DETAILED ACTION
Notice of 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.
Status of the Claims
Claims 1-65, 67-80 and 97-104 are canceled.
Claims 66, 81-96 and 105-106 are pending.
Claims 66, 81-96 and 105-106 are rejected.
Priority
This application US 18/179,144 (03/06/2023) is a DIV of US Application No. 15/806,174 (11/07/2017) which claims benefit of US Application No. 62/419,194 (11/08/2016) and US Application No. 62/445,546 (01/12/2017), as reflected in the filing receipt mailed on 06/15/2023. The claims to the benefit of priority are acknowledged and the effective filing date of claims 66, 81-96 and 105-106 is 11/08/2016.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/12/2023 was considered.
Specification Objections
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in [0274]. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 95 and 105 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
The following recitations require but lack antecedent basis, rendering their claims indefinite because there is no previous recitations of the followings terms as written:
Claim 95, "the number of the target"
claim 105, "the number of targets"
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 66, 81-96 and 105-106 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 66 and 81-96); system (claim 105) and a CRM (claim 106); each of which falls within one of the categories of statutory subject matter. [Step 1: claims 66, 81-96 and 105-106: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims found to recite abstract ideas that can fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information). The identified grouping(s) for this instant claims are as follows:
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "determining a feature vector of each cell label of the plurality of barcoded targets, wherein the feature vector comprise numbers of molecular labels with distinct sequences associated with the each cell label" (independent claims 66 and 81);
• "determining a cluster for the each cell label of the plurality of barcoded targets based on the feature vector" (independent claims 66 and 81);
• "clustering the each cell label of the plurality of barcoded targets into the cluster based on a distance of the feature vector to the cluster in a feature vector space" (claim 82);
• "projecting the feature vector from a feature vector space into a lower dimensional space; and clustering the each cell label into the cluster based on a distance of the feature vector to the cluster in the lower dimensional space" (claim 83) and
• "for one or more of the plurality of targets:1) counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; and 2) estimating the number of the target based on the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (1)" (claim 95).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to projecting feature vectors and counting numbers requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at [0035] which discloses algorithms for projecting the determined feature vectors and clustering cell labels. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "identifying the each cell label of the plurality of barcoded targets as a signal cell label or a noise cell label based on a number of cell labels in the cluster and a cluster size threshold" (independent claims 66 and 81);
• "identifying each of the cell labels as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with each of the cell labels and an identification threshold" (claim 96) and
• "re-identifying at least one of the plurality of cell labels as a signal cell label identified as a noise cell label in (b) or re-identifying at least one of the cell label as a noise cell label identified as a signal cell label in (b)" claim 96).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of identifying and re-identifying cell labels based on data obtained.
Dependent claims 84-87 and 90-94 recite further steps that limit the judicial exceptions in independent claim 81 and, as such, also are directed to those abstract ideas. For example, claims 84-85 recite further details about the “projecting the feature vector from a feature vector space into a lower dimensional space”; and claims 86-87 and 90-94 recite further details about the “clustering the each cell label into the cluster based on the distance of the feature vector to the cluster in the lower dimensional space.”
[Step 2A Prong One: claims 66, 81-96 and 105-106: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using 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, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2).
Instant claims 66, 81, 96 and 105-106 recite additional elements that are not abstract ideas:
• "barcoding a plurality of targets in a plurality of cells using a plurality of barcodes to create a plurality of barcoded targets, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels, and wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels" (independent claim 66);
• "obtaining sequencing data of the plurality of barcoded targets" (independent claim 66);
• "obtaining sequencing data of a plurality of barcoded targets, wherein the plurality of barcoded targets is create from a plurality of targets in a plurality of cells that are barcoded using a plurality of barcodes, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels, and wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels" (independent claim 81);
• "obtaining sequencing data of a plurality of first targets of cells, wherein each first target is associated with a number of molecular labels with distinct sequences associated with each cell label of a plurality of cell labels" (claim 96);
• "hardware processor; and non-transitory memory” (claim 105) and
• "computer readable medium” (claim 106).
Dependent claims 88-89 recite further details about the cell labels.
Considerations under Step 2A, Prong Two
The limitations recited in claims 66, 81, 96 and 105-106 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to “barcoding a plurality of targets in a plurality of cells using a plurality of barcodes to create a plurality of barcoded targets” read on data gathering activities, which does not provide a practical application (see MPEP 2106.05(g)).
Claims directed to "obtaining sequencing data" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 66, 81-96 and 105-106: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 66, 81, 96 and 105-106 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving is well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
With respect to the instant claims, the prior art review to Wang "Advances and applications of single-cell sequencing technologies." Molecular cell 58.4:598-609 (2015) - newly cited) discloses that using single-cell libraries where targets are barcoded and pooled together for multiplexed sequencing and appended with unique molecular indexes to reduce amplification bias is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 599 col. 2 para. 2 and p. 600, col. 2, par. 1.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 66, 81-96 and 105-106: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(l) the claimed invention was patented, described in a printed publication, or in public use, on sale,
or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 66, 81-87, 90, 92, 94-96 and 105-106 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Macosko ("Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets." Cell 161.5: 1202-1214 (2015)), as cited on the attached Form PTO-892.
Claim 66 recites:
(a) barcoding a plurality of targets in a plurality of cells using a plurality of barcodes to create a plurality of barcoded targets, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels, and wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s (i.e. cell barcode) RNA molecule (i.e. molecular label), and sequencing them all together (pg. 1202 col. 1 para. 1); wherein a cell barcode and an UMI barcode (i.e. molecular label) are disclosed in Fig. 1B (pg. 1203); wherein molecular barcoding of cellular transcriptomes in droplets generated a set of barcoded beads called single-cell transcriptomes attached to microparticles – STAMP – (i.e. each STAMP bead representing a single cell transcriptome– hence cell label) where each cell’s mRNA binds to the primer on a bead and said mRNA is reversed transcribed into cDNA where the cell’s cDNAs can be amplified in pools for high-throughput mRNA-seq to analyze any desired (i.e. target barcode) number of individual cells (i.e. (a) barcoding a plurality of targets in a plurality of cells using a plurality of barcodes to create a plurality of barcoded targets, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels) (pg. 1203 Fig. 1A); wherein the method identifies transcriptionally distinct cell populations along with molecular markers of each type (i.e. wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels) (pg. 1201 col. 2 para. 3).
(b) obtaining sequencing data of the plurality of barcoded targets
• Macosko teaches that the Drop-seq method consists of the following steps: (1) prepare a single-cell suspension from a tissue; (2) co-encapsulate each cell with a distinctly barcoded microparticle (bead) in a nanoliter- scale droplet; (3) lyse cells after they have been isolated in droplets; (4) capture a cell’s mRNAs on its companion microparticle, forming STAMPs (single-cell transcriptomes attached to microparticles); (5) reverse-transcribe, amplify, and sequence thousands of STAMPs in one reaction; and (6) use the STAMP barcodes to infer each transcript’s cell of origin (i.e. (b) obtaining sequencing data of the plurality of barcoded targets) (pg. 1203 col. 1 para. 1).
(c) determining a feature vector of each cell label of the plurality of barcoded targets, wherein the feature vector comprise numbers of molecular labels with distinct sequences associated with the each cell label
• Macosko teaches that for the analysis of cell-cycle state of 589 HEK cells, cells were assessed for their progression through the cell cycle by comparison of each cell’s global pattern of gene expression with gene sets known to be enriched in one of five phases of the cycle (pg. 1207 Fig. 4); wherein uniquely mapped reads were grouped by cell barcode and, to digitally count gene transcripts, a list of UMIs in each gene, within each cell, was assembled (i.e. a vector), and UMIs within ED = 1 were merged (i.e. cluster) together and the total number of distinct UMI sequences was counted and reported as the number of transcripts of that gene for a given cell (i.e. wherein the feature vector comprise numbers of molecular labels with distinct sequences associated with the each cell label) (p. 33, par. 3).
(d) determining a cluster for the each cell label of the plurality of barcoded targets based on the feature vector
• Macosko teaches computational approaches to first project cells onto a tSNE map based on its expression vector (Supplemental Information pg. S27 para. 2) prior to identifying and annotating clusters (i.e. (d) determining a cluster for the each cell label of the plurality of barcoded targets based on the feature vector) (Supplemental Information pg. S24 para. 3).
(e) identifying the each cell label of the plurality of barcoded targets as a signal cell label or a noise cell label based on a number of cell labels in the cluster and a cluster size threshold.
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s (i.e. cell barcode) RNA molecule (i.e. molecular barcode), and sequencing them all together (pg. 1202 col. 1 para. 1); wherein, to identify putative cell types on the tSNE map, a density clustering approach was performed to identify 49 clusters (i.e. signal cell label) with each cluster containing at least 50 cells – size threshold (Supplemental Information pg. S26 para. 2).
Claim 81 recites:
(a) obtaining sequencing data of a plurality of barcoded targets, wherein the plurality of barcoded targets is create from a plurality of targets in a plurality of cells that are barcoded using a plurality of barcodes, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels, and wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels
• Macosko teaches that the Drop-seq method consists of the following steps: (1) prepare a single-cell suspension from a tissue; (2) co-encapsulate each cell with a distinctly barcoded microparticle (bead) in a nanoliter- scale droplet; (3) lyse cells after they have been isolated in droplets; (4) capture a cell’s mRNAs on its companion microparticle, forming STAMPs (single-cell transcriptomes attached to microparticles); (5) reverse-transcribe, amplify, and sequence thousands of STAMPs in one reaction; and (6) use the STAMP barcodes to infer each transcript’s cell of origin (i.e. obtaining sequencing data of the plurality of barcoded targets) (pg. 1203 col. 1 para. 1); wherein molecular barcoding (i.e. molecular label) of cellular transcriptomes in droplets generated a set of barcoded beads called single-cell transcriptomes attached to microparticles – STAMP – (i.e. each STAMP bead representing a single cell transcriptome– hence cell label) where the cell’s cDNAs can be amplified in pools for high-throughput mRNA-seq to analyze any desired (i.e. target barcode) number of individual cells (i.e. wherein the plurality of barcoded targets is create from a plurality of targets in a plurality of cells that are barcoded using a plurality of barcodes, wherein each of the plurality of barcodes comprises a cell label and a molecular label, wherein barcoded targets created from targets of different cells of the plurality of cells have different cell labels, and wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels) (pg. 1203 Fig. 1A); wherein the method identifies transcriptionally distinct cell populations along with molecular markers of each type (i.e. wherein barcoded targets created from targets of the same cell of the plurality of cells have different molecular labels) (pg. 1201 col. 2 para. 3).
(b) determining a feature vector of each cell label of the plurality of barcoded targets, wherein the feature vector comprise numbers of molecular labels with distinct sequences associated with the each cell label
• Macosko teaches that for the analysis of cell-cycle state of 589 HEK cells, cells were assessed for their progression through the cell cycle by comparison of each cell’s global pattern of gene expression with gene sets known to be enriched in one of five phases of the cycle (pg. 1207 Fig. 4); wherein uniquely mapped reads were grouped by cell barcode and, to digitally count gene transcripts, a list of UMIs in each gene, within each cell, was assembled (i.e. a vector), and UMIs within ED = 1 were merged (i.e. cluster) together and the total number of distinct UMI sequences was counted and reported as the number of transcripts of that gene for a given cell (i.e. wherein the feature vector comprise numbers of molecular labels with distinct sequences associated with the each cell label) (p. 33, par. 3).
(c) determining a cluster for the each cell label of the plurality of barcoded targets based on the feature vector
• Macosko teaches computational approaches to project cells onto a tSNE map based on its expression vector (Supplemental Information pg. S27 para. 2) prior to identifying and annotating clusters (i.e. (c) determining a cluster for the each cell label of the plurality of barcoded targets based on the feature vector) (Supplemental Information pg. S24 para. 3).
(d) identifying the each cell label of the plurality of barcoded targets as a signal cell label or a noise cell label based on a number of cell labels in the cluster and a cluster size threshold
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s RNAs, and sequencing them all together (pg. 1202 col. 1 para. 1); wherein, to identify putative cell types on the tSNE map, a density clustering approach was performed to identify 49 clusters (i.e. signal cell label) with each cluster containing at least 50 cells – size threshold (Supplemental Information pg. S26 para. 2).
Claim 82 recites:
wherein determining the cluster for the each cell label of the plurality of barcoded targets based on the feature vector comprises clustering the each cell label of the plurality of barcoded targets into the cluster based on a distance of the feature vector to the cluster in a feature vector space
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s RNAs, and sequencing them all together (i.e. determining the cluster for the each cell label of the plurality of barcoded targets) (pg. 1202 col. 1 para. 1); wherein the edit distance relationships among UMIs was applied to collapse the sequences of the UMIs for each ERCC gene detected in each cell barcode at an edit distance of 1, including only substitutions or with both substitutions and insertions/deletions (S33 Table S1) (i.e. clustering the each cell label of the plurality of barcoded targets into the cluster based on a distance of the feature vector to the cluster in a feature vector space).
Claim 83 recites:
wherein determining the cluster for the each cell label of the plurality of barcoded targets based on the feature vector comprises: projecting the feature vector from a feature vector space into a lower dimensional space; and clustering the each cell label into the cluster based on a distance of the feature vector to the cluster in the lower dimensional space
• Macosko teaches performing unsupervised dimensionality reduction clustering analysis on the full dataset (i.e. projecting the feature vector from a feature vector space into a lower dimensional space) (Supplemental Information pg. S26 para. 2); wherein cells are projected onto a tSNE map based on its expression vector (i.e. feature vector) (Supplemental Information pg. S27 para. 2) and based on their representation within the PC-subspace (pg. 1212 col. 1 para. 5) prior to identifying and annotating clusters (Supplemental Information pg. S24 para. 3) in order to identify putative cell types on the tSNE map via a density clustering approach by setting a reachability distance parameter – eps parameter (Supplemental Information pg. S26 para. 2).
Claim 84 recites:
wherein the lower dimensional space is a two dimensional space cluster based on a distance of the feature vector to the cluster in a feature vector space
Claim 85 recites:
wherein projecting the feature vector from the feature vector space into the lower dimensional space comprises projecting the feature vector from the feature vector space into the lower dimensional space using a t-distributed stochastic neighbor embedding (tSNE) method
• Macosko teaches performing unsupervised dimensionality reduction clustering analysis on the full dataset (i.e. projecting the feature vector from a feature vector space into a lower dimensional space) (Supplemental Information pg. S26 para. 2); wherein cells are projected onto a tSNE map based on its expression vector (i.e. feature vector) (Supplemental Information pg. S27 para. 2) and based on their representation within the PC-subspace (pg. 1212 col. 1 para. 5) prior to identifying and annotating clusters (Supplemental Information pg. S24 para. 3); wherein cells with similar expression signatures of genes within our variable set, and therefore similar PC loadings, will likely localize near each other in the embedding returning a two-dimensional embedding of single cells (i.e. two dimensional space as in claim 84 and t-distributed stochastic neighbor embedding (tSNE) method as in claim 85) (Supplemental Information pg. S24 para. 2).
Claim 86 recites:
wherein clustering the each cell label into the cluster based on the distance of the feature vector to the cluster in the lower dimensional space comprises clustering the each cell label into the cluster based on the distance of the feature vector to the cluster in the lower dimensional space using a density-based method
Claim 87 recites:
wherein the density-based method comprises a density-based spatial clustering of applications with noise (DBSCAN) method
• Macosko teaches identifying putative cell types on the tSNE map via a density clustering approach implemented in the density-based spatial clustering of applications with noise DBSCAN R package (i.e. clustering the each cell label into the cluster based on the distance of the feature vector to the cluster in the lower dimensional space using a density-based method as in claim 86 and DBSCAN as in claim 87) by setting a reachability distance parameter – eps parameter (Supplemental Information pg. S26 para. 2).
Claim 90 recites:
comprising determining the cluster size threshold based on the number of cell labels of the plurality of barcoded targets
Claim 92 recites:
comprising determining the cluster size threshold based on the number of cell labels of the plurality of barcoded targets
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s RNAs (i.e. plurality of barcoded targets), and sequencing them all together (pg. 1202 col. 1 para. 1); wherein, to identify putative cell types on the tSNE map, a density clustering approach was performed via a two-step pruning strategy to avoid over partitioning of the data and suppress the co-option of outlier cells into a neighboring cluster, which identified 49 clusters involving 91% of the total data (i.e. signal cell label) with each cluster containing at least 50 cells (i.e. cluster size threshold based on the number of cell labels) (Supplemental Information pg. S26 para. 2).
Claim 94 recites:
comprising determining the cluster size threshold based on numbers of molecular labels with distinct sequences associated with each cell label of the plurality of barcoded targets
• Macosko teaches a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s RNAs (i.e. plurality of barcoded targets), and sequencing them all together (pg. 1202 col. 1 para. 1); wherein the edit distance relationships among UMIs was applied to collapse the sequences of the UMIs for each ERCC gene detected in each cell barcode at an edit distance of 1, including only substitutions or with both substitutions and insertions/deletions (S33 Table S1) (i.e. based on numbers of molecular labels with distinct sequences associated with each cell label of the plurality of barcoded targets).
Claim 95 recites:
comprising: (e) for one or more of the plurality of targets:1) counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; and 2) estimating the number of the target based on the number of molecular labels with distinct sequences associated with the target in the sequencing data counted in (1)
• Macosko teaches a method – Drop-seq – estimated to capture 10.7% of cellular mRNAs levels of ten genes from a culture of 50,000 HEK cells (i.e. estimating the number of the target based on the number of molecular labels with distinct sequences associated with the target in the sequencing data counted) by counting number of unique transcripts (i.e. counting the number of molecular labels with distinct sequences associated with the target in the sequencing data) (Supplemental Information pg. S4 pg. Figure S4A).
Claim 96 recites:
(a) obtaining sequencing data of a plurality of first targets of cells, wherein each first target is associated with a number of molecular labels with distinct sequences associated with each cell label of a plurality of cell labels;
(b) identifying each of the cell labels as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with each of the cell labels and an identification threshold; and
(c) re-identifying at least one of the plurality of cell labels as a signal cell label identified as a noise cell label in (b) or re-identifying at least one of the cell label as a noise cell label identified as a signal cell label in (b)
• Macosko teaches the identification of cell types by distinguishing molecular markers (pg. 1207 col. 1 para. 2); wherein sequencing of 49,300 barcoded beads from the retina tissue of mice (i.e. (a) obtaining sequencing data of a plurality of first targets of cells, wherein each first target is associated with a number of molecular labels with distinct sequences associated with each cell label of a plurality of cell labels) where principal component analysis of the first 13155 cells was performed followed by dimensionality reduction of 32 PCs to 2-dimensions using tSNE (pg. 1207 col. 1 para. 3) and clustering analysis of 39 distinct cluster no larger than 50 cells – hence identification threshold (pg. 1207 col. 2 para. 1) (i.e. (b) identifying each of the cell labels as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with each of the cell labels and an identification threshold); wherein the remaining the remaining 36,145 cells in the data into the tSNE and clustering analysis after the analysis of the first 13155 cells ((c) re-identifying at least one of the plurality of cell labels as a signal cell label identified as a noise cell label in (b) or re-identifying at least one of the cell label as a noise cell label identified as a signal cell label in (b)).
Claim 105 recites:
A computer system for determining the number of targets comprising: a hardware processor; and non-transitory memory having instructions stored thereon, which when executed by the hardware processor causes the processor to perform the method of claim 81
Claim 106 recites:
A computer readable medium comprising code for performing the method for performing the method of claim 81
• Macosko teaches the identification of cell types by distinguishing molecular markers (pg. 1207 col. 1 para. 2) via the unsupervised computational analysis (i.e., would inherently include a hardware processor; and non-transitory memory having instructions stored as in claim 105 and computer readable medium comprising code as in claim 106) of Drop-seq data to identify 39 transcriptionally distinct retinal cell populations, many representing specific subtypes of the major retinal cell classes (pg. 1211 col. 2 para. 2).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 88-89, 91 and 93 are rejected under 35 U.S.C. 103(a) as being unpatentable over Macosko as applied to claim 81 in the 102 rejection above further in view of Jiang ("GiniClust: detecting rare cell types from single-cell gene expression data with Gini index." Genome biology 17.1:144 (2016) Published 07/01/2016), as cited on the attached Form PTO-892.
Claim 88 recites:
wherein the cell label is identified as the signal cell label if the number of cell labels in the cluster is below the cluster size threshold
Claim 89 recites:
wherein the cell label is identified as a noise cell label if the number of cell labels in the cluster is not below the cluster size threshold
• Macosko does not teach the recitations above. However, Jiang teaches a method for detecting rare cell types from single-cell gene expression data (pg. 1 Title); wherein rare cell clusters were defined as those that consist of less than 5% of the total cell population (pg. 2 col. 2 para. 3). Here, it is interpreted that, in the context of rare cell types detection as the art teaches, cell labels/types from clusters more than 5% of the total cell population are considered noise/unwanted – as in claim 89 – and cell types/labels from clusters less than 5% of the total cell population are considered signal/kept data as in claim 88).
Claim 91 recites:
wherein the cluster size threshold is a percentage of the number of cell labels of the plurality of barcoded targets
Claim 93 recites:
wherein the cluster size threshold is a percentage of the number of cell labels of the plurality of barcoded targets
• Macosko teaches “the plurality of barcoded targets” as a method – Drop-seq - for profiling thousands of individual cells by separating them into nanoliter- sized aqueous droplets, associating a different barcode with each cell’s RNAs and sequencing them all together (pg. 1202 col. 1 para. 1).
• Macosko does not teach “wherein the cluster size threshold is a percentage of the number of cell labels”. However, Jiang teaches it as a method for detecting rare cell types from single-cell gene expression data (pg. 1 Title); wherein rare cell clusters were defined as those that consist of less than 5% of the total cell population (i.e. cluster size threshold is a percentage of the number of cell labels) (pg. 2 col. 2 para. 3).
Rationale for combining (MPEP §2142-2143)
Regarding claims 88-89, 91 and 93, 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, the methods of Macosko in view of Jiang because all references disclose methods for the characterization of individuals cells involving sequencing data and clustering techniques. The motivation would have been to incorporate a systematic cell types identification from single-cell gene expression data (pg. 1 col. 2 para. 2 Jiang) and uncover previously unrecognized cell types (pg. 1 Abstract Jiang)
Therefore it would have been obvious to one of ordinary skill in the art to substitute the characterization of individuals cells involving sequencing data and clustering techniques of Macosko to the methods by Jiang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the characterization of individuals cells involving sequencing data and clustering techniques.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
Prosecution Insights