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 .
The Status of the Claims
Claims 1-20 are pending for examination.
Claims 1 and 20 are independent Claims.
Claims 1-20 are rejected under 35 U.S.C. §103.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-13, 15-17 and 19-20 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Kermani (U.S. 2015/0169824 hereinafter Kermani) in view of Kachroo et al. (U.S. 2021/0363526 hereinafter Kachroo) in further view of Wang et al. (U.S. 2019/0237163 hereinafter Wang).
As Claim 1, Kermani teaches: a computer-implemented method (Kermani (¶0239 last 11 lines), computer and memory) of identifying unreliable clusters to improve accuracy and efficiency of base calling using a sequencing instrument, the computer-implemented method including:
accessing, for a first subset of sequencing cycles of a sequencing run on the sequencing instrument, per-cycle cluster image data (Kermani (¶0030 line 3-8), sequencing run involves imaging biochemical reactions of nucleic acids on a substrate) derived from sequencing images captured by the sequencing instrument for a plurality of clusters of nucleic acids (Kermani (¶0042 line 6-14, ¶0050 line 1-6), per-cycle cluster data (a different position of a nucleic acid) is interrogated. Each position is collated to form a sequence read);
base calling, at each sequencing cycle in the subset of sequencing cycles, each cluster of nucleic acids in the plurality of clusters of nucleic acids (Kermani (¶0042 line 11-14, ¶0043 line 1-6), base calling component includes multiple stages of base calling) depicted by the sequencing images capture by the sequencing instrument (Kermani (¶0038 line 9-11), “the sequencing instrument 12 may include primary subsystems, such as a substrate 14 for holding nucleic acids 13, a liquid handling robot 16, and a high-speed imager 18”), including:
processing the per-cycle cluster image data and generating intermediate representations of the per-cycle cluster image data (Kermani (¶0045 line 1-4, ¶0050 line 1-6, ¶0183 line 1-4, ¶0047 line 8-12), intensity for values for the current cycle is generated. Intensity of a given channel for a first position can be calculated as weighted sum of the intensities of the four channels for the first position of the first nucleic acid. Kermani (¶0038 line 2-4), “basecalling using intensity values (e.g., as determined from digital images) of nucleic acids according to one embodiment”) from sequencing images captured by the sequencing instrument (Kermani (¶0038 line 9-11), “the sequencing instrument 12 may include primary subsystems, such as a substrate 14 for holding nucleic acids 13, a liquid handling robot 16, and a high-speed imager 18”), and
processing the intermediate representations though an output layer and producing a per-cluster (Kermani (¶0182 line 1-4, ¶0183 line 1-15), system receives sequencing data of test nucleic acids. Intensity value is identified for N position of the first test nucleic acid), per-cycle probability quadruple for each cluster of nucleic acids and for each sequencing cycle (Kermani (¶0157 line 2-4, ¶0163 line 1-6), output of the model is MxN matrix scores where each of the M values represent the probability of the corresponding base to be present), wherein a particular per-cluster, per-cycle probability quadruple indicates identified probabilities of a base (Kermani (¶0157 line 2-4, ¶0163 line 1-6), output of the model is MxN matrix scores where each of the M values represent the probability of the corresponding base to be present) incorporated in a particular cluster of nucleic acids at a particular sequencing cycle being A, C, T, and G (Kermani (¶0002 line 4-6), determining an order of nucleotide bases);
determining a filter value for each per-cluster, per-cycle probability quadruple based on the identified probabilities thereby generating a sequence of filter values for each cluster of nucleic acids (Kermani (¶0189 line 1-5), a filter value is determined for each cluster, for example the highest score is sufficiently larger than a next high score (filter value));
performing at the remainder of sequencing cycles of the sequencing run, base-calling operations on the sequencing instrument (Kermani (¶0189 line 1-5), a filter value is determined for each cluster, for example the highest score is sufficiently larger than a next high score).
generating a sequencing read from base calls for the clusters of nucleic acids in the plurality of clusters that are not identified as the unreliable clusters (Kermani (¶0043), “final base calling is performed, Mapping, Assembly &/or Analysis component 24 may operate on the sequence reads and may produce a variety of outputs, including reads aligned to a reference genome (not shown) and consensus sequence assembly of overlapping reads, shown as sequence 28”)
Kermani may not explicitly disclose:
identifying, from the sequencing images captured by the sequencing instrument, unreliable clusters of nucleic acids in the plurality of clusters of nucleic acids that include sequences of filter values containing at least "N" number of filter values below a threshold "M"; and
Kachroo teaches:
identifying, from the sequencing images captured by the sequencing instrument, unreliable clusters of nucleic acids in the plurality of clusters of nucleic acids that include sequences of filter values containing at least "N" number of filter values below a threshold "M" (Kachroo (¶0082 line 9-16), reads with more than 10% Q<20 were removed); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify base filtering of Kermani instead be sample quality taught by Kachroo, with a reasonable expectation of success. The motivation would be to easily and conveniently to “exclude[ing] reads that failed” (Kachroo (¶0082 line 6)).
Kermani in view of Kachroo does not explicitly disclose:
stop at a remainder of sequencing cycles of the sequencing run, calling operations on the sequencing instrument for the unreliable clusters;
for only those clusters in the plurality of clusters of nucleic acids that are not identified as the unreliable clusters
Wang teaches:
stop at a remainder of sequencing cycles of the sequencing run, calling operations on the sequencing instrument for the unreliable clusters (Wang (¶0091), “The average base quality score is then compared to a threshold value (block 1614). The method 1600 determines whether the base quality score is below the threshold value (decision block 1616). If so, the method 1600 ends (done block 1618) and the base calling of this particular well ends (stop at a remainder of sequencing cycles of the sequencing run)”);
for only those clusters in the plurality of clusters of nucleic acids that are not identified as the unreliable clusters (Wang (¶0091), “The average base quality score is then compared to a threshold value (block 1614). The method 1600 determines whether the base quality score is below the threshold value (decision block 1616). If so, the method 1600 ends (done block 1618) and the base calling of this particular well ends. If not, the method 1600 is performed for the next flow for the particular well beginning at block 1608”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify base filtering of Kermani in view of Kachroo instead be a filtering threshold taught by Garcia, with a reasonable expectation of success. The motivation would be to conserve resources by allowing “the data analysis pipeline may be stopped if the quality of the reads is too low for further analysis with sufficient confidence” (Nuzhdina (¶0506, line 11-15)).
As Claim 2, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches:
wherein the filter value for a per-cluster, per-cycle probability quadruple is determined based on an arithmetic operation involving one or more of the probabilities (Kermani (¶0163 line 1-6), each M represents the probability of corresponding base is present).
As Claim 3, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches: wherein the arithmetic operation is subtraction (Kermani (¶0159 line 1-7), operation involves a substraction).
As Claim 4, besides Claim 3, Kermani in view of Kachroo in further view of Wang teaches wherein the filter value for the per-cluster, per-cycle probability quadruple is determined by subtracting a second highest one of the probabilities from a highest one of the probabilities (Kermani (¶0189 line 1-5), a filter value is determined for each cluster, for example the highest score is sufficiently larger than a next high score).
As Claim 5, besides Claim 2, Kermani in view of Kachroo in further view of Wang teaches wherein the arithmetic operation is division (Kermani (¶0159 line 1-7), normalized values).
As Claim 6, besides Claim 5, Kermani in view of Kachroo in further view of Wang teaches wherein the filter value for the per-cluster, per-cycle probability quadruple is determined as a ratio of the highest one of the probabilities to the second highest one of the probabilities (Kermani (¶0189 line 1-5), a filter value is determined for each cluster, for example the highest score is sufficiently larger than a next high score).
As Claim 7, besides Claim 2, Kermani in view of Kachroo in further view of Wang teaches wherein the arithmetic operation is addition (Kermani (¶0159 line 1-7), addition of neighboring nucleic acids).
As Claim 8, besides Claim 2, Kermani in view of Kachroo in further view of Wang teaches wherein the arithmetic operation is multiplication (Kermani (¶0159 line 1-7), normalized values).
As Claim 9, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches wherein the "N" ranges from 1 to 5 (Kermani (¶0051 lines 4-6), number of cycles is around 5).
As Claim 10, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches wherein the "M" ranges from 0.5 to 0.99 (Kermani (¶0163 line 1-3), M is between 0-1).
As Claim 11, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches wherein the first subset includes 1 to 25 sequencing cycles of the sequencing run (Kermani (¶0051 lines 4-6), number of cycles is around 5).
As Claim 12, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches wherein the first subset includes 1 to 50 sequencing cycles of the sequencing run (Kermani (¶0051 lines 4-6), number of cycles is around 5).
As Claim 13, besides Claim 2, Kermani in view of Kachroo in further view of Wang teaches wherein the output layer is a softmax layer and the probabilities in the per-cluster, per-cycle probability quadruple are exponentially normalized classification scores that sum to unity (Kermani (¶0180 last 4 lines), softmax functions and normalization of scores).
As Claim 15, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches further comprising generating, utilizing a chastity filter, chastity values defined as a ratio of a brightest base intensity divided by a sum of the brightest base intensity and a second brightest base intensity (Kachroo (¶0082 line 7), chastity value).
As Claim 16, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches further comprising identifying the unreliable clusters of nucleic acids in the plurality clusters of nucleic acids at a predefined cycle during a sequencing run (Kermani (¶0189 line 1-5), a filter value is determined for each cluster, for example the highest score is sufficiently larger than a next high score).
As Claim 17, besides Claim 1, Kermani in view of Kachroo in further view of Wang teaches wherein the filtering function is at least one of a maximum log probability function, a minimum squared error function, average signal-to- noise ratio (SNR), and a minimum absolute error function (Kachroo (¶0082 line 9-16), reads with more than 10% Q<20 were removed).
As Claim 19, besides Claim 18, Kermani in view of Kachroo in further view of Wang teaches further including:
determining an average probability score for each cluster of nucleic acids based on maximum probability scores in per-cluster, per-cycle probability quadruples produced for the sequencing cycles in the first subset of sequencing cycles (Kachroo (¶0082 line 9-16), reads with more than 10% Q<20 were removed); and
identifying those clusters of nucleic acids in the plurality of clusters of nucleic acids as the unreliable clusters of nucleic acids whose average probability score is below a threshold (Kachroo (¶0082 line 9-16), reads with more than 10% Q<20 were removed).
As Claim 20, Kermani teaches a system for improving accuracy and efficiency of neural network-based base calling using a sequencing instrument, the system comprising:
memory (Kermani (¶0239 line 3), storage device);
a host processor having access to the memory and configured to execute a detection and filtering logic to identify unreliable clusters (Kermani (¶0239 line 12-13), central processor);
a configurable processor having access to the memory and configured to execute a neural network to produce base call classification scores (Kermani (¶0239 line 12-13), central processor); and
a data flow logic having access to the memory, the host processor, and the configurable processor and configured to (Kermani (¶0239 line 12-19), central processor communicates with each subsystem and control the execution of instructions):
provide, to the neural network, the initial cluster image data to the neural network and cause the neural network (Kermani (¶0058 line 1-9), neural network model)
The rest of the limitations are rejected for the same reasons as Claim 1.
Claim(s) 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kermani and Kachroo in view of Wang in further view of Garcia et al. (U.S. 2012/0020537 hereinafter Garcia).
As Claim 14, Kermani in view of Kachroo in further view of Wang may not explicitly disclose:
wherein the unreliable clusters of nucleic acids are indicative of empty, polyclonal, and dim wells on a patterned flow cell.
Garcia teaches:
wherein the unreliable clusters of nucleic acids are indicative of empty, polyclonal, and dim wells on a patterned flow cell (Garcia (¶0077 last 4 lines), quality predictors include “approximate homopolymer”, “intensity decay”, “signal overlap with background” and “shifted purity G adjustment”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify filter values of Kermani in view of Kachroo in further view of Wang instead be filter values taught by Garcia, with a reasonable expectation of success. The motivation would be to easily and conveniently to identify and marking reads as poor quality data (Garcia (¶0204 line 1-5)).
As Claim 18, besides Claim 17, Kermani in view of Kachroo in further view of Wang may not explicitly disclose:
determining the average SNR over sequencing cycles in the first subset of sequencing cycles for each cluster of nucleic acids based on intensity data in the per-cycle cluster image data, wherein the intensity data depicts intensity emissions of clusters of nucleic acids in the plurality of clusters of nucleic acids and of surrounding background; and
identifying those clusters in the plurality of clusters as the unreliable clusters whose average SNR is below a threshold.
Garcia teaches:
determining the average SNR over sequencing cycles in the first subset of sequencing cycles for each cluster of nucleic acids based on intensity data in the per-cycle cluster image data, wherein the intensity data depicts intensity emissions of clusters of nucleic acids in the plurality of clusters of nucleic acids and of surrounding background (Garcia (¶0201), a measurement of the separation of the signal from the noise); and
identifying those clusters in the plurality of clusters as the unreliable clusters whose average SNR is below a threshold (Garcia (¶0201), a measurement of the separation of the signal from the noise).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify filter values of Kermani in view of Kachroo in further view of Wang instead be filter values taught by Garcia, with a reasonable expectation of success. The motivation would be to easily and conveniently to identify and marking reads as poor quality data (Garcia (¶0204 line 1-5)).
Response to Arguments
Rejections under 35 U.S.C. §103:
As Claim 1 and 20, Applicants argue Garcia and other references does not disclose “stopping at a remainder of sequencing cycles … base calling operations on the sequencing instrument for the unreliable clusters” (last 8 lines of page 11 in the remarks).
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Applicants’ arguments are moot because new reference Wang teaches the limitation(s). Wang (¶0091) teaches that “The average base quality score is then compared to a threshold value (block 1614). The method 1600 determines whether the base quality score is below the threshold value (decision block 1616). If so, the method 1600 ends (done block 1618) and the base calling of this particular well ends. If not, the method 1600 is performed for the next flow for the particular well beginning at block 1608”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147
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