DETAILED ACTION
The amendment filed on 09/30/2026 has been entered and fully considered. Claims 5 and 10-14 are canceled. Claims 1-4, 6-9 and 15-22 are pending, of which claim 1-3, 8 and 15-18 are amended, and Claims 21-22 are newly added.
Response to Amendment
In response to amendment, the examiner modifies rejection over the prior art established in the previous Office 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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1-4, 6-9 and 15-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. Patent No. 11,242,558, claim 1-21 of U.S. Patent No. 11,814,619, claim 1-22 of U.S. Patent No. 11,834,714, claim 1-17 of U.S. Patent No. 12,252,745, claim 1-6 of U.S. Patent No. 12,000,842, claim 1-18 of U.S. Patent No. 12,265,088, claim 1-30 of U.S. Patent No. 12,270,815, claim 1-20 of U.S. Patent No. 12,540,948, claim 1-23 of U.S. Patent No. 12,631,651. Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant claims and the currently patented claims expressly recite the same subject matter, it would have been obvious to one of ordinary skill in the art at the time the invention was made to employ both device and methods, as recited in both sets of claims.
The pending claims are directed to an obvious variation of the commonly owned claimed subject matter, including partitioning target nucleic acid molecules, detecting optical signals from target-positive partitions, and generating a count based on the detected signals. The additional recitation of identifying target-positive partitions using an optical property criterion and a morphology criterion merely further specifies known image-analysis criteria used to classify and count positive partitions from optical image data. Applicant has not shown that these additional image-processing criteria result in a patentably distinct invention over the reference claims.
Claims 1-4, 6-9 and 15-22 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-21 of copending Application No. 19/648,997 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant claims and the currently copending claims expressly recite the same subject matter, it would have been obvious to one of ordinary skill in the art at the time the invention was made to employ both device and methods, as recited in both sets of claims.
The pending claims are directed to an obvious variation of the commonly owned claimed subject matter, including partitioning target nucleic acid molecules, detecting optical signals from target-positive partitions, and generating a count based on the detected signals. The additional recitation of identifying target-positive partitions using an optical property criterion and a morphology criterion merely further specifies known image-analysis criteria used to classify and count positive partitions from optical image data.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-4, 6-9 and 15-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liao et al. (PNAS, 2020, IDS) (Liao) in view of Heyries et al. (Nature Methods, 2011) (Heyries)..
Regarding claim 1, Liao teaches a method comprising:
(a) providing a plurality of partitions, wherein the plurality of partitions is within a continuous phase within a container (Fig. 1A, page 25628, par 4; page 25632, par 6);
(b) using an imaging system to scan the plurality of partitions across a set of optical channels to detect signals from at least a subset of the plurality of partitions (Fig. 1B-C, page 25628, par 4; page 25630, par 1; page 25632, par 7); and
(c) using the signals detected in (b) to generate a count of a number of a target nucleic acid molecule by identifying partitions of the plurality of partitions comprising the target nucleic acid molecule based on satisfaction of an optical property criterion and a morphology criterion, wherein the optical property criterion is based on a lateral or axial intensity gradient across a plurality of pixels and the morphology criterion is based on a three-dimensional profile of a positively-identified partition profile of a control sample (Liao, p. 25628, “By recording a series of planar images of the CLEAR emulsion … we are able to rapidly reconstruct the 3D structure of the whole PCR emulsion and achieve digital counting of the positive droplets”; Liao, p. 25630, “By stacking the raw light-sheet images into a 3D volume, we further identified and counted the positive droplets using a customized algorithm”; Liao, p. 25630, Fig. 3 caption, “in silico scanning detects the spatial local intensity maxima … as candidate positive signals” and “the fluorescently positive droplets … are located and counted based on the identified candidates”; Liao, p. 25632, “we searched for intensity local maxima in the 3D image matrices” and “the candidate coordinates were filtered based on intensity, template correlation, contrast, and distance to remove noise and artifact signals”; Liao, p. 25632, “Finally the nucleic acid copy number and concentration was obtained with the number of identified positive droplets by Poisson distribution” — Liao’s detected fluorescence image signals are used to identify positive droplet partitions and generate a nucleic-acid copy number/count).
Liao teaches or at least suggests the claimed optical property criterion because Liao identifies candidate positive signals using spatial local intensity maxima in 3D image matrices and filters candidates based on intensity and contrast. A spatial local intensity maximum and contrast in a 3D image matrix are based on intensity changes relative to neighboring pixels/voxels, and it would have been obvious to implement this intensity/contrast analysis using a lateral or axial intensity gradient across a plurality of pixels to identify true fluorescent positive partitions and reject background/noise.
Liao further teaches or at least suggests the claimed morphology criterion because Liao filters candidate positive signals based on “template correlation,” “distance,” and 3D reconstruction of the droplet emulsion. It would have been obvious to a person of ordinary skill in the art to use a three-dimensional profile of a positively identified partition from a control sample as the template/profile for Liao’s template-correlation filtering, because a control-sample-derived positive partition profile would predictably improve identification of true positive partitions and reduce false-positive counts from noise or artifact signals in Liao’s 3D light-sheet digital PCR counting method.
Liao does not explicitly teach wherein the plurality of partitions comprises at least 1 million partitions. However, Liao teaches generating around 443,000 droplets in 4 minutes in a 200-µL PCR microcentrifuge tube and teaches increasing the number of through-hole channels “from 7 to 37” to increase/maintain droplet generation rate. Heyries further teaches a digital PCR device that performs “a million single-molecule xiaoyunxiaoyuresp PCRs in uniform arrays of picoliter-volume chambers” and teaches “uniform and defect-free partitioning of molecules in an array of 1,000,000 chambers.” Heyries’ 1,000,000 chambers correspond to at least 1 million digital PCR partitions.
It would have been obvious to a person of ordinary skill in the art to increase the number of partitions in Liao to at least 1 million partitions because Liao teaches high-throughput droplet digital PCR using hundreds of thousands of partitions, and Heyries teaches that increasing the total number of digital reactions improves sensitivity, precision, and dynamic range. Further, Liao’s disclosed rate of approximately 443,000 droplets in 4 minutes corresponds to about 110,750 droplets/minute, such that generating 1,000,000 droplets would have required only about 9 minutes under the same disclosed generation rate. Thus, increasing Liao’s droplet number to at least 1 million would have been a predictable scaling of Liao’s droplet-generation process to increase digital PCR counting capacity, precision, and dynamic range.
Regarding claim 2, Liao teaches that wherein the target molecules comprise a nucleic acid molecule (page 25632, par 8).
Regarding claim 3, Liao teaches the method of claim 1 as discussed above. Liao does not explicitly teach wherein the plurality of partitions comprises a plurality of wells. However, Heyries teaches a microfluidic digital PCR device that “performs a million single-molecule PCRs in uniform arrays of picoliter-volume chambers,” (page 1, par 3), wherein a sample mixed with PCR reagents is injected into a PDMS device connected to linear arrays of 10-pL “dead-end” chambers (page 1, par 4), and wherein the chambers are partitioned by flushing the device with an immiscible fluorinated oil (Fig. 1c, page 1, par 4). Heyries further teaches that “[t]his method allows for the uniform and defect-free partitioning of molecules in an array of 1,000,000 chambers.” (page 1, par 4) Heyries’ picoliter-volume chambers/dead-end chambers correspond to the claimed set of wells.
It would have been obvious to a person of ordinary skill in the art to modify Liao’s digital PCR method to use Heyries’ chamber/well-based partitions because Liao recognizes digital PCR as a partitioning, amplification, and counting process, and Heyries teaches a known digital PCR partition format using uniform picoliter-volume chambers for single-molecule PCR. Heyries further teaches that increasing the total number of digital reactions improves sensitivity, precision, and dynamic range, and that the 1,000,000-chamber device provides high-density partitioning for digital PCR (page 2, par 3). Therefore, using Heyries’ well/chamber-based partitions in Liao’s digital PCR method would have been a predictable substitution of one known digital-PCR partition format for another to provide high-density partitioning and improved digital PCR performance.
Regarding claim 4, Liao teaches that wherein (b) comprises scanning a sheet of the container using a light sheet imaging system (Fig. 1B, page 25632, par 7).
Regarding claim 6, Liao does not explicitly teach wherein the plurality of partitions comprises at least 5 million partitions. However, Heyries teaches that “[t]his approach may be scaled to achieve tens of millions of reactions per device using the same footprint.” Heyries’ scalable tens-of-millions reaction device teaches at least 5 million partitions.
It would have been obvious to a person of ordinary skill in the art to further increase the number of partitions in Liao’s digital PCR method to at least 5 million partitions because Heyries expressly teaches scaling digital PCR partition arrays to tens of millions of reactions per device, and teaches that increasing the total number of digital reactions improves sensitivity, precision, and dynamic range. The modification would have been a predictable scaling of the number of digital PCR partitions to improve counting performance and dynamic range.
Regarding claim 7, Liao does not explicitly teach wherein (c) is performed within 5 seconds. However, Liao teaches high-speed counting/readout in which “high-speed scanning of the whole tube can be completed within 6 s,” and further teaches that, by stacking the raw light-sheet images into a 3D volume, Liao “identified and counted the positive droplets using a customized algorithm.” Liao further teaches processing raw LSFM image stacks by correcting background variance, searching for intensity local maxima in 3D image matrices, filtering candidates, and obtaining nucleic acid copy number and concentration from the number of identified positive droplets.
It would have been obvious to a person of ordinary skill in the art to optimize Liao’s computational counting step to be performed within 5 seconds because Liao expressly emphasizes rapid, high-throughput CLEAR-dPCR readout and uses a straightforward image-processing/counting procedure based on stacking image planes, identifying local maxima, filtering candidate coordinates, and counting positive droplets. Performing the counting step within 5 seconds would have been a predictable optimization of known image-processing software/hardware to reduce analysis time and improve high-throughput digital PCR performance.
Regarding claim 8, Liao teaches that wherein the plurality of partitions comprises a plurality of nucleic acid molecules comprising the target nucleic acid molecule (page 25632, par 8).
Regarding claim 9, Liao teaches that the method of claim 8, further comprising before (b), amplifying the plurality of nucleic acid molecules (page 25628, par 4).
Regarding claim 15, Liao teaches that wherein the count of the number of target nucleic acid molecules is between 1 copy of the target nucleic acid molecule and 1,000,000 copies of target nucleic acid molecules (page 25630, par 2).
Regarding claim 16, Liao teaches that wherein identifying partitions of the plurality of partitions comprising the target nucleic acid molecule is further based on satisfaction of a signal intensity criterion (Fig. 3D, page 25632, par 8).
Regarding claim 17, Liao teaches the method of claim 16 as discussed above. Liao does not explicitly teach wherein the signal intensity criterion is based upon maximum convoluted intensity of the plurality of pixels, a difference between the maximum intensity and the minimum intensity of the plurality of pixels, and a total intensity of the plurality of pixels. However, Liao teaches identifying positive droplets by searching for intensity local maxima in 3D image matrices and filtering candidate coordinates based on intensity, template correlation, and contrast (page 25632, par 8). Liao’s local intensity maxima correspond to maximum intensity information, Liao’s contrast filtering corresponds to evaluating a difference between high and low intensity/background signal, and Liao’s template correlation corresponds to comparing image intensity data with an expected droplet template/profile.
Heyries further teaches image analysis in digital PCR, wherein intensity measurements for each color channel at each chamber are computed by summing the pixel intensities over a 20 pixel by 20 pixel square centered at each chamber, and a threshold is determined to classify positive and negative chambers (page 5, par 2). Heyries’ summed pixel intensities correspond to total intensity of the set of pixels.
It would have been obvious to a person of ordinary skill in the art to implement Liao’s signal-intensity-based droplet identification using maximum convoluted intensity/template-correlation intensity, max-min intensity difference/contrast, and total pixel intensity as taught or suggested by Liao and Heyries because such intensity features were known image-analysis parameters for classifying positive digital PCR partitions and would predictably improve discrimination of true positive partitions from background, noise, and artifacts.
Regarding claim 18, Liao teaches that wherein the signal intensity criterion is based upon a maximum signal intensity of the plurality of pixels (Fig. 3D, page 25632, par 8).
Regarding claim 19, Liao does not explicitly teach wherein the container has a volumetric capacity of 50 microliters. However, Liao teaches performing CLEAR-dPCR in regular PCR microcentrifuge tubes and generating around 443,000 droplets in 200-µL regular PCR microcentrifuge tubes (page 25632, par 6). Liao further teaches that reduced reagent cost, simplified workflow, and high-throughput digital PCR are desired advantages of miniaturized digital PCR (page 25628, par 1). It would have been obvious to a person of ordinary skill in the art to use a smaller PCR tube or container having a 50-µL volumetric capacity because container volume is a routine design choice/result-effective variable in PCR/digital PCR systems, and reducing the container volume would predictably reduce reagent use, reduce assay cost, and maintain compatibility with partitioned PCR processing.
Regarding claim 20, Liao teaches that wherein the container is a tube (Fig. 1A).
Regarding claim 21, Liao further teaches wherein the count of the number of the target nucleic acid molecules is further based on identifying partitions of the plurality of partitions comprising nucleic acid molecule based on the satisfaction of a shape criterion (Liao, p. 25630, “The spatial resolution of our light-sheet imaging is 6.5 μm (lateral) and 20 μm (axial), sufficient to identify the 41-μm droplets in all three dimensions”; Liao, p. 25632, “the candidate coordinates were filtered based on intensity, template correlation, contrast, and distance to remove noise and artifact signals” — Liao’s identifying 41-μm droplets in 3D and filtering candidates based on template correlation and distance teaches or suggests identifying true droplet partitions based on expected droplet shape/geometry).
To the extent Liao does not explicitly recite the term “shape criterion,” it would have been obvious to a person of ordinary skill in the art to use a shape criterion when identifying Liao’s target-positive droplet partitions because Liao’s droplets have an expected three-dimensional droplet geometry and known size, and using shape/geometry would predictably distinguish true droplet partitions from noise, artifacts, merged droplets, or non-droplet signals in Liao’s 3D image-based counting method.
Regarding claim 22, Liao further teaches wherein the shape criterion is based on a radius of a partition of the plurality of partitions (Liao, p. 25632, “Around 443,000 droplets were generated in 200-μL regular PCR microcentrifuge tubes with an average diameter of 41 μm”; Liao, p. 25630, “The spatial resolution of our light-sheet imaging is 6.5 μm (lateral) and 20 μm (axial), sufficient to identify the 41-μm droplets in all three dimensions” — Liao’s 41-μm average droplet diameter corresponds to an expected droplet radius of about 20.5 μm).
To the extent Liao does not explicitly state that a radius is used as the shape criterion, it would have been obvious to use the expected droplet radius as a shape criterion because Liao’s droplets have a known average diameter/radius and Liao identifies droplets in three dimensions. Using the expected radius would predictably improve classification of true droplet partitions and rejection of artifacts or non-droplet signals.
Response to Arguments
Applicant's arguments filed 09/30/2026 have been fully considered but they are not persuasive.
Response to Double Patenting Arguments
Applicant argues that the pending claims are patentably distinct from the identified patents and application because the identified claims allegedly do not recite “using the signals detected in (b) to generate a count of a number of the target nucleic acid molecule by identifying partitions of the plurality of partitions comprising the target nucleic acid molecule based on the satisfaction of an optical property criterion and a morphology criterion, wherein the optical property criterion is based on a lateral or axial intensity gradient across a plurality of pixels and the morphology criterion is based on a three-dimensional profile of a positively-identified partition profile of a control sample.”
Applicant’s arguments have been considered but are not persuasive. The fact that the reference claims do not use the exact same wording as amended claim 1 does not, by itself, establish patentable distinctness. The issue for nonstatutory obviousness-type double patenting is whether the pending claims are patentably distinct from the claims of the identified patents/applications, not whether the claims are written in identical terms. Applicant has not provided a substantive explanation why the amended counting/image-analysis features would have been patentably distinct from the digital analysis/counting features claimed in the identified patents/applications.
The rejection is maintained because the pending claims are directed to an obvious variation of the commonly owned claimed subject matter, including partitioning target nucleic acid molecules, detecting optical signals from target-positive partitions, and generating a count based on the detected signals. The additional recitation of identifying target-positive partitions using an optical property criterion and a morphology criterion merely further specifies known image-analysis criteria used to classify and count positive partitions from optical image data. Applicant has not shown that these additional image-processing criteria result in a patentably distinct invention over the reference claims.
Response to prior art rejection Arguments
Applicant argues that claim 1 is novel over Liao because Liao does not teach or disclose “wherein the plurality of partitions comprises at least 1 million partitions” and does not teach the recited step of “using the signals detected in (b) to generate a count of a number of a target nucleic acid molecule by identifying partitions of the plurality of partitions comprising the target nucleic acid molecule based on satisfaction of an optical property criterion, and a morphology criterion, wherein the optical property criterion is based on a lateral or axial intensity gradient across a plurality of pixels and the morphology criterion is based on a three-dimensional profile of a positively-identified partition profile of a control sample.”
Applicant’s arguments have been considered but are not persuasive of patentability. The prior rejection of claim 1 under 35 U.S.C. § 102 over Liao has been withdrawn in view of the amendment. However, amended claim 1 is rejected under 35 U.S.C. § 103 over Liao in view of Heyries, as set forth below.
Regarding the limitation requiring at least 1 million partitions, Liao teaches generating around 443,000 droplets in 200-µL regular PCR microcentrifuge tubes in 4 minutes. Thus, Liao teaches a droplet-generation rate of approximately 110,750 droplets/minute. At that disclosed rate, generating 1,000,000 droplets would have required only about 9 minutes of droplet generation. Claim 1 does not require generating the at least 1 million partitions within 4 minutes. Further, Liao teaches increasing the number of through-hole channels from 7 to 37 to compensate for reduced droplet generation rate, which shows that the number of generated droplets can be increased by routine process scaling. Heyries further confirms that digital PCR arrays having 1,000,000 partitions were known and beneficial for improving digital PCR sensitivity, precision, and dynamic range. Accordingly, it would have been obvious to a person of ordinary skill in the art to increase Liao’s number of droplet partitions to at least 1 million by extending the droplet-generation time, increasing the number of through-hole channels, and/or processing a corresponding amount of reaction mixture.
Regarding step (c), Liao teaches using signals detected during light-sheet imaging to generate a target nucleic-acid count by identifying positive droplet partitions. Liao teaches recording planar images of the CLEAR emulsion, reconstructing a 3D structure of the whole PCR emulsion, and digitally counting positive droplets. Liao further teaches stacking raw light-sheet images into a 3D volume, detecting spatial local intensity maxima as candidate positive signals, locating and counting fluorescently positive droplets, and obtaining nucleic acid copy number and concentration from the number of identified positive droplets. Thus, Liao teaches the core recited operation of using the signals detected in step (b) to identify target-positive partitions and generate a count of target nucleic acid molecules.
Liao also teaches or suggests the recited image-analysis criteria. Liao teaches searching for intensity local maxima in 3D image matrices and filtering candidate coordinates based on intensity and contrast. A local intensity maximum and contrast in a 3D image matrix are based on intensity differences relative to neighboring pixels/voxels, and it would have been obvious to implement Liao’s intensity/contrast analysis using a lateral or axial intensity gradient across a plurality of pixels to identify true fluorescent positive partitions and reject background or noise. Liao further teaches filtering candidates based on template correlation, distance, and 3D reconstruction of the droplet emulsion. It would have been obvious to use a three-dimensional profile of a positively identified partition from a control sample as the template/profile for Liao’s template-correlation filtering, because a control-sample-derived positive partition profile would predictably improve identification of true positive partitions and reduce false-positive counts caused by noise or artifacts.
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 XIAOYUN R XU, Ph. D. whose telephone number is (571)270-5560. The examiner can normally be reached M-F 8am-5pm.
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/XIAOYUN R XU, Ph.D./ Primary Examiner, Art Unit 1797
