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 .
Priority
Receipt is acknowledged that application is a National Stage application of PCT PCT/CA2022/051645. Priority to 63/276,803 with a priority date of 11/08/2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDSs dated 5/07/2024 and 12/14/2025 has been considered and remains placed in the application file.
Election/Restrictions
Claims 1-11 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on June 4, 2026; therefore the restriction requirement is made final.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 12-15 are rejected under 35 U.S.C. 103 as obvious over Katsigiannis et al., "MIGS-GPU: Microarray Image Gridding and Segmentation of the GPU," IEEE J of Biomed and Health Informatics (2017), 21(3): 867-74 in view of Karthik, S. A., and S. S. Manjunath. "An enhanced approach for spot segmentation of microarray images." Procedia computer science 132 (2018): 226-235 and US Patent Publication 2023 0349893 A1, (Didar et al.)
Claim 12
[AltContent: textbox (Figure 1 shows the gridding and block segmentation techniques used to segment each spot.)] Regarding claim 12, Katsigiannis et al. teach a method of processing images of sensor spots printed on a microarray substrate wherein the sensor spots emit light pixels for detection of the sensor spots, said method comprising the following steps: employing a central processing unit (CPU) ("All the other steps of the algorithm were
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computed on the CPU," sec. 3, par. 1) for detecting a sensor spot region containing the sensor spots to be analyzed through a gridding algorithm; ("The determination of "vertical'" or "horizontal" line segments is viewed as an optimization problem, which is addressed by using the GA presented in [28]. The GA determines the exact values of the coordinates of all the "vertical" or "horizontal" line segments constituting the borders of adjacent blocks or spot cells," sec. 2, par. 2) segmenting microarray images of the sensor spots into individual sub images with a same dimension wherein each sub image contains a sensor spot and a background of the sensor spot; (The microarray image is segmented into numerous cells, each containing one spot and background," sec. 1, par. 3) and transferring constructed 3D arrays of both sensor spots and initial level set Φo(x, y) using a CPU program to a graphic processing unit (GPU) device ("the computed gridding results and the segmentation mask are then returned to the host memory," sec. 4, par. 5).
Katsigiannis et al. do not explicitly teach all of stacking the sub images into an array of 2D images with a depth of a total number of sensor spots; in parallel constructing another 3D array with the same dimension using a pre-defined identical initial level set Φo(x, y) wherein Φ(x, y) is defined as the signed distance function from C, where the value of Φ(x, y) is positive inside C and negative outside C, C being a circular curve around the sensor spot.
However, Karthik et al. teach in parallel constructing another 3D array with the same dimension using a pre-defined identical initial level set Φo(x, y) wherein Φ(x, y) is defined as the signed distance function from C, where the value of Φ(x, y) is positive inside C and negative outside C, C being a circular curve around the sensor spot ("time t intensity set function defines separation of spot and contour edge of the spot is given by two areas, area inside of curvature(ρ≥0) and outside of curvature (ρ<0). Edge contour is obtained by zero intensity set function {(i,j) such that ρ(i,j,t)=0)," sec. 3.2, par. 2).
Therefore, taking the teachings of Katsigiannis et al. and Karthik et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify microarray image gridding and segmentation methods as taught by Katsigiannis et al. to use the spot edge defining methods as taught by Karthik et al. The suggestion/motivation for doing so would have been that, “Fundamental logic is to begin with curvature Cr allow the curvature to shift orthogonally towards inside of the curvature with predefined momentum. This method is applied to get movement of outline by making use of attributes of spot such as radius, width, height of the spot[23]. An interface of Cr represented as set function ω(i,j,t) called intensity set function at time t where (i,j) are points in plane of curvature” as noted by the Karthik et al. disclosure in section 3.2, paragraph 1, which also motivates combination because the combination would predictably have a higher accuracy as there is a reasonable expectation that the resulting curvature and intensity-based boundary evolution accurately and dynamically captures the true morphological variations of individual spots; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
While neither Katsigiannis et al. nor Karthik et al. teach stacking the sub images into an array of 2D images with a depth of a total number of sensor spots, Didar et al. teach stacking the sub images into an array of 2D images with a depth of a total number of sensor spots ("The images were split into stacks with only the green or red stack being retained," par. 209).
Therefore, taking the teachings of Katsigiannis et al., Karthik et al., and Didar et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify microarray image gridding and segmentation methods as taught by Katsigiannis et al. to use the spot edge defining methods as taught by Karthik et al. and the Chan-Vese segmentation algorithm as taught by Didar et al. The suggestion/motivation for doing so would have been that, “In Chan-Vese segmentation image processing in Python, pixels within a border demark the spot region while pixels outside the border belong to local background. Spot signal is calculated as the MFI of spot region minus the median pixel value of local background (i.e., background subtraction). For each level, a ‘raw’ mean and standard deviation are calculated from the signals of 18 replicates” as noted by the Didar et al. disclosure in paragraph [0183], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that the resulting system would achieve more precise background subtraction and quantify spot signals for high-replicate assay reliability; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 13
While Katsigiannis et al. teaches applying a spot-finding method, Katsigiannis et al. does not disclose applying a spot-finding method using a Chan-Vese segmentation algorithm and a quality-check process to every pixel in parallel through a kernel function on a GPU to generate the segmentation result.
However, Didar et al. teach the steps of applying a spot-finding method using a Chan-Vese segmentation algorithm and a quality-check process to every pixel in parallel through a kernel function on a GPU to generate the segmentation result ("Chan-Vese segmentation image processing in Python, pixels within a border demark the spot region while pixels outside the border belong to local background," par. 183).
Katsigiannis et al., Karthik et al., and Didar et al. are combined as per claim 12.
Claim 14
While Katsigiannis et al. discloses segmentation of spot images, Katsigiannis et al. do not explicitly teach all of the segmentation result is a 3D image with the same dimension and order as the input 3D spot image.
[AltContent: textbox (Figure 3 shows the segmentation inputs and results.)]
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However, Karthik et al. teach the segmentation result is a 3D image with the same dimension and order as the input 3D spot image ("A unit template of mentioned size created to get circular shaped contour because shape of the spot assumed to be circular … Before getting segmented propose method demands for size of spot. Since radius of the spot varies from image to image because of this phenomenon initialization of the spot done then resize of spot can be achieved," sec. 3.1 par. 1).
Katsigiannis et al., Karthik et al., and Didar et al. are combined as per claim 12.
Claim 15
Regarding claim 15, Katsigiannis et al. teach the step of transferring back the segmentation result with corresponding quality checks to the CPU for the reporting of results ("by pressing a button, the program executes, and subsequently, the final results are returned. MIGS-GPU runs on the Windows OS and requires an NVIDIA graphics card with CUDA compute capability 3.0 or above," sec. 5, par. 1).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2004 0047499 A1 to Shams discloses adaptive grid-based DNA microarray image segmentation and intensity extraction techniques.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARSTEN F LANTZ whose telephone number is (571) 272-4564. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/Karsten F. Lantz/Examiner, Art Unit 2664
Date: 6/24/2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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