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 .
Election/Restrictions
Applicant’s election without traverse of Species I (claims 1-9, 15 and 17-18) in the reply filed on Feb 23rd 2026 is acknowledged. The application has pending claims 1-9, 15 and 17-18 (withdrawn claims 10-14, and 16 are withdrawn from further consideration).
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.
Claims 1, 3–6, 15 and 17-18 are rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael (Yorav-Raphael et al, US 2020/0034967 A1, 2020) in view of Wagner (Wagner et al, US 2022/0284574 A1, 2022).
Regarding claim 1, Yorav-Raphael teaches a cell image analysis method comprising:
an image acquisition step of acquiring a plurality of images of the cell captured at a plurality of imaging distances that are different from each other in a bright field, the imaging distance being relative distance in imaging a cell using an imaging device between an object subjected to imaging including the cell and a position of focus of an optical system of the imaging device;
(Yorav-Raphael, in [0008], discloses obtaining data of a series of images of a cell sample, the images being captured by performing a depth scan using a digital microscope and being associated with a series of depth levels of the cell sample; wherein [0086], teaches that the in-depth scanning may be performed by varying a distance between a focus plane of the microscope and a sample holder intended to accommodate a cell sample by methods well known in the art, and may be performed with brightfield illumination.)
an index information acquisition step of acquiring index information that is information regarding an index for evaluating a difference between the plurality of images acquired in the image acquisition step;
(Yorav-Raphael, in [0010], further teaches calculating image contrast for the images using a contrast function such as variance, standard deviation, or sum of absolute-value of derivatives, and teaches processing the data by detecting at least one depth level corresponding to a drop in image contrast, where the image contrast at that detected depth level is lower than the image contrast at the immediately preceding and immediately following depth levels. Yorav-Raphael teaches a change in image contrast over depth level and selecting depth from a drop / well in the contrast curve.)
an imaging distance determination step of determining such an imaging distance that a rate of a change in the index information with respect to a change in the imaging distance is equal to or less than a predetermined threshold value;
([0017-0026]: Yorav-Raphael teaches determining a reference depth level by detecting a well in a contrast curve representing image contrast as a function of depth level, the well / bottom reflecting how the image-contrast index changes, including its steepness / slope [as depicted in the figure], over changing imaging distance. Yorav-Raphael further teaches threshold-based comparison of focus-configuration variation, thereby teaching determination of an acceptable imaging distance based on change behavior of the contrast index over depth.)
Yorav-Raphael however fails to teach where Wagner teaches:
a region extraction step of extracting a cell region included in an image captured at the imaging distance determined in the imaging distance determination step; and
([0414]: Wagner discloses that, after capturing brightfield images of a cell culture at multiple Z levels near optimal focus, a cell locator segments the image to identify cells or nuclei and extracts their center coordinates and nuclear envelopes from the segmented image, including by thresholding the predicted image and applying watershed morphological image processing to determine the centroid of each nucleus and the corresponding nuclear envelope, thereby extracting a cell region from the captured image.)
a region correction step of performing correction on the cell region extracted in the region extraction step.
(Wagner, next in [0414] & [0417], Wagner discloses that, following extraction / segmentation of cell- or nucleus-associated regions from brightfield z-stack image data, the resulting image / segmentation is thresholded and subjected to watershed morphological image processing to determine the corresponding nuclear envelope, and further discloses image-based post-processing and refinement of semantic segmentation masks. Thereby teaching correction of the extracted cell region for subsequent analysis.)
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify Yorav-Raphael to perform the claimed region extraction and region correction using Wagner, because Yorav-Raphael already determines the appropriate imaging distance / focus configuration for a cell-sample image based on image-derived contrast behavior across depth levels, and Wagner teaches known techniques for extracting cell- or nucleus-associated regions from brightfield multi-Z image data and refining those regions by thresholding, watershed processing, and segmentation-mask post-processing. Applying Wagner’s known extraction and correction techniques to Yorav-Raphael’s selected-focus image would merely have used known image-segmentation and refinement methods in the same cell-imaging environment to improve delineation of the cell region, yielding the predictable result of more accurate and reliable cell-region analysis.
Regarding claim 3, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 1, wherein the index information acquisition step includes setting a rectangular region as a cell-containing region, the rectangular region including the cell region for each of the plurality of images.
(Yorav-Raphael [0063-0064]: the diagnostic fields / a portion of diagnostic field for analysis, the focus fields and the mapping fields in [Fig. 2A] are depicted as rectangular shapes. Thus, Yorav-Raphael teaches setting a rectangular region as a localized cell-containing region for each of the plurality of images.)
Regarding claim 4, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 3, wherein the index information acquisition step includes setting two or more rectangular regions that are different from each other for each of the plurality of images.
(Yorav-Raphael [0069-0071]: discloses a plurality of focus analysis regions within a mapping field, including a 3×3 grid of rectangle-shaped focus analysis regions, and further teaches that the number of focus analysis regions within a given mapping field may range from 2 to 1000, with more specific examples including 9 to 120 focus analysis regions, and one illustrated embodiment having 35 identical rectangular focus analysis regions for images captured by a depth scan. [0129]: a rectangular diagnostic field, split to twelve focus analysis regions. Thus, Yorav-Raphael teaches setting two or more different rectangular regions for each of the plurality of images.)
Regarding claim 5, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 4, wherein the imaging distance determination step includes calculating at least one value selected from a mean value, a median value, and a mode value of the imaging distances determined for the two or more rectangular regions.
( [0010], [0014], & [0137]: Yorav-Raphael teaches: (i) defining a plurality of rectangular regions, (ii) determining an imaging distance (reference depth level) for each region based on a contrast function, and (iii) using those per-region imaging distances collectively to derive an overall focus relationship across the field (e.g., plane fitting). Yorav-Raphael already computes per-region focus configurations using a contrast function based on variance / standard deviation / derivatives; to calculate those representative statistical values, a posita has to obtain statistic values for a mean value, a median value, and a mode value, by operating statistical functions. A posita can routinely use simple statistics (mean/median/mode) over multiple measured depths to obtain a robust single focus distance for a field, especially in noisy or heterogeneous samples. )
Regarding claim 6, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 1, wherein the region correction step includes determining a correction method to be used for correction of the cell region.
(Wagner, in [0414] and [0417], teaches applying thresholding and watershed morphological image processing to the extracted image data to determine the corresponding nuclear envelope, and further teaches that the cell locator and cell feature predictor may utilize a range of processing algorithms, including image-based post-processing and refinement of semantic segmentation masks derived from deep-learning models, thereby teaching the use of a correction/refinement method for correction of the cell region.)
Regarding claims 15 and 17-18, the rationale provided for claim 1 is incorporated herein. In addition, the method of claim 1 corresponds to the non-transitory storage medium of claim 15, as well as the device of claim 17-18, and performs the steps disclosed herein. Therefore, the claims are all ineligible.
Claim 2 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael [as modified by Wagner], and further in view of Mohan (Mohan et al., US 2014/0193892 A1, 2014).
Regarding claim 2, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 1, wherein the index information acquisition step includes determining a cell-containing region for each of the plurality of images acquired in the image acquisition step and extracting the cell region from the cell-containing region, and
(Yorav-Raphael, in [0069-0071] and [0129], teaches dividing the mapping field / diagnostic field into a plurality of focus analysis regions for images of the cell sample obtained by a depth scan, thereby determining cell-containing regions in the acquired images, and in [0139-0140] and [0142-0143] teaches selecting a portion of the diagnostic field for analysis and using the determined focus configuration for that portion, thereby extracting a localized cell region from the determined cell-containing region.)
Yorav-Raphael [as modified by Wagner] however fails to teach where Mohan teaches wherein the index information is an area of the cell region.
(Mohan, in [0273], teaches that watershed segmentation is performed on each image acquired for the field of view, the cell ROI areas are compared across all images, and the ROI with the largest area is recorded as the final cell ROI for that nucleus; that a cell ROI is defined as the region interior to the contour and is used to compute shape and size metrics such as area, volume, and circularity [0295]; thereby teaching that the image-derived information includes the area of the cell region.)
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify Yorav-Raphael [as modified by Wagner] to use the area of the cell region as the claimed index information in view of Mohan, because Yorav-Raphael [as modified by Wagner] already determines localized cell-containing regions / cell regions from images of a cell sample, and Mohan teaches that segmented cell ROI area is a standard quantitative feature that can be compared across multiple acquired images and used to identify a final ROI for the nucleus. Using Mohan’s known area metric in Yorav-Raphael [as modified by Wagner] would merely have substituted one known image-derived descriptor for use in evaluating extracted cell regions, with the predictable result of providing a quantitative basis for comparing and analyzing the extracted cell regions.
Claim 7 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael [as modified by Wagner], and further in view of Grover (Grover et al., US 2019/0053790 A1, 2019).
Regarding claim 7, Yorav-Raphael [as modified by Wagner] fails to teach where Grover teaches the cell image analysis method according to claim 6, wherein the region correction step includes determining the correction method by selecting from contraction processing, a level-set method, and an active contour method.
(Grover, in [0037-0038] & [0053], teaches applying morphological techniques such as erosion, dilation, and filling to formulate and extract ROIs, and further teaches alternative algorithms including level set segmentation and active contours. Accordingly, Grover teaches that the region correction / extraction method may be chosen from these known candidate methods, including erosion corresponding to contraction processing, level set segmentation, and active contours.)
It would have been obvious to a person of ordinary skill in the art, at the time of the invention, to modify Yorav-Raphael [as modified by Wagner]’s cell image analysis method to include selecting the region correction / extraction method from known candidate image-processing methods taught by Grover, including erosion (i.e., contraction processing), level set segmentation, and active contours; because Grover teaches that such methods are recognized alternatives for formulating and extracting ROIs from image data. A person of ordinary skill in the art would have been motivated to incorporate Grover’s known alternative region correction / segmentation methods into Yorav-Raphael [as modified by Wagner] in order to improve the accuracy and robustness of delineating cell regions having different shapes, boundaries, and image characteristics. The modification would have involved nothing more than the predictable use of known image-processing techniques for their established functions in the analogous context of correcting / extracting image regions, and would have yielded no more than predictable results.
Claim 8 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael [as modified by Wagner], and further in view of Ching (Ching et al., US 2022/0058370 A1, 2022).
Regarding claim 8, Yorav-Raphael [as modified by Wagner] fails to teach where Ching teaches the cell image analysis method according to claim 6, wherein the region correction step includes determining the correction method based on at least one of a circularity or a solidity of the cell region.
(Ching , [0099], teaches determining whether a region or segment satisfies a circularity threshold and a solidity threshold, and, when the thresholds are satisfied, correcting a corresponding region to incorporate the segment as a single cell region; and in [0101], teaches correcting a corresponding region to incorporate the segment as a single cell region when the thresholds are satisfied. Thus, Ching teaches determining how to handle / correct the region based on at least one of circularity or solidity of the region.)
It would have been obvious to a person of ordinary skill in the art, at the time of the invention, to modify Yorav-Raphael [as modified by Wagner] to determine the region correction method based on circularity and/or solidity, as taught by Ching, because Yorav-Raphael [as modified by Wagner] already performs image-based analysis of localized cell regions, and Ching teaches that circularity thresholds and solidity thresholds are useful criteria for deciding whether a segment should be accepted and incorporated into a corresponding region as a single cell region. Using Ching’s known morphology-based decision criteria in Yorav-Raphael [as modified by Wagner] would have been a simple substitution of one known region-handling criterion for another to obtain the expected benefit of improved cell-region correction and more reliable distinction of valid single-cell regions from unsuitable regions.
Claim 9 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael [as modified by Wagner], in view of Grover, and further in view of Roach (Roach et al., US 2014/0032406 A1, 2014).
Regarding claim 9, Yorav-Raphael [as modified by Wagner] teaches the cell image analysis method according to claim 1,
Yorav-Raphael [as modified by Wagner] however fails to teach where Grover teaches wherein the correction in the region correction step is performed by contraction processing, and
(Grover, at [0053], teaches applying morphological techniques including erosion to formulate and extract ROIs; because erosion contracts image regions, Grover teaches region correction by contraction processing.)
It would have been obvious to a person of ordinary skill in the art, at the time of the invention, to incorporate Grover’s erosion-based contraction processing into Yorav-Raphael [as modified by Wagner]’s region-correction workflow as a known alternative image-region refinement technique to achieve the predictable result of improving correction / refinement of the extracted region.
However, Yorav-Raphael [as modified by Wagner and Grover] additionally fails to teach where Asai teaches wherein the number of reduced pixels in the contraction processing is the number of pixels determined based on images of a dot pattern captured at the plurality of the imaging distances that are different from each other.
(Asai, in [Abstract] & [0013–0017], discloses a thinning / contraction process in which a set number of pixels is determined according to the width by which a dot protrudes from a record pixel / the width over which the dots extend beyond the recording pixels, thereby teaching determination of the contraction amount from dot-related image information.
It would have been obvious to a person of ordinary skill in the art, at the time of the invention, to modify Yorav-Raphael [as modified by Wagner and Grover]’s cell-image-analysis method to determine the amount of contraction processing using Asai, because Asai teaches obtaining a shrunk image by reducing pixels from the edge of an object image and determining the number of reduced pixels based on the protrusion width of a dot from a record pixel. A person of ordinary skill in the art would have been motivated to apply Asai’s known pixel-setting technique to Yorav-Raphael’s region-correction workflow in order to calibrate the amount of shrinkage according to image geometry and thereby improve the predictability and consistency of the correction result. Such a modification would have involved only the predictable use of a known image-shrinking control technique for its established purpose).
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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