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 .
Response to Amendment
The amendment filed September 1, 2026 has been entered. Claims 1–19 are pending. Claim 1 has been amended, and claim 21 has been added. Claims 10–14 and 16 remain withdrawn from consideration.
Election/Restrictions
Newly added claim 19 has been reviewed and is readable on elected Species I. Claim 19 recites the cell-image-analysis sequence of image acquisition, index acquisition, imaging-distance determination, cell-region extraction, and region correction, with an additional limitation specifying selection of the qualifying imaging distance having the smallest absolute value. This limitation further defines the imaging-distance determination within the elected subject matter. Accordingly, claim 19 will be examined on the merits with the other claims directed to Species I.
Response to Arguments
Applicant argues, at pages 11–13 of the Remarks, that Yorav-Raphael seeks to determine or improve focus and therefore does not suggest intentionally selecting a defocused imaging distance for cell-region extraction. Applicant further argues that Wagner’s segmentation and image-processing disclosures do not provide a reason to modify Yorav-Raphael in the claimed manner. The argument that the references provide no reason to use defocused image information is not persuasive. Wagner, paragraphs [0175–0176] and [Figure 3], expressly discusses differences between images obtained at the focal plane and images obtained at displaced Z positions. Wagner explains that an image obtained approximately at the cell plane principally provides information concerning small features and cell edges, whereas images obtained at displaced positions provide information concerning larger cellular structures. Wagner identifies this additional information as useful for cell analysis. Wagner, paragraph [0414], further discloses segmenting cell-culture images to identify cells or nuclei and obtaining corresponding coordinates and nuclear envelopes. Its illustrated implementation processes brightfield images acquired at multiple Z positions to generate a predicted nuclear image, followed by thresholding and watershed processing. Paragraph [0417] separately discloses subsequent post-processing and refinement of the resulting semantic segmentation masks.
Applicant’s argument that such use necessarily defeats Yorav-Raphael’s purpose is also unpersuasive. Yorav-Raphael’s focus analysis can establish a reference for selecting an imaging position, while Wagner supplies a reason to use image information obtained at a displaced position for subsequent analysis. Yorav-Raphael, paragraph [0145], also permits an investigation depth that differs from the reference depth. Physical defocus is supported by Wagner’s express discussion of displaced imaging positions, rather than inferred solely from a difference between reference and investigation depths.
Applicant’s statement that claims 17–18 depend from claim 1 is inaccurate. Claim 17 is an independent device claim, and claim 18 depends from claim 17. Accordingly, those claims are evaluated according to their own limitations rather than treated as automatically incorporating the amendment to claim 1.
Applicant notes, at page 14 of the Remarks, that the previous rejection heading identifies Roach while the accompanying analysis attributes the dot-protrusion teaching to Asai. Applicant’s request for clarification is well taken. The heading mistakenly identified Roach; the accompanying analysis relied on Asai. The intended reference is Asai et al., JP2014147009A, published August 14, 2014. The reference identification is corrected accordingly, Asai was also included in PTO-892 form and Asai’ document was attached in the previous action. Grover discloses morphological erosion for image-region processing.
Applicant requests, at page 15 of the Remarks, examination of newly added claim 19 with the elected claims. Claim 19 recites the cell-region extraction and correction subject matter of the elected invention and is considered with that invention. Applicant further argues that claim 19 selects the entry point of a stable plateau closest to the focal position, whereas Yorav-Raphael selects a local minimum. Claim 19 expressly requires selecting the imaging distance having the smallest absolute value among distances satisfying the rate-threshold condition. It does not expressly require an extended plateau or exclude an imaging distance of zero. The specification, paragraph [0041], identifies zero imaging distance with the focal position.
Based on these facts, this action is made FINAL.
Claim Objections
Claim 1 is objected to because of the following informalities: in the first added “wherein” clause, the term “defocused image capturing distance” is introduced without an article. For consistent introduction of this term, insert “a” immediately before “defocused image capturing distance”. The corrected clause should read: “wherein the imaging distance determination step includes determining a defocused image capturing distance shifted from a focal point position”.
Appropriate correction is required.
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.
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 does not expressly disclose extracting and subsequently correcting a cell region from an image captured at the determined distance, and does not expressly characterize its shifted investigation level as a defocused image-capturing distance relative to the cell’s focal-point position. Wagner teaches the usefulness of physical defocus and the corresponding cell-region processing:
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. )
wherein the imaging distance determination step includes determining defocused image capturing distance shifted from a focal point position.
( Wagner, [0175–0176] and [Figure 3], expressly distinguish the cell’s in-focus plane at approximately Z = 0 from image-capturing positions displaced in the positive and negative Z directions. Wagner further teaches that, as the imaging position moves away from the in-focus plane, larger cellular structures become resolvable in the resulting intensity images. Wagner [0408] provides a specific example of capturing brightfield images at Z = −5 μm, Z = 0, and Z = +5 μm.
In view of Wagner’s teaching, implementing Yorav-Raphael’s shifted investigation level as a physically defocused image-capturing distance and to select a useful off-focus contrast peak for cell-region analysis. The stationary-point determination described above is applied to that off-focus contrast peak so that the rate condition is satisfied at the same defocused imaging distance selected for image capture and region extraction. )
wherein the region extraction step includes extracting the cell region from the defocused image captured at the defocused image capturing distance determined at the imaging distance determination step.
( Wagner, [0175–0176], teaches that images captured at positions displaced from the in-focus plane contain information concerning larger cellular structures, while [0414] teaches segmenting image information to identify and extract cell or nuclear regions. Wagner [0421] further teaches that derived cell features can be traced back to specific pixel regions in the image data.
In the proposed combination, the image acquired at the physically defocused contrast maximum determined in the imaging-distance determination step is supplied to Wagner’s cell-segmentation processing. The resulting segmentation mask therefore identifies the cell region represented in the image captured at that selected defocused distance. The extracted mask is subsequently refined as taught by Wagner [0417]. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Yorav-Raphael’s shifted investigation-level selection in view of Wagner’s teaching that defocused brightfield images reveal larger cellular structures, and to apply Wagner’s segmentation and mask refinement to the selected image to identify and correct the corresponding cell region. Locating the off-focus contrast maximum through a stationary-point calculation would determine the desired imaging position between scan samples. A predetermined zero-slope criterion would satisfy the rate condition at that selected distance. Yorav-Raphael’s reference level would remain the positioning baseline. A skilled artisan would have reasonably expected success because both references concern brightfield cell imaging, and Wagner’s segmentation and refinement process the image data supplied by the modified acquisition method. The combination would predictably produce a corrected cell-region mask from the selected defocused image.
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 rejected.
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, before the effective filing date of the claimed invention, to select Grover’s erosion technique for Yorav-Raphael [as modified by Wagner]’s refinement of the extracted cell-region mask, thereby reducing noise-related foreground portions through contraction. Grover’s technique addresses refinement of image regions and would perform the same morphological operation on the combined method’s extracted cell-region mask.
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, in [0099–0101], discloses evaluating the circularity and solidity of image segments rejected during watershed segmentation. When a segment fails a threshold, the system treats it as a separation line and skips further processing. When the segment satisfies both thresholds, the system evaluates its overlap with watershed regions and whether it contains a unique nucleus. If it contains a unique nucleus, the system corrects the corresponding region by incorporating the segment as a single-cell region. Paragraphs [0102–0103] disclose additional merging or division-and-merging operations based on overlap. Thus, Ching discloses determining the treatment of an extracted image region through a correction procedure conditioned on circularity and solidity, together with nucleus and overlap information. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Ching’s circularity and solidity tests into Yorav-Raphael [as modified by Wagner]’s post-segmentation refinement, thereby determining whether a rejected segment should undergo incorporation or merging instead of being retained as a separation line. This would apply Ching’s disclosed correction procedure to the combined method’s cell-region masks to recover cell portions rejected during segmentation.
Claim 9 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael [as modified by Wagner], in view of Grover (Grover et al., US 2019/0053790 A1, 2019), and further in view of Asai (Asai et al., JP6021662B2, 2016).
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.
Claim 19 is rejected under 35 U.S.C. §103 as being unpatentable over Yorav-Raphael, in view of Wagner, further in view of Lang (Lang et al, US 3721759 A, 1973).
Regarding claim 19, the findings and rationale provided for the corresponding image acquisition, index information acquisition, imaging distance determination, region extraction, and region correction limitations of claim 1 are incorporated herein, subject to the further modification of the imaging-distance determination. Claim 19 additionally recites limitation that Lang discloses:
wherein the imaging distance determination step includes determining, from among imaging distances at which the rate of a change in index information with respect to the change in the imaging distance is equal to or less than a predetermined threshold, an imaging distance having the smallest absolute value.
( Lang, at [column 5, lines 10–31], and [Figure 5], discloses moving the microscope stage at constant speed, differentiating a signal proportional to the measured image area, and using a comparator to stop the stage when the differentiated signal reaches zero. Column 6, lines 10–22, identifies the resulting position as placing the object in the focal plane of the viewing optics.
During constant-speed movement, the time derivative equals the derivative with respect to axial position multiplied by the nonzero scanning velocity. Therefore, the disclosed zero time derivative also establishes a zero rate of change of the image-area index with respect to imaging distance. The predetermined zero-crossing criterion supplies the zero-r3ate criterion in the proposed combination. At the selected focal position, the relative displacement between the object and the optical focal plane is zero. Because no absolute imaging distance can be less than zero, that qualifying imaging distance necessarily has the smallest absolute value among the qualifying imaging distances. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to use Lang’s image-area measurement and zero-crossing focus criterion in Yorav-Raphael [as modified by Wagner]’s imaging-distance determination, followed by cell-region extraction and mask refinement, to obtain a reproducible focused image while limiting defocus-related enlargement of the measured image region. Applying Lang’s disclosed focusing procedure would predictably select the zero-defocus position satisfying the zero-rate criterion, thereby providing the qualifying imaging distance having the smallest absolute value.
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.
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
