DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 22, 2026 has been entered.
Preliminary Remark
Claims 1-9 are canceled.
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 Sheng et al. (CN 112070711A, published December 2020; using Google machine-translated to English), made in the Office Action mailed on October 23, 2025 is withdrawn in view of the below ground of rejection based on additional reference(s).
Rejection – New Grounds
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 10-18 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 Sheng et al. (CN 112070711A, published December 2020; using Google machine-translated to English).
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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).
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).
Sheng et al. teach a method for analyzing microdroplets from an image comprising microdroplets by use of artificial intelligence:
“invention relates to the field of micro-droplets, in particular to a method for analyzing microdroplets in a micro-droplet image detection method” (page 4)
“purpose of the present invention is to … provide a micro-droplet analysis method in the micro-droplet image detection method, characterized in that the analysis method includes … training a A convolutional neural network classifier that can perform binary classification on the input image, the network is used to identify and judge which droplets are qualified droplets and which are unqualified droplets in the microdroplet image to be detected … all the images of the suspected microdroplet … is sent to the classifier trained by S1 [neural network], and the classifier interprets which droplets in the microdroplet image to be tested are qualified droplets and which are unqualified droplets” (page 4, bottom paragraph)
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 Sheng 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.
To this end, Sheng et al. identify the same problem when performing digital PCR:
“Due to its extremely high sensitivity and accuracy, microdroplet digital PCR has been widely used in the field of biomedical detection. This technology dilutes and disperses DNA or RNA samples in tens of thousands or even millions of independent microfluids. In the droplet, each reaction unit contains zero or one or more target molecules (DNA or RNA templates). After all the droplets are amplified, the fluorescence signal intensity in each droplet is analyzed and combined with mathematics … enables the detection of nucleic acid concentration in a sample” (page 4)
Sheng et al. also recognize the importance of accurately processing the image to account for “perturbations”:
“in large-field imaging, an image may contain hundreds of thousands or even millions of microdroplets. Due to uneven lighting conditions, the droplets at the end often appear to be different from the droplets at the center, different characteristics … insoluble impurities may be introduced into the microdroplets … dust in the air may also be adsorbed on the microfluidic chip …the unqualified droplets often have extremely high fluorescence intensity, which is likely to cause false positives” (page 4)
Sheng et al. solves such a problem by providing an automated means (via neural network) to accurately identify qualified and unqualified microdroplets from an image containing a plurality of microdroplets (see page 6, bottom paragraph S1-S4).
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to employ an artificial neural network means of Sheng 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., while discarding unqualified droplet images, 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.
Conclusion
No claims are allowed.
Applicant’s arguments with respect to the previous rejection have been considered but are moot because the new ground of rejection based on a new reference.
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.
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/YOUNG J KIM/Primary Examiner
Art Unit 1637 February 10, 2026
/YJK/