DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-41, 45, 48, 50-51, 53-55, 58-59, 64-70, and 75-80 are cancelled.
Claim 83 is newly added.
Claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 are currently pending and examined on the merits.
Claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 are rejected.
Priority
The instant application claims priority to U.S. Provisional Application 63/210,294 filed on 14 June 2021. At this point in examination, the effective filing date of claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 is 14 June 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4 October 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Abstract
The abstract of the disclosure is objected to because line 4 recites "sbuject", which is a typographical error and should read as "subject". A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 62 recites the limitation "the plurality of long sequence reads" in line 2. There is insufficient antecedent basis for this limitation in the claim. The rejection might be overcome by amending the claim to introduce clear antecedent basis for “the plurality of long sequence reads”. For compact prosecution, it is assumed that the preceding suggested will be implemented.
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 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-82 are directed to a system (machine), comprising a non-transitory memory (machine). Claim 83 is directed to a method (process) for determining a variable number tandem repeat (VNTR) status. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 42, 44, 46, 52, and 83 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 42 further recites:
determining a variable number tandem repeat (VNTR) status (i.e., mental processes);
for each of the plurality of haplotypes of the VNTR, realigning short sequence reads, of the plurality of short sequence reads aligned to the VNTR, to the haplotype to generate a realignment (i.e., mental processes);
determining a probability of each of the plurality of haplotypes for the test subject using the realignment of the short sequence reads realigned to the haplotype (i.e., mental processes, mathematical concepts);
determining a status of the VNTR of the test subject (i.e., mental processes).
Dependent claim 44 further recites:
realigning the long sequence reads extracted to a left flanking region and a right flanking region of the VNTR to determine aligned long sequence reads (i.e., mental processes);
determining a haplotype of the plurality of haplotypes based on the aligned long sequence reads each with an alignment score above an alignment threshold (i.e., mental processes).
Dependent claim 46 further recites:
trimming sequences, of the aligned long sequence reads each with the alignment score above the alignment threshold, aligned to the left flanking region and the right flanking region to generate trimmed long sequence reads (i.e., mental processes);
determining the haplotype of the plurality of haplotypes based on the trimmed long sequence reads (i.e., mental processes).
Dependent claim 52 further recites:
determining a consensus sequence of the trimmed long sequence reads (i.e., mental processes).
Dependent claim 83 further recites:
determining a variable number tandem repeat (VNTR) status (i.e., mental processes);
determining a plurality of haplotypes of a VNTR using long sequence reads of the plurality of long sequence reads aligned to the VNTR in a reference (i.e., mental processes);
for each of the plurality of haplotypes of the VNTR, realigning short sequence reads, of the plurality of short sequence reads aligned to the VNTR, to the haplotype to generate a realignment (i.e., mental processes);
determining a probability indication of each of the plurality of haplotypes of the VNTR for the second subject using the realignment of the short sequence reads realigned to the haplotype (i.e., mental processes, mathematical concepts);
determining a status of the VNTR of the second subject based on the probability indications of each of the plurality of haplotypes (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 42, 44, 46, 52, and 83 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
Claims 46 and 52 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Claims 42, 44, and 83 recite the additional non-abstract elements of data gathering:
receiving a plurality of short sequence reads generated from a test sample obtained from a test subject (claim 42);
extracting the long sequence reads of the plurality of long sequence reads of the test sample aligned to the VNTR in the reference (claim 44);
receiving a plurality of long sequence reads generated from a plurality of first samples obtained from a plurality of first subjects (claim 83);
receiving a plurality of short sequence reads generated from a second sample obtained from a second subject (claim 83).
which are each a data gathering step, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 42 and 82-83 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a system for determining a variable number tandem repeat (VNTR) status (claim 42);
non-transitory memory configured to store executable instructions and a plurality of haplotypes of a VNTR (claim 42);
a hardware processor in communication with the non-transitory memory (claim 42);
wherein the hardware processor is programmed by the executable instructions to perform: generating a user interface (UI) comprising a UI element representing the status of the VNTR (claim 82);
under control of a hardware processor (claim 83).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 42, 44, and 82-83 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 42, 44, and 83: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 42 and 82-83: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 are patent ineligible under 35 U.S.C. § 101.
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.
Claims 42-44, 46-47, 49, 52, 56-57, 60-63, 71-74, and 81-83 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Genome Biology, 2019, 20(219), 1-13) as provided in the IDS filed 10/4/2022, in view of Bakhtiari et al. (Genome Research, 2018, 28(11), 1709-1719) as provided in the IDS filed 10/4/2022.
With respect to claim 42:
Claim 42 recites a system, a non-transitory memory configured to store executable instructions and a plurality of haplotypes of a VNTR, and a hardware processor.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
With respect to the recited receiving a plurality of short sequence reads generated from a test sample obtained from a test subject, Chen et al. discloses “To evaluate the performance of different methods, we genotyped the LRGT SVs on short-read data of NA12878 (63x), NA24385 (35x), and NA24631 (40x) using Paragraph and two widely used SV genotypers, SVTyper [16] and Delly Genotyper [17].” (Page 3, Section “Test for recall and precision”, col. 1, paragraph 1, lines 1-5). This suggests that short-read data was generated from a test sample obtained from a test subject.
With respect to the recited for each of the plurality of haplotypes of the VNTR, realigning short sequence reads, of the plurality of short sequence reads aligned to the VNTR, to the haplotype to generate a realignment, Chen et al. discloses “Here, we present a graph-based genotyper, Paragraph, that is capable of genotyping SVs in a large population of samples sequenced with short reads. The use of a graph for each variant makes it possible to systematically evaluate how reads align across breakpoints of the candidate variant.” (Page 2, col. 1, paragraph 2, lines 1-6). Also, further discloses “Most targeted methods for genotyping are integrated with particular discovery algorithms and require the input SVs to be originally discovered by the designated SV caller [15-17], require a complete genome-wide realignment [18, 19]” (Page 1, col. 2, paragraph 2, lines 2-7). Chen et al. discloses “To evaluate the performance of different methods, we genotyped the LRGT SVs on short-read data of NA12878 (63x), NA24385 (35x), and NA24631 (40x) using Paragraph” (Page 3, Section “Test for recall and precision”, col. 1, lines 1-4). This suggests that the Paragraph genotyper realigns short-read data to the haplotypes of the structural variants (SVs), or VNTRs, to generate a realignment.
With respect to the recited determining a probability of each of the plurality of haplotypes for the test subject using the realignment of the short sequence reads realigned to the haplotype, Chen et al. discloses “Only uniquely mapped reads, meaning reads aligned to only one graph location with the best alignment score, are used to genotype breakpoints.” (Page 9, Section “Graph alignment”, col. 2, paragraph 2, lines 1-3). Also, further discloses “A breakpoint occurs in the sequence graph when a node has more than one connected edges. Considering a breakpoint with a set of reads with a total read count
R
and two connecting edges representing haplotype
h
1
and
h
2
, we define the read count of haplotype
h
1
as
R
h
1
and haplotype
h
2
as
R
h
2
. The remaining reads in
R
that are mapped to neither haplotype are denoted as
R
≠
h
1
,
h
2
. The likelihood of observing the given set of reads with the underlying breakpoint genotype
G
h
1
/
h
2
can be represented as:
p
(
R
|
G
h
1
/
h
2
)
=
p
(
R
h
1
,
R
h
2
|
G
h
1
/
h
2
)
×
p
(
R
≠
h
1
,
h
2
|
G
h
1
/
h
2
)
” (Page 9, Section “Breakpoint genotyping”, col. 2, paragraphs 1-2, lines 1-11). This suggests that the short sequence reads realigned to haplotypes are used to determine a probability of observing the reads with the underlying genotypes, or plurality of haplotypes.
Chen et al. does not disclose determining a variable number tandem repeat (VNTR) status.
However, Bakhtiari et al. discloses “In contrast to methods like VNTRseek that seek to discover/identify VNTRs, we describe a method, adVNTR, for genotyping VNTRs at targeted loci in a donor genome. For any target VNTR in a donor, adVNTR reports an estimate of RU counts and point mutations within the RUs.” (Page 1710, col. 2, paragraph 1, lines 1-5). This suggests determining VNTR status or the state of the VNTR, which includes repeat unit (RU) counts and point mutations.
Chen et al. does not disclose determining a status of the VNTR of the test subject.
However, Bakhtiari et al. discloses “In contrast to methods like VNTRseek that seek to discover/identify VNTRs, we describe a method, adVNTR, for genotyping VNTRs at targeted loci in a donor genome. For any target VNTR in a donor, adVNTR reports an estimate of RU counts and point mutations within the RUs.” (Page 1710, col. 2, paragraph 1, lines 1-5). This suggests determining a status of the VNTR of a test subject.
It would have been prima facie obvious to one of ordinary skill in the art to modify the teachings disclosed by Chen et al. to incorporate the VNTR status disclosed by Bakhtiari et al. One would be motivated to make this modification because adVNTR performance was tested on PacBioSim by comparing against a naïve method that estimates RU counts based on read length between the flanking regions from the consensus of reads that cover VNTR, which showed high genotype accuracy for adVNTR over a wide range of RU counts and coverage (Page 1711, Section “VNTR genotyping (RU count estimation) with PacBio reads”, col. 2, paragraph 1, lines 3-8). There is a likelihood of success, since both teachings are of genotypers that genotype structural variants and are well known bioinformatics tools in the art of computational genomics.
With respect to claim 43:
With respect to the recited wherein the plurality of haplotypes of the VNTR is determined using long sequence reads of a plurality of long sequence reads aligned to the VNTR in a reference, and wherein the plurality of long sequence reads is generated from a plurality of reference samples obtained from a plurality of reference subjects, Chen et al. discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005).” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-6). This describes long-read ground truths (LRGTs) as long sequence reads generated from three reference samples obtained from a plurality of reference subjects. Haplotypes of the SVs are determined using these long sequence reads aligned to the SVs in reference samples.
With respect to claim 44:
With respect to the recited wherein the plurality of haplotypes of the VNTR is determined, Chen et al. discloses “For each SV defined in an input VCF file, Paragraph constructs a directed acyclic graph containing paths representing the reference sequence and possible alternative alleles (Fig. 1) for each region where a variant is reported. Each node represents a sequence that is at least one nucleotide long. Directed edges define how the node sequences can be connected to form complete haplotypes.” (Page 2, Section “Graph-based genotyping of structural variations”, col. 1-2, paragraph 1, lines 1-8). This suggests constructing directed acyclic graphs that represent plurality of haplotypes of the VNTR.
With respect to the recited for each of the plurality of samples, Chen et al. discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005).” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-6). This suggests a plurality of samples.
With respect to the recited extracting the long sequence reads of the plurality of long sequence reads of the test sample aligned to the VNTR in the reference, Chen et al. discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005).” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-6). Also, further discloses “Paragraph extracts reads, as well as their mates (for paired-end reads), from the flanking region of each targeted SV in a Binary Alignment Map (BAM) or CRAM file.” (Page 9, Section “Graph alignment”, col. 2, paragraph 1, lines 1-4). This suggests that the Paragraph genotyper extracts long sequence reads from the test samples aligned to the SVs in the references.
With respect to the recited realigning the long sequence reads extracted to a left flanking region and a right flanking region of the VNTR to determine aligned long sequence reads, Chen et al. discloses “Paragraph extracts reads, as well as their mates (for paired-end reads), from the flanking region of each targeted SV in a Binary Alignment Map (BAM) or CRAM file. The default target region is one read length upstream of the variant starting position to one read length downstream of the variant ending position, although this can be adjusted at runtime. The extracted reads are realigned to the pre-constructed sequence graph using a graph-aware version of a Farrar’s Striped Smith-Waterman alignment algorithm implemented in GSSW library” (Page 9, Section “Graph alignment”, col. 2, paragraph 1, lines 1-11). Also, further discloses “Only uniquely mapped reads, meaning reads aligned to only one graph location with the best alignment score, are used to genotype breakpoints.” (Page 9, Section “Graph alignment”, col. 2, paragraph 2, lines 1-3). A left flanking region is one read length upstream of the variant starting position and a right flanking region is one read length downstream of the variant ending position. Therefore, this suggests the realignment of long sequence reads extracted to a left flanking region and a right flanking region of the targeted SV to determine aligned long sequence reads, which are reads with the best alignment score used to genotype breakpoints.
Chen et al. does not disclose determining a haplotype of the plurality of haplotypes based on the aligned long sequence reads each with an alignment score above an alignment threshold.
However, Bakhtiari et al. discloses “The first step in adVNTR is to recruit all reads that match a portion of the VNTR sequence. Alignment-based methods do not work well due to changes in RU counts (Results), but the adVNTR HMM allows for variable RU count.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 1, lines 1-4). Also, further discloses “Filtered reads were aligned to the HMM using the Viterbi algorithm. Only reads with matching probability higher than a specified threshold were retained.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 2, lines 1-3). Bakhtiari et al. also discloses “Genotyping VNTRs in a donor genome sequenced using short (Illumina) or longer single-molecule reads, requires the following: (1) recruitment of reads containing the VNTR sequence; (2) counting RUs for each of the two haplotypes” (Page 1709, col. 2, paragraph 2, lines 1-4). This suggests determining a haplotype of plurality of haplotypes by filtering reads that align with the VNTR sequence with an alignment probability score above an alignment threshold and using those reads to count repeat units for the haplotypes.
With respect to claim 46:
With respect to the recited wherein the haplotype of the plurality of haplotypes of the VNTR is determined, Chen et al. discloses “For each SV defined in an input VCF file, Paragraph constructs a directed acyclic graph containing paths representing the reference sequence and possible alternative alleles (Fig. 1) for each region where a variant is reported. Each node represents a sequence that is at least one nucleotide long. Directed edges define how the node sequences can be connected to form complete haplotypes.” (Page 2, Section “Graph-based genotyping of structural variations”, col. 1-2, paragraph 1, lines 1-8). This describes that a haplotype of a plurality of haplotypes is represented in a directed acyclic graph.
Chen et al. does not disclose trimming sequences, of the aligned long sequence reads each with the alignment score above the alignment threshold, aligned to the left flanking region and the right flanking region to generate trimmed long sequence reads.
However, Bakhtiari et al. discloses “The first step in adVNTR is to recruit all reads that match a portion of the VNTR sequence. Alignment-based methods do not work well due to changes in RU counts (Results), but the adVNTR HMM allows for variable RU count.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 1, lines 1-4). Also, further discloses “Filtered reads were aligned to the HMM using the Viterbi algorithm. Only reads with matching probability higher than a specified threshold were retained.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 2, lines 1-3). Bakhtiari et al. also discloses “We use an HMM architecture with three parts, which have their own three groups of states (Fig. 5). The first part matches the 5’ (left) flanking region of the VNTR. The second part is an HMM that matches an arbitrary number of (approximately identical) repeating units. The last part matches the 3’ (right) flanking region (Supplemental Fig. S1).” (Page 1716, col. 1, paragraph 1, lines 4-9). Aligned long sequence reads are first recruited and then selected based on alignment scores above the alignment probability threshold. This suggests that the aligned long sequence reads are trimmed in an HMM, where the trimmed sequences are aligned to a left flanking region and a right flanking region.
Chen et al. does not disclose determining the haplotype of the plurality of haplotypes based on the trimmed long sequence reads.
However, Bakhtiari et al. discloses “The first step in adVNTR is to recruit all reads that match a portion of the VNTR sequence. Alignment-based methods do not work well due to changes in RU counts (Results), but the adVNTR HMM allows for variable RU count.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 1, lines 1-4). Also, further discloses “Filtered reads were aligned to the HMM using the Viterbi algorithm. Only reads with matching probability higher than a specified threshold were retained.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 2, lines 1-3). Bakhtiari et al. also discloses “Genotyping VNTRs in a donor genome sequenced using short (Illumina) or longer single-molecule reads, requires the following: (1) recruitment of reads containing the VNTR sequence; (2) counting RUs for each of the two haplotypes” (Page 1709, col. 2, paragraph 2, lines 1-4). Aligned long sequence reads are first recruited and then selected based on alignment scores above the alignment probability threshold. This suggests that the trimmed long sequence reads are modeled using HMMs and used to determine haplotypes by counting repeat units for the haplotypes.
With respect to claim 47:
Chen et al. does not disclose wherein the reference sample is homozygous for the VNTR, and the haplotype of the plurality of haplotypes of the VNTR is determined to comprise only one haplotype of the plurality of haplotypes based on the trimmed long sequence reads.
However, Bakhtiari et al. discloses “To test adVNTR performance on PacBioSim, we compared against a naïve method that estimates RU counts based on read length between the flanking regions from the consensus of reads that cover VNTR. Detailed performance on three exemplars (genes INS, CSTB, and HIC1) showed high genotype accuracy for adVNTR over a wide range of RU counts and coverage (Fig. 2A). Similar results were obtained for all 2944 VNTRs (Fig. 2B). Overall, 98.45% of adVNTR estimates were correct, whereas 26.45% of estimates made by the naïve method were correct. Because it is difficult for the naïve method to call heterozygotes, we also compared the subset of test data with homozygous RU counts: 97.95% of adVNTR estimates were correct, whereas the consensus method was correct in 66.16% of samples” (Page 1711, Section “VNTR genotyping (RU count estimation) with PacBio reads”, col. 2, paragraph 1, lines 3-15). This implies the reference sample is homozygous because the adVNTR performance was compared with homozygous RU counts. The accuracy of adVNTR estimates when comparing with homozygous RU counts suggests that the haplotype of the VNTR is determined to comprise one haplotype based on the trimmed long sequence reads from the read recruitment step of the genotyper.
With respect to claim 49:
Chen et al. does not disclose wherein the reference sample is heterozygous for the VNTR and wherein the haplotype of the plurality of haplotypes of the VNTR is determined to comprise two haplotypes of the plurality of haplotypes based on the trimmed long sequence reads.
However, Bakhtiari et al. discloses “We also performed a long range (LR) PCR experiment on the individual NA12878 to assess the accuracy of the adVNTR genotypes using PacBio data (Supplemental Tables S2, S3). The observed PCR product lengths (black bands in Fig. 2D) were consistent with the adVNTR predictions (red arrows), while being different from the hg19 reference RU count. adVNTR correctly predicted all VNTRs to be heterozygous with the exception of SLC6A4, which was predicted to be homozygous.” (Page 1713, col. 1, paragraph 2, lines 1-8). This implies the reference sample is heterozygous because adVNTR predicted all VNTRs in individual NA12878 to be heterozygous. The consistently correct predictions made by adVNTR suggests that the haplotype of the VNTR is determined to comprise two haplotypes based on the trimmed long sequence reads from the read recruitment step of the genotyper.
With respect to claim 52:
With respect to the recited the haplotype of the plurality of haplotypes of the VNTR is determined by: determining a consensus sequence of the trimmed long sequence reads, Chen et al. discloses “An alternative approach to build up a reference SV catalog would be to sequence many samples (possibly at lower depth) using PacBio contiguous long reads (CLR) or Oxford Nanopore long reads rather than CCS technology and derive consensus calls across multiple samples.” (Page 5, Section “Genotyping with breakpoint deviations”, col. 1, paragraph 1, lines 4-9). This suggests building a reference SV catalog that determines haplotypes of structural variants such as VNTRs by deriving consensus sequences of trimmed long sequence samples.
With respect to claim 56:
With respect to the recited wherein qualities of the long sequence reads of the plurality of long sequence reads aligned to the VNTR in the reference and/or qualities of the plurality of haplotypes satisfy quality criteria, Chen et al. discloses “In a sequence graph, each node represents a sequence that is at least one nucleotide long and directed edges define how the node sequences can be connected together to form complete haplotypes.” (Page 9, Section “Graph construction”, col. 1, paragraph 1, lines 1-4). Also, further discloses “A breakpoint occurs in the sequence graph when a node has more than one connected edges.” (Page 9, Section “Breakpoint genotyping”, paragraph 1, lines 1-2). Chen et al. also discloses “We perform a series of tests for the confidence of breakpoint genotypes. For a breakpoint to be labeled as “passing,” it must meet all of the following criteria: … 3. The Phred-scaled score of its genotyping quality (derived from genotype likelihoods) is at least 10.” (Page 10, Section “SV genotyping”, col. 1, paragraph 1, lines 1-10). A node with more than one connected edges demonstrates a long sequence read. Therefore, long sequence reads aligned to the VNTR represented as breakpoints in a sequence graph are assessed for their qualities and whether or not they satisfy the quality score of at least 10.
With respect to claim 57:
Chen et al. does not disclose wherein the status of the VNTR comprises a haplotype status of the VNTR and/or a genotype status of the VNTR, optionally wherein the haplotype status comprises a haplotype, a length of the haplotype, and a confidence interval of the length of the haplotype, and optionally wherein the genotype status comprises a genotype, lengths of the haplotypes of the genotypes, and a confidence interval of the length of each of the haplotypes of the genotype.
However, Bakhtiari et al. discloses “In contrast to methods like VNTRseek that seek to discover/identify VNTRs, we describe a method, adVNTR, for genotyping VNTRs at targeted loci in a donor genome. For any target VNTR in a donor, adVNTR reports an estimate of RU counts and point mutations within the RUs. It trains hidden Markov models (HMMs) for each target VNTR locus, which provide the following advantages: (1) It is sufficient to match any portions of the unique flanking regions for read alignment; (2) it is easier to separate homopolymer runs from other indels helping with frameshift detection, and to estimate RU counts even in the presence of indels; and (3) each VNTR can be modeled individually, and complex models can be constructed for VNTRs with complex structure, along with VNTR specific confidence scores.” (Page 1710, col. 2, paragraph 1, lines 1-13). Also, further discloses “In particular, tandem repeats correspond to locations where a short DNA sequence or Repeat Unit (RU) is repeated in tandem multiple times. RUs of length less than 6 bp are classified as short tandem repeats (STRs), whereas longer RUs spanning potentially hundreds of nucleotides are denoted as Variable Number Tandem Repeats (VNTRs).” (Page 1709, col. 1, paragraph 1, lines 9-14). Genotypes and haplotypes are represented within VNTRs as RU counts, each having a particular length. Each VNTR is modeled individually along with VNTR specific confidence scores, which includes confidence intervals of the lengths of haplotypes and the lengths of haplotypes of genotypes. Therefore, the status of the VNTR comprises a haplotype status and a genotype status, each comprising their respective haplotypes/genotypes, haplotype lengths, and confidence intervals of the lengths of haplotypes.
With respect to claim 60:
Chen et al. does not disclose wherein the probability indication of each of the plurality of haplotypes of the VNTR comprises a probability of each of the plurality of haplotypes of the VNTR, and wherein the probability criterium comprises a probability threshold.
However, Bakhtiari et al. discloses “The first step in adVNTR is to recruit all reads that match a portion of the VNTR sequence. Alignment-based methods do not work well due to changes in RU counts (Results), but the adVNTR HMM allows for variable RU count.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 1, lines 1-4). Also, further discloses “Filtered reads were aligned to the HMM using the Viterbi algorithm. Only reads with matching probability higher than a specified threshold were retained.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 2, lines 1-3). Bakhtiari et al. also discloses “Genotyping VNTRs in a donor genome sequenced using short (Illumina) or longer single-molecule reads, requires the following: (1) recruitment of reads containing the VNTR sequence; (2) counting RUs for each of the two haplotypes” (Page 1709, col. 2, paragraph 2, lines 1-4). This suggests a matching probability indication of the haplotypes of the VNTRs and a probability threshold that must be overcome for the haplotypes to be retained after the read recruitment step of adVNTR.
With respect to claim 61:
Chen et al. does not disclose wherein an accuracy of the haplotype status is at least 60%.
However, Bakhtiari et al. discloses “To test for accuracy with changing RU counts, we simulated different RU counts for individuals at three VNTRs (Supplemental Table S$). adVNTR RU counts showed 100% accuracy in each of the 52 different samples tested.” (Page 1711, Section “VNTR genotyping (RU count estimation) with PacBio reads”, col. 2, paragraph 1, lines 17-20). This suggests a haplotype status with an accuracy of 100%, which is at least 60%.
With respect to claim 62:
With respect to the recited wherein the plurality of long sequence reads comprises sequence reads that are about 10,000 base pairs to about 20,000 base pairs in length each, Chen et al. discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005). Long-read data from these three individuals was generated on a Pacific Biosciences (PacBio) Sequel system using the Circular Consensus Sequencing (CCS) technology (sometimes called “HiFi” reads) [30]. Each sample was sequenced to an average of 30 fold depth and ~11,100 bp read length.” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-11). This describes long sequence reads that are approximately 11,100 base pairs in length, which is within the range of about 10,000 base pairs to about 20,000 base pairs.
With respect to claim 63:
With respect to the recited wherein the plurality of short sequence reads comprises sequence reads that are about 100 base pairs to about 1000 base pairs in length each, Chen et al. discloses “This is especially problematic for a large number of SVs that are longer than the read lengths of short-read (100-150 bp) high-throughput sequence data.” (Page 1, Section “Background”, col. 1, paragraph 1, lines 7-9). This describes short sequence reads that are 100-150 base pairs in length, which is within the range of about 100 base pairs to about 1000 base pairs.
With respect to claim 71:
Chen et al. does not disclose wherein each haplotype of the plurality of haplotypes of the VNTR comprises a plurality of copies of a repeat unit.
However, Bakhtiari et al. discloses “In particular, tandem repeats correspond to locations where a short DNA sequence or Repeat Unit (RU) is repeated in tandem multiple times. RUs of length less than 6 bp are classified as short tandem repeats (STRs), whereas longer RUs spanning potentially hundreds of nucleotides are denoted as Variable Number Tandem Repeats (VNTRs).” (Page 1709, col. 1, paragraph 1, lines 9-14). This suggests that VNTRs are comprised of haplotypes represented as DNA sequences or repeat units.
With respect to claim 72:
Chen et al. does not disclose wherein the repeat unit is more than six base pairs in length.
However, Bakhtiari et al. discloses “In particular, tandem repeats correspond to locations where a short DNA sequence or Repeat Unit (RU) is repeated in tandem multiple times. RUs of length less than 6 bp are classified as short tandem repeats (STRs), whereas longer RUs spanning potentially hundreds of nucleotides are denoted as Variable Number Tandem Repeats (VNTRs).” (Page 1709, col. 1, paragraph 1, lines 9-14). Repeat units that are longer than 6 base pairs in length are considered VNTRs.
With respect to claim 73:
Chen et al. does not disclose wherein the number of the plurality of copies is at least three.
However, Bakhtiari et al. discloses “In particular, tandem repeats correspond to locations where a short DNA sequence or Repeat Unit (RU) is repeated in tandem multiple times. RUs of length less than 6 bp are classified as short tandem repeats (STRs), whereas longer RUs spanning potentially hundreds of nucleotides are denoted as Variable Number Tandem Repeats (VNTRs).” (Page 1709, col. 1, paragraph 1, lines 9-14). This suggests that the copies of repeat units are repeated in tandem multiple times. Therefore, the number of copies is at least three.
With respect to claim 74:
Chen et al. does not disclose wherein sequences of two copies of the plurality of copies of the repeat unit of a haplotype of the plurality of haplotypes are different at one or more differentiating positions.
However, Bakhtiari et al. discloses “In particular, tandem repeats correspond to locations where a short DNA sequence or Repeat Unit (RU) is repeated in tandem multiple times. RUs of length less than 6 bp are classified as short tandem repeats (STRs), whereas longer RUs spanning potentially hundreds of nucleotides are denoted as Variable Number Tandem Repeats (VNTRs).” (Page 1709, col. 1, paragraph 1, lines 9-14). Also, further discloses “reads contained within the VNTR sequence have multiple equally likely mappings and therefore will be mapped randomly to different locations with low mapping quality” (Page 1710, col. 1, paragraph 1, lines 2-5). This suggests that the sequences of the two repeat units of a haplotype can be different at any location or differentiating positions because they have multiple likely mappings.
With respect to claim 81:
Chen et al. does not disclose wherein a haplotype of the plurality of haplotypes of the VNTR is associated with a disease.
However, Bakhtiari et al. discloses “multiple studies have linked variation in VNTRs with Mendelian diseases, for example, Medullary cystic kidney disease (Kirby et al. 2013), Myoclonus epilepsy (Lalioti et al. 1997), FSHD (Lemmers et al. 2002), and complex disorders such as bipolar disorder (Table 1).” (Page 1709, col. 1, paragraph 2, lines 9-13). This suggests that haplotypes in VNTRs are associated with a disease.
With respect to claim 82:
Claim 82 recites wherein the hardware processor is programmed by the executable instructions to perform: generating a user interface (UI) comprising a UI element representing the status of the VNTR. This is considered an aesthetic design change as the processor is generating a user interface that displays the UI element representing VNTR status and nothing more. See MPEP 2144.04 (I).
With respect to claim 83:
Claim 83 recites under control of a hardware processor.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
With respect to the recited receiving a plurality of long sequence reads generated from a plurality of first samples obtained from a plurality of first subjects, Chen et al. discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005).” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-6). This describes long-read ground truths (LRGTs) as long sequence reads generated from three first samples obtained from a plurality of first subjects.
With respect to the recited determining a plurality of haplotypes of a VNTR using long sequence reads of the plurality of long sequence reads aligned to the VNTR in a reference, Chen et al. discloses “For each SV defined in an input VCF file, Paragraph constructs a directed acyclic graph containing paths representing the reference sequence and possible alternative alleles (Fig. 1) for each region where a variant is reported. Each node represents a sequence that is at least one nucleotide long. Directed edges define how the node sequences can be connected to form complete haplotypes.” (Page 2, Section “Graph-based genotyping of structural variations”, col. 1-2, paragraph 1, lines 1-8). Also, further discloses “To estimate the performance of Paragraph and other existing methods, we built a long-read ground truth (LRGT) from SVs called in three samples included in the Genome in a Bottle (GIAB) [11, 29] project data: NA12878 (HG001), NA24385 (HG002), and NA24631 (HG005).” (Page 2, Section “Construction of a long read-based ground truth”, col. 2, paragraph 1, lines 1-6). Haplotypes of a VNTR are determined using directed acyclic graphs containing paths representing the reference sequence, which includes long sequence reads aligned to the VNTR in the reference. Long-read ground truths (LRGTs) are the long sequence reads generated from three reference samples, which are used for the directed acyclic graphs.
With respect to the recited receiving a plurality of short sequence reads generated from a second sample obtained from a second subject, Chen et al. discloses “To evaluate the performance of different methods, we genotyped the LRGT SVs on short-read data of NA12878 (63x), NA24385 (35x), and NA24631 (40x) using Paragraph and two widely used SV genotypers, SVTyper [16] and Delly Genotyper [17].” (Page 3, Section “Test for recall and precision”, col. 1, paragraph 1, lines 1-5). This suggests that short-read data was generated from a second sample obtained from a second subject.
With respect to the recited for each of the plurality of haplotypes of the VNTR, realigning short sequence reads, of the plurality of short sequence reads aligned to the VNTR, to the haplotype to generate a realignment, Chen et al. discloses “Here, we present a graph-based genotyper, Paragraph, that is capable of genotyping SVs in a large population of samples sequenced with short reads. The use of a graph for each variant makes it possible to systematically evaluate how reads align across breakpoints of the candidate variant.” (Page 2, col. 1, paragraph 2, lines 1-6). Also, further discloses “Most targeted methods for genotyping are integrated with particular discovery algorithms and require the input SVs to be originally discovered by the designated SV caller [15-17], require a complete genome-wide realignment [18, 19]” (Page 1, col. 2, paragraph 2, lines 2-7). Chen et al. discloses “To evaluate the performance of different methods, we genotyped the LRGT SVs on short-read data of NA12878 (63x), NA24385 (35x), and NA24631 (40x) using Paragraph” (Page 3, Section “Test for recall and precision”, col. 1, lines 1-4). This suggests that the Paragraph genotyper realigns short-read data to the haplotypes of the structural variants (SVs), or VNTRs, to generate a realignment.
With respect to the recited determining a probability indication of each of the plurality of haplotypes of the VNTR for the second subject using the realignment of the short sequence reads realigned to the haplotype, Chen et al. discloses “Only uniquely mapped reads, meaning reads aligned to only one graph location with the best alignment score, are used to genotype breakpoints.” (Page 9, Section “Graph alignment”, col. 2, paragraph 2, lines 1-3). Also, further discloses “A breakpoint occurs in the sequence graph when a node has more than one connected edges. Considering a breakpoint with a set of reads with a total read count
R
and two connecting edges representing haplotype
h
1
and
h
2
, we define the read count of haplotype
h
1
as
R
h
1
and haplotype
h
2
as
R
h
2
. The remaining reads in
R
that are mapped to neither haplotype are denoted as
R
≠
h
1
,
h
2
. The likelihood of observing the given set of reads with the underlying breakpoint genotype
G
h
1
/
h
2
can be represented as:
p
(
R
|
G
h
1
/
h
2
)
=
p
(
R
h
1
,
R
h
2
|
G
h
1
/
h
2
)
×
p
(
R
≠
h
1
,
h
2
|
G
h
1
/
h
2
)
” (Page 9, Section “Breakpoint genotyping”, col. 2, paragraphs 1-2, lines 1-11). This suggests that the short sequence reads realigned to haplotypes are used to determine a probability of observing the reads with the underlying plurality of haplotypes.
Chen et al. does not disclose determining a variable number tandem repeat (VNTR) status.
However, Bakhtiari et al. discloses “In contrast to methods like VNTRseek that seek to discover/identify VNTRs, we describe a method, adVNTR, for genotyping VNTRs at targeted loci in a donor genome. For any target VNTR in a donor, adVNTR reports an estimate of RU counts and point mutations within the RUs.” (Page 1710, col. 2, paragraph 1, lines 1-5). This suggests determining VNTR status or the state of the VNTR, which includes repeat unit (RU) counts and point mutations.
Chen et al. does not disclose determining a status of the VNTR of the second subject based on the probability indications of each of the plurality of haplotypes.
However, Bakhtiari et al. discloses “The first step in adVNTR is to recruit all reads that match a portion of the VNTR sequence. Alignment-based methods do not work well due to changes in RU counts (Results), but the adVNTR HMM allows for variable RU count.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 1, lines 1-4). Also, further discloses “Filtered reads were aligned to the HMM using the Viterbi algorithm. Only reads with matching probability higher than a specified threshold were retained.” (Page 1716, Section “Read recruitment”, col. 2, paragraph 2, lines 1-3). Bakhtiari et al. also discloses “Genotyping VNTRs in a donor genome sequenced using short (Illumina) or longer single-molecule reads, requires the following: (1) recruitment of reads containing the VNTR sequence; (2) counting RUs for each of the two haplotypes” (Page 1709, col. 2, paragraph 2, lines 1-4). This suggests a matching probability indication of the haplotypes of the VNTRs and a probability threshold that must be overcome for the haplotypes to be retained before determining a VNTR status, which includes repeat unit counts.
Conclusion
No claims are allowed.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686