DETAILED ACTION
Applicant’s response, filed Aug 13 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
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 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.
Claim Status
Claims 1-20 are pending.
Claims 1, 12, and 17 are objected to.
Claims 1-20 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/369,482, filed Jul 26 2022. Accordingly, each of claims 1-20 are afforded the effective filing date of the Jul 26 2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on May 21 2026 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Nucleotide and/or Amino Acid Sequence Disclosures
The sequence listing filed Aug 13 2026 is accepted.
Claim Objections
The outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities. The instant objection is newly stated and is necessitated by claim amendment.
Claim 1 recites, in the 4th limitation, “a configurable processor”. The limitation should each be amended to recite “a configurable processor of the at least one processor” to improve readability. Each successive recitation of “configurable processor” in claim 1 should be similarly amended. Claims 12 and 17 are similarly objected to.
Claim Rejections - 35 USC § 101
The outstanding rejections under 35 USC 101 are hereby withdrawn in view of further consideration of the instant claims as currently amended. The instant claims include steps that are in addition to the recited judicial exceptions that provide for integration of the recited exceptions into a practical application of those exceptions. Specifically, the steps directed to “configure, based on data representing a transcriptomic alignment model, a configurable processor to execute the transcriptomic alignment model” and “configure, based on data representing a genomic alignment model, the configurable processor to execute the genomic alignment model” in claims 1, 12, and 17 are additional elements that integrate the judicial exceptions present in the claims into a practical application at Step 2A, Prong 2. The configuring steps at least result in a particular machine at Step 2A, Prong 2, which performs the judicial exceptions of “aligning” the transcriptomics reads with a reference genome and the genomic reads with the reference genome. The specification as published defines a “configurable processor” to be a “circuit or chip that can be configured or customized to perform a specific application” which does “not include a CPU or GPU” [0041]. Therefore, the claims do not read on a general purpose computer, but rather recite a particular configuration for performing the judicial exceptions.
Applicant’s arguments at p. 20-22, section 3, regarding an improvement at Step 2A, Prong 2, of the improvement in computer-processing speed due to the separate configurations of the configurable processor for aligning each of the different types of data are also convincing.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maheshwari et al. (US 2022/0076780; previously cited; corresponds to WO2022051528 cited on the May 9 2024 IDS) in view of Van Rooyen et al. (US 2017/0124254; newly cited). The instant rejection is newly stated and is necessitated by claim amendment.
Claim 1 discloses a system comprising: at least one processor; and a non-transitory computer readable medium comprising a multiomics executable file comprising instructions in a programming language that a computing device can directly execute. Claim 12 discloses a non-transitory computer-readable medium comprising a multiomics executable file comprising instructions in a programming language that a computing device can directly execute. Claim 17 discloses a computer-implemented method.
The prior art to Maheshwari discloses methods and systems may be provided for distinguishing cell populations from non-cell populations within a data set, the method comprising receiving a data set at least associated with a plurality of cells, wherein the data set comprises molecule counts of at least two genomic features for each cell (abstract). Maheshwari teaches a non-transitory computer-readable medium is provided for storing computer instructions that, when executed by a computer comprising a processor [0173], cause the computer to perform the method [0006]. Maheshwari does not teach “comprising a multiomics executable file comprising instructions in a programming language that a computing device can directly execute”; see below for teachings by Van Rooyen regarding this limitation.
Maheshwari, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
The steps performed by the system of claim 1, a computing device of claim 12, and the method of claim 17 comprise:
identifying, for a sample (and by executing a multiomics executable file comprising instructions in a programming language that a computing device can directly execute; only in claim 17), transcriptomic reads comprising a first set of cellular barcode sequences representing candidate cells and genomic reads comprising a second set of cellular barcode sequences representing candidate cells;
Maheshwari teaches receiving a data set at least associated with a plurality of cells, wherein the data set comprises molecule counts of at least two genomic features [0027] for each cell and identifying duplicate subsets of data points from the data set (claim 1). Maheshwari teaches gene expression data (i.e., transcriptomic reads; [0023]) processing comprising processing the barcodes [0045] in the single cell sequencing data set [0084-0092] and ATAC data (i.e., genomic reads; [0023; 0028]) processing comprising processing the barcodes [0045] in the single cell ATAC sequencing data [0093-0098].
Maheshwari teaches that the methods of the present teachings may be implemented as firmware and/or a software program and applications written in conventional programming languages such as C, C++, Python, etc., and that if implemented as firmware and/or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above [0181].
See below for teachings by Van Rooyen regarding the limitation “comprising a multiomics executable file comprising instructions in a programming language that a computing device can directly execute”.
configuring, (by executing the multiomics executable file and; only in claim 17) based on data representing a transcriptomic alignment model, a configurable processor to execute the transcriptomic alignment model;
aligning, utilizing the configurable processor configured to execute the genomic alignment model, the transcriptomic reads with a reference genome and the genomic reads with the reference genome;
configuring, (by executing the multiomics executable file and; only in claim 17) based on data representing a genomic alignment model, the configurable processor to execute the genomic alignment model;
aligning, utilizing the configurable processor configured to execute the genomic alignment model, the transcriptomic reads with a reference genome and the genomic reads with the reference genome;
Regarding the above “configuring” and “aligning” limitations, Maheshwari teaches that the methodologies may be implemented by various means, including for a hardware implementation, the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein (i.e., configurable processor). As Maheshwari teaches aligning the gene expression read sequences to a reference sequence [0087] and aligning ATAC read sequences to a reference sequence [0096] as part of their methodologies, it is considered that Maheshwari fairly teaches implementing those alignments on a configurable processor as instantly claimed.
However, Maheshwari does not teach utilizing a configurable processor configured to execute a transcriptomic alignment model and a genomic alignment model. See below for teachings by Van Rooyen regarding this limitation.
determining (by executing the multiomics executable file; claim 17 only) counts of aligned transcriptomic reads and counts of aligned genomic reads for target nucleotide sequences within the candidate cells;
Maheshwari teaches counting gene expression read sequences based on the UMI barcodes [0088-0090]. Maheshwari teaches counting barcodes of ATAC read sequences [0095].
selecting (by executing the multiomics executable file; claim 17 only) a subset of candidate cells corresponding to a subset of cellular barcode sequences based on the counts of aligned transcriptomic reads and the counts of aligned genomic reads for target nucleotide sequences within the candidate cells; and
Maheshwari teaches joint cell calling to associate a subset of barcodes observed in both the single cell gene expression library and the single cell ATAC library to the cells loaded from the sample [0100]. Maheshwari teaches that the record of mapped high-quality fragments that passed all the filters of the various embodiments disclosed in the steps above and were indicated as a are recorded [0102], where the number (i.e. counts) of fragments that overlap any peak regions, for each barcode, can be utilized to separate the signal from noise, i.e., to separate barcodes associated with cells from non-cell barcodes (i.e., select a subset of candidate cells) [0102-0103]. Maheshwari teaches that generating cell populations and non-cell populations for joint cell calling comprises receiving multi-omic data matrix comprising molecular counts of at least two genomic features for each cell [0120] which is output by gene expression data processing step and ATAC data processing step [0122] and identifying non-cell barcodes based on low or intermediate values [0124-0139; 0144-0148].
generating, for the sample (and by executing the multiomics executable file; claim 17 only), single-cell multiomics outputs for individual cells of the selected subset of candidate cells based on the counts of aligned transcriptomic reads and the counts of aligned genomic reads.
Maheshwari teaches performing ATAC + gene expression analysis after joint cell calling (FIG. 2). Maheshwari teaches performing feature linkage analysis for detecting correlations between pairs of genomic features, for example, between peaks and genes from single cell datasets [0117], or between the single cell gene expression library and the single cell ATAC library [0118], in order to examine the relationship between elevated or repressed (i.e., counts of aligned transcriptomic reads) gene expression and accessible enhancer regions (i.e., counts of aligned genomic reads) [0119].
Maheshwari does not teach a multiomics executable file comprising instructions in a programming language that a computing device can directly execute or the limitations directed to configuring and utilizing a configurable processor to execute separate transcriptomic and genomic alignment models, as in claims 1, 12, and 17.
However, the prior art to Van Rooyen discloses “A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline” (abstract). Van Rooyen teaches that the one or more integrated circuits may include a set of hardwired digital logic circuits that are configured for performing a secondary and/or tertiary processing analysis pipeline on the generated reads of genomic data [0033], where the hardwired digital logic circuits of the integrated circuit and/or associated interconnects may be configured so as to be able to receive the one or more reads of genomic data and one or more of the hardwired digital logic circuits may be arranged as a set of processing engines, such as where each processing engine is formed of a subset of the hardwired digital logic circuits, and is configured so as to perform one or more steps in the sequencing and/or analysis pipeline, such as on the plurality of reads of genomic data [0034]. Van Rooyen teaches that the one or more of the processing engines may include an alignment module [0035-0036]. Van Rooyen teaches that the one or more integrated circuit(s) may include a master controller so as to establish the wired configuration for each subset of the hardwired digital logic circuits, for instance, for performing the one or more of mapping, aligning, and/or sorting functions, which functions may be configured as one or more steps in a sequence analysis pipeline and/or may include the performance of one or more aspects of a sequencing and/or variant call function, where the integrated circuit may be configured as a field programmable array (FPGA), an application specific integrated circuit (ASIC) having hardwired digital logic circuits or as a structured application specific integrated circuit (Structured ASIC) having hardwired digital logic circuits (i.e., configurable processors) [0038; 0469; 0472]. Van Rooyen teaches that the an apparatus for executing one or more steps of a sequence analysis pipeline, such as on genetic data, is provided wherein the genetic data includes one or more of a genetic reference sequence(s), an index of the one or more genetic reference sequence(s), an index of one or more splice junctions, e.g., an annotated splice junction index or table, and/or a plurality of reads, such as of genetic data, e.g., DNA or RNA, where the set of hardwired digital logic circuits may further be in a wired configuration, so as to access the index of the one or more genetic reference sequences and/or annotative splice junctions, via one of the plurality of physical electrical interconnects, and to map the plurality of reads of DNA and/or RNA to one or more segments of the one or more genetic reference sequences, such as according to the index or indexes [0040]. Van Rooyen discusses the difference between mapping and aligning DNA and RNA sequencing reads [0043-0044; 0310-0317], and teaches that an aspect of the present disclosure overcomes these challenges, and therefore allows for the rapid and accurate whole-transcriptome RNA sequencing, mapping, aligning, and/or sorting by an RNA-capable mapper/aligner (i.e., a transcriptomic alignment module) configured to process RNA reads using indexes that include a table that allows for the ready lookup of various known or determined splice junctions employed by biological systems in transcribing RNA from DNA using a configuration of the hardwired digital logic circuits where an alignment module may be provided, wherein the alignment module is configured for accessing the one or more DNA (i.e., a genomic alignment module) and/or RNA reference sequences [0045; 0318-0395; 0416; 0418-0424; 0435-0441]. Van Rooyen teaches that the configurable processor may have a chip that is capable of being configurable, e.g., its programming may be changed, so as to be more adaptable in meeting a given user's needs with respect to performing the various genomic functions detailed herein, where ethe user can change and/or modify the algorithms employed dependent on the key parameters desired to be emphasized in the overall system, such as to give additional functionality or change out what was first presented on the chip, e.g., such as re-configuring the chip to employ a different algorithm [0466-0467; 0523]. Van Rooyen teaches a configuration manager driven by a parameter file, such that the configuration manager may be adapted so as to configure the various modules of the pipeline [0469; 0472], where a library of pre-existing, editable, configuration files (i.e., an executable file), such as files orientated to the typical user selected functioning of the hardware, such as with respect to a portion or whole genome analysis, is stored [0482-0483]. Van Rooyen teaches that the computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in machine language (i.e., an executable file comprising instructions in a programming language that a computing device can directly execute) [0651]. Van Rooyen teaches an embodiment where a single FPGA may be provided and configured for being at least partially reconfigured between performing both a mapping and an alignment operation [0585], or more generally where one or more of the FPGAs may be at least partially reconfigured, such as between performing pipeline tasks [0595]. Van Rooyen teaches that each chip may include all or a selection of the modules, including the alignment module [0472; 0486]. As the instant specification as published defines the term “configurable processor” to refer to a circuit or chip that can be configured or customized to perform a specific application [0041], it is considered that the configurable processor taught by Van Rooyen reads on the instantly claimed configurable processor.
Regarding claims 1, 12, and 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, Maheshwari and Van Rooyen because each reference discloses methods for multi-omics data analysis. As Van Rooyen teaches configurable processors that can be reconfigured to perform specific genomic data processing tasks, especially alignment [0585; 0595], and also teaches configuring processors to with a RNA-capable aligner in comparison to an aligner for DNA sequences [0045; 0318-0395; 0416; 0418-0424; 0435-0441], it would be obvious to one of ordinary skill in the art to configure and reconfigure the configurable processors of Van Rooyen to align RNA and DNA reads as instantly claimed. The motivation to configure a configurable processor to separately align transcriptomics and genomics data would have been to perform the algorithms in a less labor and/or processing intensive manner with a greater percentage accuracy, where the algorithm(s) has been optimized in accordance with the manner, e.g., software, hardware, or a combination of both, in which it is to be implemented, in order to process RNA-seq and DNA reads differently, as taught by Van Rooyen [0015; 0018; 0318]. Further, it would have been obvious to include the code to perform the method of Mahshwari in view of Van Rooyen in a computer program implemented in machine language, as taught by Van Rooyen [0651], because one could have combined the elements as taught by both known methods, and in that combination, each element merely would have performed the same function as it did separately; furthermore one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 13, and 18, Maheshwari in view of Van Rooyen teaches claims 1, 12, and 17 as described above. Claims 2, 13, and 18 further add that selecting the subset of candidate cells corresponding to the subset of cellular barcode sequences comprises: determining, for each target nucleotide sequence of the target nucleotide sequences within each candidate cell of the candidate cells, a first count of aligned transcriptomic reads and a second count of aligned genomic reads; and clustering, from the first set of cellular barcode sequences and the second set of cellular barcode sequences, cellular barcode sequences in a selected cluster of candidate cells and a non-selected cluster of candidate cells based on the first count of aligned transcriptomic reads and the second count of aligned genomic reads for each target nucleotide sequence within each candidate cell of the candidate cells.
Maheshwari teaches joint cell calling to associate a subset of barcodes observed in both the single cell gene expression library and the single cell ATAC library to the cells loaded from the sample [0100]. Maheshwari teaches that the record of mapped high-quality fragments that passed all the filters of the various embodiments disclosed in the steps above and were indicated as a fragment in the fragment file (e.g., the fragments.tsv file), are recorded [0102], where the number (i.e. counts) of fragments that overlap any peak regions, for each barcode, can be utilized to separate the signal from noise, i.e., to separate barcodes associated with cells from non-cell barcodes (i.e., select a subset of candidate cells) [0102-0103]. Maheshwari teaches that generating cell populations and non-cell populations for joint cell calling comprises receiving multi-omic data matrix comprising molecular counts of at least two genomic features for each cell [0120] which is output by gene expression data processing step (i.e. first count) and ATAC data processing step (i.e. second count) [0122] and identifying non-cell barcodes based on low or intermediate values [0124-0139; 0144-0148]. Maheshwari teaches that the data may be clustered to determining a threshold that separates cell data from non-cell data, for example, separates cell barcodes from non-cell barcodes, and that after a threshold is determined, the threshold may be applied to the deduplicated data to generate initial cell populations and non-cell populations [0129].
Regarding claims 3, 14, and 19, Maheshwari in view of Van Rooyen teaches claims 1-2, 12-13, and 17-18 as described above. Claims 3, 14, and 19 further add that determining the first count of aligned transcriptomic reads comprises determining, for each gene encoded by a nucleotide sequence within each candidate cell of the candidate cell, a count of unique molecular identifier (UMI) sequences corresponding to aligned genomic reads; and determining the second count of aligned genomic reads comprises determining, for each accessible transcriptomic region corresponding to a read-coverage peak within each candidate cell, a count of read fragments from aligned genomic reads.
Maheshwari teaches UMI counting per gene in the aligned gene expression data [0088-0090]. Maheshwari teaches for the ATAC data, determining the number of fragments (i.e., read-coverage) that overlap any peak regions for each barcode of the aligned ATAC reads [0102].
Regarding claims 4, 15, and 20, Maheshwari in view of Van Rooyen teaches claims 1-2, 12-13, and 17-18 as described above. Claims 4, 15, and 20 further add that clustering the cellular barcode sequences comprises clustering the cellular barcode sequences into the selected cluster of candidate cells and a non-selected cluster of candidate cells based on a first dimension for summed counts of aligned transcriptomic reads for each candidate cell of the candidate cells and a second dimension for summed counts of aligned genomic reads for each candidate cell of the candidate cells.
Maheshwari teaches UMI counting per gene in the aligned gene expression data (i.e., summed counts of aligned transcriptomic reads) [0088-0090]. Maheshwari teaches for the ATAC data, determining the number of fragments that overlap any peak regions for each barcode of the aligned ATAC reads (i.e., summed counts of aligned genomic reads) [0102]. Maheshwari teaches that generating cell populations and non-cell populations for joint cell calling comprises receiving multi-omic data matrix comprising molecular counts of at least two genomic features for each cell [0120] which is output by gene expression data processing step (i.e. first dimension) and ATAC data processing step (i.e. second dimension) [0122] and identifying non-cell barcodes based on low or intermediate values [0124-0139; 0144-0148]. Maheshwari teaches that the data may be clustered to determining a threshold that separates cell data from non-cell data, for example, separates cell barcodes from non-cell barcodes, and that after a threshold is determined, the threshold may be applied to the deduplicated data to generate initial cell populations and non-cell populations [0129].
Regarding claims 5 and 16, Maheshwari in view of Van Rooyen teaches claims 1 and 12 as described above. Claims 5 and 16 further add that the data representing the transcriptomic alignment model comprise a first bitstream encoding the transcriptomic alignment model, and the data representing the genomic alignment model comprise a second bitstream encoding the genomic alignment model, which Maheshwari does not teach.
However, Van Rooyen teaches that the ASIC, FPGA, or structure ASIC chips are associated with memory architectures [0632], that may be used for facilitating the performance of the various modules described herein, for instance, the aligner [0484]. Van Rooyen teaches that a circuit may have an input for receiving one or a plurality of the configurable structure protocols, e.g., from the memory, and may further be adapted for implementing the one or more structures on the integrated circuit in accordance with the configurable processing structure protocols (i.e., a transcriptomic and genomic alignment model) [0603]. Van Rooyen teaches the use of Dynamic RAMs (DRAMs) for system memory because they require only one transistor and capacitor per bit by representing a single bit of information as the presence or absence of charge on a capacitor [0622; 0633; 0636]. It is therefore considered that Van Rooyen fairly teaches storing configurable processing structure protocols in memory where each piece of information is a bit, which indicates that the entirety of the protocol/model would be in a bitstream as instantly claimed.
Regarding claim 6, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 6 further adds selecting the subset of candidate cells corresponding to the subset of cellular barcode sequences by: retaining, in random-access memory, the counts of aligned transcriptomic reads and the counts of aligned genomic reads determined for the target nucleotide sequences; and accessing, from the random-access memory, the counts of aligned transcriptomic reads and the counts of aligned genomic reads in selecting the subset of candidate cells corresponding to the subset of cellular barcode sequences.
Maheshwari teaches counting gene expression read sequences based on the UMI barcodes [0088-0090]. Maheshwari teaches counting barcodes of ATAC read sequences [0095]. Maheshwari teaches that the computer system can also include a memory, which can be a random-access memory, for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor [0173]. As Maheshwari teaches counting gene expression read sequences [0088-0090], counting barcodes of ATAC read sequences [0095], and generating cell populations and non-cell populations for joint cell calling comprises receiving multi-omic data matrix comprising molecular counts of at least two genomic features for each cell [0120] which is output by gene expression data processing step and ATAC data processing step [0122] and identifying non-cell barcodes based on low or intermediate values [0124-0139; 0144-0148], it is considered that Maheshwari fairly teaches storing temporary variables or intermediate information on random-access memory for use in processes of the method such as selecting the subset of candidate cells.
Alternatively, Van Rooyen teaches random access memory that may be used for facilitating the performance of the various modules described herein, for instance, the mapper, aligner, and/or sorter, by storing sorted reads, annotated reads, compressed reads, and/or variant calls [0484]. Van Rooyen also teaches accessing the aligned sequences from the memory [0486], which reads on retaining and accessing the counts of those aligned reads in a random-access memory as instantly claimed.
Regarding claim 7, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 7 further adds that the first set of cellular barcode sequences differs from the second set of cellular barcode sequences, and the first set of cellular barcode sequences and the second set of cellular barcode sequences correspond to a same set of candidate cells.
Maheshwari teaches methods such as GEM, “Gel bead-in-EMulsion”, where single cells are associated with a known identifier like a barcode [0059-0064], lysing the cells and barcoding the RNA molecules or fragments to label them [0065-0069]. Maheshwari teaches that each GEM has a ATAC DNA barcode oligonucleotide and a gene expression barcode oligonucleotide attached, and that although the ATAC DNA barcode oligonucleotide and the gene expression barcode oligonucleotide may be different, they are designed to have a known association, so each genomic feature receives a cell-associated barcode that may comprise a pair of barcode sequences (i.e., correspond to a same set of candidate cells) [0047].
Regarding claim 8, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 8 further adds that the transcriptomic reads comprise a sequence of complementary DNA synthesized from single-stranded ribonucleic acid (RNA) from the sample; and the genomic reads comprise a nucleotide sequence of genomic deoxyribonucleic acid (DNA) complementing a genomic sequence from the sample.
Maheshwari teaches reverse transcription of mRNA to cDNA after lysis and prior to sequencing [0066; 0068; 0076]. Maheshwari teaches that when a first nucleic acid strand binds to a second nucleic acid strand made up of nucleotides that are complementary to those in the first strand, the two strands bind to form a double strand, and that nucleic acid sequencing data is indicative of the order of the nucleotide bases in a molecule of DNA or RNA [0037]. Therefore, it is considered that Maheshwari fairly teaches a nucleotide sequence of genomic deoxyribonucleic acid (DNA) complementing a genomic sequence from the sample, as instantly claimed.
Regarding claim 9, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 9 further adds that the genomic reads comprise Assay for Transposase-Accessible Chromatin (ATAC) reads for the sample.
Maheshwari teaches a sequencing data analysis workflow for analyzing the single cell sequencing data for gene expression analysis and the single cell ATAC sequencing data for identifying genome-wide differential accessibility of gene regulatory elements [0080].
Regarding claim 10, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 10 further adds generating the single-cell multiomics outputs for individual cells by generating a joint cell-by-feature matrix comprising both single-cell counts of aligned transcriptomic reads and single-cell counts of aligned genomic reads for target nucleotide sequences organized by each candidate cell within the selected subset of candidate cells.
Maheshwari teaches generating a multi-omic data matrix comprising molecular counts of at least two genomic features, where two genomics features per single cell can be measured, wherein the first genomic feature is a gene (i.e., transcriptomic reads), and the second genomic feature is an open genomic region (i.e., genomic reads) (i.e., a joint cell-by-feature matrix) [0120-0121]. Maheshwari teaches that data matrix can be output by gene expression data processing step and ATAC data processing steps and can be input into joint cell calling step [0122] in order to deduplicate the data [0124-0128], and cluster the deduplicated data to separate the barcodes into an initial cell population and an initial non-cell population [0129-0135]. It is therefore considered that as the input into the joint cell calling step is a matrix which reads on the joint cell-by-feature matrix as instantly claimed, the output of the joint cell calling step would produce a similar matrix which would indicate data per cell in the identified cell population, as taught by Maheshwari [0129-0135].
Regarding claim 11, Maheshwari in view of Van Rooyen teaches claim 1 as described above. Claim 11 further adds generating the single-cell multiomics outputs for individual cells by: generating a first set of single-cell metrics indicating gene expression for each candidate cell of the selected subset of candidate cells based on the counts of aligned transcriptomic reads; and generating a second set of single-cell metrics indicating accessible genomic deoxyribonucleic acid (DNA) corresponding to open chromatin for each candidate cell of the selected subset of candidate cells based on the counts of aligned genomic reads.
Maheshwari teaches that the gene expression analysis can include a feature-barcode matrix that summarizes that gene expression counts per each cell that is generated from the output from the Cell Calling step that jointly analyzes gene expression and ATAC data (i.e., a first set of single-cell metrics) [0105]. Maheshwari teaches that for the ATAC analysis, a raw peak-barcode matrix can be generated first, which is a count matrix consisting of the counts of fragment ends within each peak region for each barcode, and that the raw matrix can then be filtered to consist only of cell barcodes by filtering out the non-cell barcodes from the raw peak-barcode matrix (i.e., a second set of single-cell metrics) [0110].
Response to Applicant Arguments
With respect to Applicant’s arguments under 35 USC 103, the arguments have been fully considered but are moot in view of the new grounds of rejection set forth above as necessitated by claim amendment herein.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANNA NICOLE SCHULTZHAUS whose telephone number is (571)272-0812. The examiner can normally be reached on Monday - Friday 8-4.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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