the highest negative control UMI count for that cell (pg. 5, col. 1). Fischer teaches that the dCODE dextramer reagents with the negative control peptides were combined prior to sorting, as such negative control applied to both sorted and non-sorted signals (pg. 3, col. 1, first para. Of 10X Genomics); reading on limitations of determining a maximum negative control dextramer signal; determining, a maximum sorted dextramer signal and determining, a maximum unsorted dextramer signal.
Regarding claim 19, Fischer teaches that in standard single-cell RNA-seq processing, such effects are often rectified through normalization. Fischer further states that such normalization factors and negative control pMHC counts (for example, pMHC normalization) are useful predictors of a false negative binding event: We compared models only considering the donor identity covariate and models that also included a scaled total mRNA count (for example, cell-wise) covariate and ones that contained negative control count covariates (section: Cell-specific covariates improve binding event prediction, pg. 2, para. 3); reading on limitations of normalizing the dextramer sequence data, wherein normalizing the dextramer sequence data comprises: performing, for each cell represented in the dextramer sequence data, cell-wise and normalization on the dextramer signals associated with each cell; and performing, for each cell represented in the dextramer sequence data, pMHC-wise normalization.
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 Fischer and Yamashita, the examiner concludes that the combination of Fischer and Yamashita represents applying known technique to a known method. Both Fischer and Yamashita are directed to predicting antigen specify of T cells using high throughput single cell repertoire profiling experiments. Fischer only disclosed data preprocessing of generating training dataset of multi-task models, given multiple initial amino acid embeddings, for TCR sequences and inherently by using 10x Genomics dataset V and J segment sequences as a one-dimensional input vector comprising paired alpha-beta chain CDR3 sequence and predicting binding affinity. In the same field of research, Yamashita provided a binding affinity model predictor that outputs the probability of a given immunological entity pair binding to the same binding mode/epitope/antigen. One ordinary skilled in the art would have recognized that applying the known technique of Yamashita would have yielded predictable results and resulted in an improved method. Combining the method of Fischer with probability output of Yamashita would have successfully allowed for assembling a meaningful dataset and more accurate predictive results. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer and Yamashita and the combination would have been successful. This combination would have been expected to have provided a more meaningful assembly of dataset for a more accurate binding site 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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Fischer in view of Yamashita, as applied to claim 10, 14-15, 19 above, in view of Dash et al. (Quantifiable predictive features define epitope specific T cell receptor repertoires, July 2017, 7 Macmillan Publishers Limited, part of Springer Nature pages 89-109), and further in view of Wolock (Scrublet: Computational Identification of Cell Doublets in Single Cell Transcriptomic Data, Cell Syst. 2019 April 24; 8(4): 281–291).
Regarding claim 11, Fischer teaches removing putative doublets from the data set (Section: Cell-specific covariates improve binding event prediction, pg. 2, para. 3); reading on limitations of filtering, from the dextramer sequence data, based on the single cell sequence data, data associated with low-quality cells
Fischer further teaches removing putative doublets from the data set (Section: Cell-specific covariates improve binding event prediction pg. 2, para. 3).
Further regarding claim 11, Fischer and Yamashita do not expressly disclose determining a number of genes outside a gene threshold range. Dash is directed to the determinants of epitope specificity of T cell receptor repertoires (Abstract). Dash teaches quantifying V and J segment usage within a chain and across chains characterizing by an overrepresentation of individual genes as well as significant gene pairing preferences (pg. 89, col. 2, para. 2). Dash further teaches setting upper and lower bound thresholds and quantifying covariations between gene usage (pg. 94, col. 2, last para. - page 95, col. 1, first para.); reading on limitations of determining, for each cell represented in the dextramer sequence data, based on the single cell sequence data, a number of genes; removing, from the dextramer sequence data, data associated with cells having a number of genes outside of a gene threshold range.
Further regarding claim 11, Fischer, Yamashita, and Dash do not disclose determining a fraction of mitochondrial gene expression that exceeds a gene expression threshold. However, Wolock is directed to computational identification of cell doublets in single cell transcriptomic data and introduces Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets (Summary). Wolock further teaches excluding mitochondrial gene counts of higher than a threshold in the doublet detection step (pg. 23, para. 3).
Applying the KSR standard to Fischer, Yamashita, and Dash, the examiner concludes that the combination of Fischer, Bulik-Sullivan, and Dash represents the use of known techniques to improve similar methods. Fischer, Bulik-Sullivan, and Dash are directed to predicting antigen specify of T cells using high throughput single cell repertoire profiling experiments. Fischer and Yamashita only disclosed data preprocessing and dataset assembly and training a model. In the same field of research, Dash provided quantifying V and J segment usage within a chain and across chains characterized by an overrepresentation of individual genes according to defined thresholds, for the purpose of assessing the diversity of the immune receptor repertoire, indicating which gene segments are more common in antigen recognition providing a higher resolution of epitope specificity. Combining the method of Fischer and Yamashita with gene quantification of Dash would have allowed for identifying potential antigen-specific TCRs. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer and Yamashita with methods of Dash. This combination would have been expected to have provided a more meaningful assembly of dataset for a more accurate binding site 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
Applying the KSR standard to Fischer, Yamashita, Dash and Wolock, the examiner concludes that the combination of Fischer, Bulik-Sullivan, Dash and Wolock, represents the use of known techniques to improve similar methods. Fischer, Bulik-Sullivan, Dash and Wolock use high throughput single cell repertoire profiling experiments. Fischer, Bulik-Sullivan, and Dash only disclosed data preprocessing and dataset assembly and training a model and related gene quantifications. Wolock provided a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets by excluding mitochondrial gene counts of higher than a threshold in the doublet detection step. Combining the method of Fischer, Bulik-Sullivan, and Dash with mitochondrial gene expression analysis of Wolock would have allowed for a more accurate identification of doublets. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer, Dash, and Wolock. This combination would have been expected to have provided a more meaningful assembly of dataset by removing problematic multiplets. 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.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Fischer in view of Yamashita, as applied to claim 10, 14-15, 19 above, in view of Tungatt et al. (Antibody Stabilization of Peptide–MHC Multimers Reveals Functional T Cells Bearing Extremely Low-Affinity TCRs, J Immunol (2015) 194 (1): 463–474)
Regarding claim 16, Fischer and Yamashita teach the limitations of claims 11, and 14-15 above. Further, Fischer discloses that an alternative interpretation of the improved performance of multi-task models is their ability to learn better de-noised low-dimensional representations of TCR sequence through the integration of more diverse training data (Continuous binding affinities can be predicted based on pMHC counts, pg. 3, last para.). Fischer further discloses using fluorescent antibodies to stain reactions and gating cells, as evidenced by 10X Genomics (pg. 3, col. 1, para. 3). Fischer further discloses using the binarization described in 10X Genomics for the raw counts to receive binary outcome labels: A total pMHC UMI count larger than 10 and at least five times as high as the highest observed UMI count across all negative control pMHCs was required for a binding event. If more than one pMHC passed these criteria, the pMHC with the largest UMI count was chosen as the single binder (Binarization of 10x CD8+ T-cell pMHC counts into bound and unbound states); reading on limitations of estimating, based on the maximum negative control dextramer signals, a dextramer binding background noise; estimating, based on the maximum sorted dextramer signals and the maximum unsorted dextramer signals, a dextramer sorting gate efficiency; determining, based on the dextramer binding background noise and the dextramer sorting gate efficiency measure of background noise.
Fischer and Yamashita do not expressly disclose subtracting, for each cell represented in the dextramer sequence data, the measure of background noise from a dextramer signal associated with each cell. However, Tungatt discloses an improved staining technique using anti-fluorochrome unconjugated primary Abs followed by secondary staining with anti-Ab fluorochrome-conjugated Abs to amplify fluorescence intensity. Tungatt further discloses a technique resulting an improved fluorescence intensity with both pMHC tetramers and dextramers (abstract). Tungatt further discloses subtracting background noise from signals tetramer/dextramer signals (Figure 2 and 4).
Applying the KSR standard to Fischer, Yamashita, and Tungatt, the examiner concludes that the combination of Fischer, Yamashita, and Tungatt represents the use of known techniques to improve similar methods. Fischer, Yamashita, and Tungatt are directed to characterization of antigen specify of T cells using dextramer sequence data. Fischer and Yamashita only disclosed estimating, a dextramer binding background noise; estimating a dextramer sorting gate efficiency, and data preprocessing, dataset assembly, and training a model. In the same field of research, Tungatt provided an improved staining technique in which they subtracted background noise from tetramer/dextramer signals. Combining the background noise estimation of Fischer and Yamashita with background noise subtraction of Tungatt would have allowed removal of non-specific binding and unwanted signals, resulting in improved signal to noise ratio. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer, Yamashita, and Tungatt. This combination would have been expected to have provided an improved noise to signal ratio ensuring that data reflects the actual antigen-specific binding event. 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.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Fischer in view of Yamashita, as applied to claim 10, 14-15, 19 above, in view of Redmond et al. (Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq, Genome Medicine (2016) 8:80).
Regarding claim 20, Fischer teaches designing a model to predict TCR-antigen binding based on ɑ- and β-chain sequences and cell-specific covariates accounting for the variability found in data including chains (Fig. 1b) (Results) indicating that only sequences containing both alpha and beta chains were used. Fischer further discloses removing all cellular barcodes that contain more than one ɑ- or β-chain as mature CD8+ T cells are expected to only have a single functional ɑ- and β-chain allele (pg. 22, L. 455-460). Fischer does not expressly teach removing, from the normalized dextramer sequence data, based on the presence or the absence of the at least one a-chain and the at least one b-chain, data associated with cells having only an a-chain, only a b-chain, or multiple a- or b-chains.
However, Redmond teaches a method of characterizing of the repertoire of the T-cell receptor (TCR) alpha and beta chains. Redmond further teaches that after normalization, potential alpha and beta chains were concatenated and gap-filled assembly was performed and that only samples with expression of both TRAV and TRBV was used in the analysis (pg. 8) implying that cells with only an alpha chain or only a beta chain might not be considered as part of the core output of the scTCRseq analysis.
Applying the KSR standard to Fischer, Yamashita, and Redmond, the examiner concludes that the combination of Fischer, Yamashita, and Redmond represents the use of known techniques to improve similar methods. Fischer, Yamashita, and Redmond are directed to predict binding affinity of antigen to T cells by using Single-cell RNAseq reads. Fischer and Yamashita only disclosed designing a model to predict TCR-antigen binding based on a- and B-chain sequences and removing barcodes containing multiple alpha and beta chains. In the same field of research, Redmond provided method of excluding data that includes either of the chains after the normalization step. Combining the predictive model of Fischer and Yamashita with technique of chain exclusion after normalization for a higher accuracy in the prediction. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer, Yamashita, and Redmond. This combination would have been expected to have provided improved targeted therapeutics. 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.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Fischer in view of Yamashita, as applied to claim 10, 14-15, 19 above, in view of Yelenski et al. (US 20220265812 A1).
Regarding claim 21, Fischer discloses that in their predictive algorithm, a python package was built (TcellMatch) that hosts a pre-trained model zoo for analysts to impute pMHC-derived antigen specificities and allows transfer and re-training of models on new data sets (introduction). Further Fischer teaches predicting TCR-antigen binding based on ɑ- and β-chain sequences and cell-specific covariates and modeling binding events within a panel of antigens as a single- or multi-task prediction model through a vector of output nodes corresponding to antigens. (results); reading on limitations of training a predictive model based on the data remaining in the normalized filtered dextramer sequence data; predicting a binding status of a newly presented receptor sequence according to the trained predictive model; presenting, to the predictive model, subject TCR sequence data.
Further regarding claim 21, Fischer and Yamashita do not expressly disclose determining, based on a repository of antigen locations and the subject TCR binding pattern, a likelihood that a subject associated with the TCR sequence data has traveled to one or more locations. However, Yelenski teaches determining whether the subject expresses one or more HLA alleles involves a population-based analysis. More specifically, determining whether the subject expresses one or more HLA alleles includes determining the origin of the subject and further identifying one or more HLA alleles that are known to be commonly expressed by the population of individuals of that origin. Examples of an origin can be geographic location [0507].
Applying the KSR standard to Fischer, Yamashita and Yelenski , the examiner concludes that the combination of Fischer, Bulik-Sullivan, and Yelenski represents the use of known techniques to improve similar methods. Fischer, Yamashita and Roman are directed to predicting binding affinity of antigen to T cells using dextramer sequence data. Fischer and Yamashita only disclosed training a predictive model based on the data remaining in the normalized filtered dextramer sequence data; predicting a binding status of a newly presented receptor sequence. In the same field of research, Yelenski provided a population-based analysis where the geographic location of the subject can be determined. Combining the predictive model of Fischer and Yamashita with population-based analysis of Yelenski would have provided insight into targeted therapy for the subject. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer, Yamashita and Yelenski. This combination would have been expected to have provided improved targeted therapeutics. 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.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Fischer in view of Yamashita, as applied to claim 10, 14-15, 19 above, in view of Peng (US11,513,113B2).
Regarding claim 25, Fischer teaches steps as disclosed above with respect to claim 10. Fischer further teaches predicting TCR-antigen binding based on ɑ- and β-chain sequences and cell-specific covariates and modeling binding events within a panel of antigens as a single- or multi-task prediction model through a vector of output nodes corresponding to antigens. (Results, pg. 2, para. 2); reading on limitations of generating, based on the data remaining in the normalized dextramer sequence data associated with reliable TCR-pMHC binding events, a TCR binding pattern for a subject.
Further regarding claim 25, Fischer and Yamashita do not expressly disclose receiving, at a subsequent point in time, second single cell sequence data, second dextramer sequence data, and second single cell T Cell Receptor (TCR) sequence data for the subject; determining, based on the second single cell sequence data, second dextramer sequence data, and second single cell T Cell Receptor (TCR) sequence data for the subject, a second TCR binding pattern; and identifying, based on a comparison of the TCR binding pattern for the subject and the second TCR binding pattern, the subject.
However, Peng teaches a method of monitoring an immune repertoire in a subject, comprising: providing two or more distinct particle sets, each distinct particle set comprising a unique antigen peptide and at least one defined barcode operably associated with the identity of the antigen peptide, and each set comprises a first particle comprising a first identifying label and a second particle comprising a second identifying label distinct from the first identifying label; providing a sample known or suspected to comprise one or more T cells, wherein the sample is obtained from a subject over time; contacting the sample with the two or more particle sets, wherein the contacting comprises providing conditions sufficient for a single T cell to bind to the unique antigen of at least one particle set; isolating one or more T cells associated with the first and second identifying label; performing an assay to identify one or more barcodes bound to the isolated T cell; determining a ratio of the barcodes bound to the isolated T cell wherein the ratio is calculated by identifying a first copy number of a predominant barcode and a second copy number of a distinct barcode from step (e) and dividing the first copy number by the second copy number; identifying the antigen specificity of the T cell based on the ratio; and monitoring changes in the antigen specific T cells identified by the method in the subject (col. 6, last two para.); reading on limitations of receiving, at a subsequent point in time, second single cell sequence data, second dextramer sequence data, and second single cell T Cell Receptor (TCR) sequence data for the subject; determining, based on the second single cell sequence data, second dextramer sequence data, and second single cell T Cell Receptor (TCR) sequence data for the subject, a second TCR binding pattern; and identifying, based on a comparison of the TCR binding pattern for the subject and the second TCR binding pattern, the subject.
Applying the KSR standard to Fischer, Yamashita, and Peng, the examiner concludes that the combination of Fischer, Sid Bulik-Sullivan, and Peng represents the use of known techniques to improve similar methods. Fischer, Yamashita, and Peng are directed to characterization of antigen specify of T cells using dextramer sequence data. Fischer and Yamashita disclosed generating, based on the data remaining in the normalized dextramer sequence data associated with reliable TCR-pMHC binding events, a TCR binding pattern for a subject. In the same field of research, Peng provided a second dextramer sequence data, and second single cell T Cell Receptor (TCR) sequence data for the subject by monitoring of the immune repertoire of the subject that allows gaining insight about the health status of the subject. Combining the predictive model of Fischer and Yamashita with monitoring system of Peng would have provided insight into the health of the subject, which in turn is crucial for disease diagnosis and treatment response prediction. One ordinary skilled in the art before he effective filing data of the claimed invention would have had a reasonable expectation of success at combining the method of Fischer, Yamashita, and Peng. This combination would have been expected to have provided an improved prognosis. 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 Applicant’s Arguments
Applicant's arguments filed 06/24/2026 have been considered but they are not persuasive. Amendments to instant claims necessitated new art rejections. As stated above, the combination of Fischer and Yamashita disclose all the limitations of claim 10.
Conclusion
No claims are allowed.
Claim 17 appears to be free from the prior art because the closest prior art to Fischer, Yamashita, Dash, Tungatt, Redmond, Yelenski, and Peng does not appear to teach or fairly suggest the steps directed to estimating, based on the maximum sorted dextramer signals and the maximum unsorted dextramer signals, the dextramer sorting gate efficiency comprises determining a maximum difference between the maximum sorted dextramer signals and the maximum unsorted dextramer signals.
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
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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