DETAILED ACTION
The present Office Action is responsive to the Amendment received on May 11, 2026.
Preliminary Remark
Claims 1-9 are canceled.
Claim 19 is new.
Claim Rejections - 35 USC § 103
The rejection of claims 10-18 under 35 U.S.C. 103 as being unpatentable over Carmen et al. (US 2014/0221239 A1, published August 7, 2014) in view of Colston et al. (WO 2010/036352 A1, published April 2010) and Sheng et al. (CN 112070711A, published December 2020; using Google machine-translated to English), made in the Office Action mailed on February 13, 2026 is withdrawn in view of the Amendment received on May 11, 2026.
Rejection – New Grounds, Necessitated by Amendment
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.
Claims 10-19 are rejected under 35 U.S.C. 103 as being unpatentable over Carmen et al. (US 2014/0221239 A1, published August 7, 2014) in view of Colston et al. (WO 2010/036352 A1, published April 2010) and Hu et al. (Analytical Methods, June 2019, vol. 11, pages 3410-3418).
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With regard to claim 10, Carmen et al. teach a below-pictured device (reproduced from Fig. 15):As seen, Carmen et al. teach a device for providing a quantitative information for target (“exemplary systems are suitable for making both qualitative and quantitative measurements, section [0071]; “system may be utilized to perform a digital PCR (polymerase chain reaction) analysis”, section [0040]), comprising:
a chamber or a channel which receives a plurality of microdroplets including targets and includes a detection region in which a plurality of microdroplets is present as a single layer (see elements 568 and 570, “fluorescence detection chambers 568, 570”, section [0119]);
a light source which irradiates the plurality of microdroplets if present as single layer (see element 576, “radiation source 576 is configured to illuminate droplets within chambers 568, 570 …”, section [0121)]; “[c]hambers 568, 570 may be configured to have a relatively shallow depth, to allow substantially only a mono-layer of droplets within each chamber, so that only one droplet is disposed within each portion of the line of sight of a detector and is confined to the focal plane of the detector”, section [0120]);
an image sensor configured to provide a single layer image of the microdroplets in which the plurality of microdroplets is present as the single layer in the detection region (see above, see also Batch Fluorescence Detection, “FIG. 15 depicts the system at a time when chamber 568 has already been filled with droplets and is being illuminated and/or imaged …”, section [0123]; “a simple array of droplet-containing wells or reservoirs (such as a plate array) may be placed in a fluorescence detection region and imaged …”, section [0125]); and
a processor which is operably connected to the image sensor (“[s]ource 576 may be configured in various ways to illuminate substantially all of the droplets within a chamber”, section [0121]; “process signals received from the detector (e.g., to identify droplets and estimate target concentrations), and so on”, section [0137]; “droplets and is being illuminated and/or imaged”, section [0123], “a simple array of droplet-containing wells or reservoirs … may be placed in a fluorescence detection region and imaged”, section [0124]); wherein
the processor is configured to provide quantitative data of targets based on the plurality of microdroplets (“hundreds to millions of droplets are analyzed per run … after a desired number of signals have been detected by fluorescence detector 208 … the positive and negative signals are counted and analyzed … and Poisson statistics to determine target presence and target concentration”, section [0070]) , and the detection region has a height which is one time to about two times a diameter of the plurality of microdroplets (“[c]hambers … may be configured to have a relatively shallow depth, so to allow substantially only a monolayer of microdroplets within the chamber”, section [0120], shallow depth to accommodate a monolayer of droplets would necessarily require a depth of at least 1x the diameter of the droplet but not to the point where it can form a multi-layer), and the detection region is defined as a region in which the plurality of microdroplets is dispersed in a plurality of columns to fill the detection region (see above Figure where columns of microdroplets are formed).
With regard to claim 11, the chamber further includes a valve which controls the movement of the plurality of droplets, wherein the processor is configured to adjust the valve to stop the plurality of microdroplets in the chamber; and wherein the image sensor is configured to acquire a chamber image in which the plurality of stopped microdroplets is present (“[s]ystem 560 is configured to … temporarily stopped from flowing through the system. This allows the fluorescence level of many droplets to be detected in a single detection operation”, section [0118]; “[u]pon completion of the detection process on the droplets within chambers … valve 574 may be closed, valve 572 may be opened and another valve 580 at the distal end of chamber 568 also may be opened. This stops the flow of droplets into chamber 570 and restarts the flow of droplets into chamber 568, while allowing the droplets already in chamber 568 to escape through distal valve 580. Another distal valve 582 may be disposed at the end of chamber 570 for a similar purpose”, section [0124]; “electronic components to achieve coordinated operation and control of the system functions”, section [0138]).
With regard to claim 12, the system further comprises an inlet which flows the microdroplets into the chamber (see element 562 of figure above, also, “system 560 includes a droplet input channel 562”, section [0119]); an outlet which the plurality of microdroplets is discharged to the outside of the chamber (see channel connected away from the chambers (distal end) in conjunction with valve 580 which controls the microdroplets to flow out from the chamber); wherein the processor is configured to flow the plurality of microdroplets from inlet to the outlet; and the image sensor is configured to acquire a chamber image in which the plurality of microdroplets moves from the inlet to the outlet (“electronic components to achieve coordinated operation and control of the system functions”, section [0138]).
With regard to claim 13, the chamber has a tapered structure having a width which is reduced toward the inlet or the outlet (see above figure).
With regard to claim 14, the system further comprises a sample generator that provides the mixing of the sample (comprising target nucleic acids) with the reagents necessary for detection (i.e., fluorescent probes) and an immiscible carrier fluid (oil):
“system may include sample preparation 52, droplet generation 54, reaction 56 (e.g., amplification), detection 58, and data analysis 60. In some embodiment, the system may be utilized to perform a digital PCR (polymerase chain reaction) analysis … Droplet generation 54 may involve encapsulating the analyte and/or target nucleic acid in droplets, for example, with an average of about one copy of each analyte and/or target nucleic acid per droplet, where the droplets are suspended in an immiscible carrier fluid, such as oil, to form an emulsion. Reaction 56 may involve subjecting the droplets to a suitable reaction, such as thermal cycling to induce PCR amplification, so that target nucleic acids, if any, within the droplets are amplified to form additional copies. Detection 58 may involve detecting some signal(s) from the droplets …” (section [0041])
“illuminating the droplets with radiation at a wavelength chosen to induce fluorescence … from one or more fluorescent probes associated with the amplified PCR target sequences” (section [0047])
With regard to claim 15, the claim does not require that the device comprise any microdroplets. Therefore, the chamber of Carmen et al.’s device is capable of receiving any number of droplets to single-layer (or monolayer) of droplets which are packed together.
While Carmen et al. explicitly teach that their device is directed to performing digital PCR (which is quantitative), as well as explicitly stating that the image of the chamber comprising the plurality of microdroplets are analyzed, the artisans do not explicitly discuss the use of generated image of the plurality of microdroplets in determining which droplets are target-positive and which are target-negative for the purpose of arriving at a quantity data of the target nucleic acids in the sample.
Carmen et al. do not explicitly teach that artificial neural network should be employed to determine which droplets are target-positive and which droplets are target-negative (claims 16-18), or that the plurality of regions further include a background region in which there is no microdroplet, wherein the single layer image is a fluorescence image, and wherein the quantitative data comprises at least one copy number of targets and concentration of targets (claim 19).
Colston et al. teach a system for performing nucleic acid quantification method utilizing digital PCR (“system may be utilized to perform digital PCR (polymerase chain reaction) analysis”, page 16, lines 6-10).
Colston et al. teach an embodiment of such a device, wherein detection region is configured to contain a batch of single monolayer of droplets (similar to Carmen et al.), with a detector that is configured to image the droplets therein (see Fig. 35 below):
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The plurality of droplets (1386) are in a monolayer (“monolayer of droplets 1386”, page 67, line 26), with an imager (1382) functionally coupled thereto (“System 1380 may include an imager 1382”, page 67, lines 19-20), and this imager is disclosed as taking an image of the plurality of monolayer droplets present in the detection chamber (“imager 1382 and at least one imaging slide 1384 operatively disposed with respect to the imager, to permit image collection of droplets 1386 held by the slide”, page 67, lines 19-21; “the imager may collect images of droplets disposed in wells 1366 … using a CCD camera or a line-scan CCD, among others … a larger field of view, plate 1364 and/or the camera may be placed … otherwise connected to, a translation stage … include laser/PMT device, as is used for detection of microarrays”, page 66, line 28 to page 67, line 2).
Colston et al. also teach the same height conditions for maintaining the monolayer droplets within the detection chamber (“imaging chamber 1388 may have a high aspect ratio with a length and width … the height of the chamber 1388 may correspond to the diameter of the droplets, such as being the same as the droplet diameter or no more than about twice the droplet diameter, among others”, page 67, lines 23-30), as well as teaching that the thermocycling of the droplets can occur in the chamber 1388 and imaged (“the emulsion may be loaded into chamber 1388 before reaction, the slide optionally sealed, and then the emulsion reacted (e.g., thermally cycled) and imaged in the same slide”, page 68, lines 1-3).
Hu et al. teach a method of processing dPCR images for quantitation of the target in a sample:
“convolutional neural network (CNN), a deep learning method which hierarchical feature learning capabilities has made great breakthroughs in biomedical image analysis … we trained two different models to process chip-based and droplet-based PCR images. Once the training is completed, it takes only a few seconds for each test to discover all positive microchambers exactly using the same type of pictures.” (page 3411, 1st column)
“[d]roplet images under a microscope were taken … Images of amplification results were captured under a microscope …” (page 3411, 2nd column)
Hu et al. teaches the segmentation of regions that contain signal-positive and signal-negative (see page 3412, ResNet101 that employs a sliding pane).
Hu et al. teach that the microdroplets comprise fluorescence and the image generated comprises areas where no microdroplets are found (see Fig. 2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Carmen et al. with the teachings of Colston et al. and Hu et al., thereby arriving at the invention as claimed for the following reasons.
As already discussed above, the teachings of Carmen et al. demonstrate the well-known means of performing digital PCR amplification in a plurality of droplets, which are prepared and imaged (see above).
As well, one of ordinary skill in the art would have been well-aware of the decades old digital PCR wherein, an absolute quantification relies on the random distribution of target molecules across the partitions and the data is expected to fit a Poisson distribution.
When performing a quantification via digital PCR, one of ordinary skill in the art would have recognized the need to accurately identify and count and classify
which of the droplets is target-positive (i.e., signal positive) and which are target-negative (i.e., signal negative) from the image captured by the method of Carmen et al.
While Carmen et al. do not explicitly discuss that the “image” of droplets was what was produced from the chamber of their device and utilized in their Batch Fluorescence embodiment , it is clear from the context of the disclosure (as well in view of Colston et al.) that the microdroplets were imaged and this image is utilized when determining the quantity of the target nucleic acids.
As well, digital PCR process requires determining which droplets contained a positive target and which of the droplets did not. Therefore, in a chamber filled a randomly distributed droplets, it is not enough to know the signals produced from a plurality of droplets without the actual image from which the signals were produced.
Therefore, if not implicit, one of ordinary skill in the art would have had the common sense to recognize that the “image” of the microdroplets were utilized when quantitating the amount of target nucleic acids in the Batch embodiment of Carmen et al. in view of the clarification provided by Colston et al.
Based on such a recognition one of ordinary skill in the art would have been lead to employ art-recognized pattern recognition means, such as neural network or artificial intelligence to accurately identify the droplets from an image containing a plurality of droplet images.
Hu et al. teach that for dPCR, “the quick and accurate recognition of positive reaction chambers in fluorescence images is of great importance to ensure detection accuracy” (page 3410, 1st column) and utilizes neural network (R-CNN) to process the images of the droplets.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to employ a neural network means of Hu et al., in order to arrive at a digital PCR system that clearly identifies which droplets are target-positive and which are target-negative from the amplification image of Carmen et al. and Colston et al., so as to accurately arrive at quantifying the amount of target nucleic acid in a sample.
With regard to categorizing which droplets are target-positive and which are target-negative, based on the maturity of the digital PCR technology that involves detection reagents, such as Taqman® probe or SYBR Green, one of ordinary skill in the art would have been well aware of distinguishing a signal produced from a droplet comprising a target versus the background level produced from a droplet only containing the reagents without the target.
Therefore, the invention as claimed is deemed prima facie obvious over the cited references.
Response to Arguments:
While the present rejection is based on a new reference, the Office would like to respond to some of the issues raised by Applicants in their recent response.
Applicants contend that the references of record, “still do not reach the processor workflow recited in the amended claim 10” (page 9, Response), which is not merely the question of whether a chamber of monolayer droplets may be imaged, or whether quantitative analysis may generally be performed in a digital PCR setting, but addressing whether the prior art of record, “teaches or suggests a processor that analyzes image data obtained from the image sensor so as to segment the single layer image into regions including regions of positive microdroplets and regions of negative microdroplets, represented by the segmented regions” (page 10, Response).
Applicants urge that the cited combination of references still fail to teach that image domain workflow.
The Office has carefully considered the argument, but nevertheless has reached a different conclusion.
Re: claim 10:
The argument that the processor must analyze the image from the sensor and “segment the single layer image into regions including regions of positive microdroplets and regions of negative microdroplets, represented by the segmented regions,” is noted. In addressing this argument, the Office assumes that Applicants acquiesce to the fact that the combination of Carmen and Colston do teach a system that comprises a chamber/channel that receives a plurality of microdroplets, a detection region that present the microdroplets in a single layer, and an image sensor that provide a single layer image of the microdroplets1.
Applicants’ contention is that the combination of references lack a processor that processes this image of the single layer of microdroplets to plurality of regions that include positive microdroplets and negative microdroplets by segmenting the image, and using this process to quantitate the amount of targets based on the numbers of positive and negative microdroplets (page 10, bottom paragraph, Response; page 11, 1st paragraph, Response).
To this end, the Office respectfully disagrees with Applicants’ conclusion.
As Applicants are well-aware, a digital PCR is a quantitative means of determining the number of target that is present in a sample, wherein a sample is partitioned into a plurality of partitions that result on an average of zero to 1 copy of a target analyte in each of the partitions, and requires a Poisson Distribution. The quantitation is achieved by determining the total number of partitions and counting the partitions which are positive for target (signal positive) and partitions which are negative for target (signal negative).
Therefore, one of ordinary skill in the art would have been well-aware that implementation of a digital PCR means would have necessarily required the counting of partitions which are signal positive and signal negative, as well as knowing the total amount of partitions generated for the assay.
Because the partitions employed by Colston was microdroplets, with the explicit teaching of generating in image of the single-layer of microdroplets (i.e., partitions), one of ordinary skill in the art would have recognized that the image contained an “image” of all microdroplets which were signal positive and signal negative.
While Colston did not explicitly teach that this image is segmented to determine the signal-positive and signal-negative microdroplets, one of ordinary skill in the art would have recognized that such would have been necessary to determine: a) count the total microdroplets that were present; and b) if a Poisson Distribution was achieved.
In other words, the image containing a plurality of microdroplets must be analyzed by an image processor that is capable of recognizing the microdroplets as well as distinguishes the signal-positive and signal-negative microdroplets. In view of the fact that such processors as well as teachings of using neural network to achieve the steps, it would have been obvious to one of ordinary skill in the art to utilize a computerized means, such as neural network (as evidenced by Hu et al.), so as to arrive at the invention as claimed.
The Office also notes that doing so would have been well-within the purview of the ordinarily skilled artisan as Applicants’ own specification does not contain any such programming, or detailed algorithm by which the method is accomplished. While the Office notes that such a description is not a requirement, it also points to the reasonable expectation of success at combining neural network/programming in image analysis based on the skill level of the ordinarily skilled artisan.
Re: claim 15:
Applicants argue that the limitation of claim 15 describes how the microdroplets are structurally arranged within the device. The office respectfully disagrees because the claimed device does not comprise any microdroplets. Rather, the parent claim 10 recites that the chamber or channel “which receives a plurality of microdroplets” includes a detection region. As to whether the plurality of microdroplets including the amplified targets “moves or is received without spacing the microdroplets,” the device provided by Colston et al. appears to move within the channel in similar manner as that of Applicants’ own system (see Fig. 105 of Colston vs. Applicants’ Fig. 1A and 2A).
Re: claim 16:
Applicants’ arguments presented for claim 16 is addressed together for claim 10 above as the combination of references involve the use of neural network for analyzing the image of the microdroplets in a dPCR quantitation.
For these reasons, the invention as claimed is deemed prima facie obvious over the cited references.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiries
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Young J. Kim whose telephone number is (571) 272-0785. The Examiner can best be reached from 7:30 a.m. to 4:00 p.m (M-F). The Examiner can also be reached via e-mail to Young.Kim@uspto.gov. However, the office cannot guarantee security through the e-mail system nor should official papers be transmitted through this route.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Gary Benzion, can be reached at (571) 272-0782.
Papers related to this application may be submitted to Art Unit 1681 by facsimile transmission. The faxing of such papers must conform with the notice published in the Official Gazette, 1156 OG 61 (November 16, 1993) and 1157 OG 94 (December 28, 1993) (see 37 CFR 1.6(d)). NOTE: If applicant does submit a paper by FAX, the original copy should be retained by applicant or applicant’s representative. NO DUPLICATE COPIES SHOULD BE SUBMITTED, so as to avoid the processing of duplicate papers in the Office. All official documents must be sent to the Official Tech Center Fax number: (571) 273-8300. Any inquiry of a general nature or relating to the status of this application should be directed to the Group receptionist whose telephone number is (571) 272-1600.
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/YOUNG J KIM/Primary Examiner
Art Unit 1637 July 2, 2026
/YJK/
1 Applicants contend, regarding Colston that “mere presence of an image-collection structure is not equivalent to a processor configured as now claimed” (page 11, Response). By this the Office assumes that Applicants concede that imager of Colston generates an image of the single layer of microdroplets produced.
Prosecution Insights