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 .
The instant application having Application No. 18/814,792 filed on 8/26/2024 is presented for examination by the examiner.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 19, 32, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Li “Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy”, in view of Akashi (JP 2017083790 A)(see attached machine translation).
Regarding claim 19, Li discloses a focus adjustment method, in at least Figure 5, comprising:
obtaining at least two first microscopic images (last paragraph of page 2 – first paragraph of page 3 states "autofocus methods have been implemented whereby the microscope captures a stack of images (10 - 20) at different defocus positions") by performing image capturing of a first subject using a microscope (last paragraph of page 2 – first paragraph of page 3 states "autofocus methods have been implemented whereby the microscope captures a stack of images (10 - 20) at different defocus positions", page 3, paragraph 2 states "Several studies have used deep learning to perform autofocus, mostly on histopathology slides that were acquired by a bright field microscope and using a single frame") including an objective lens (abstract states "A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens … autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet"), the image capturing being performed a plurality of times while changing a position of the objective lens relative to the first subject in an optical axis direction at a predetermined interval (abstract states "autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet", first paragraph of page 2 states "the relative position of the light-sheet and the objective focal plane (Δz) constantly shifts in volume imaging", last paragraph of page 2 – first paragraph of page 3 states "Determining the best position of the objective lens that overlaps with the light-sheet to provide superior image quality can be accomplished by eye. However, this is highly time-consuming and laborious, especially in high throughput platforms that image large numbers of specimens. To solve this problem, autofocus methods have been implemented whereby the microscope captures a stack of images (10 - 20) at different defocus positions", last paragraph of page 4 – first paragraph of page 4 states "The detection objective lens (10×/numerical aperture (NA) 0.6, Olympus; XLPLN10XSVMP-2) was placed on a motorized linear translation stage (Newport; CONEX-TRB12CC with SMC100CC motion controller), which provides 12 mm travel range with ±0.75µm bi-directional repeatability", second paragraph of page 4 states "For every defocused image stack that was used in the training and testing stages (∼420 stacks), first, the objective lens was translated (CONEX-TRB12CC motor) by the user to find the optimal focal plane");
by inputting the at least two first microscopic images to a learned model (abstract states "a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images", page 4, paragraph 3 states "we modified the network to accept multiple defocused images as an input, instead of solely one image. To train the network, 421 defocused image stacks were acquired: 337, 42, and 42 datasets were dedicated for training, validation, and testing respectively", page 6, paragraph 3 states "we tested how the number of defocused images, which were fed into the deep neural network (DNN), influenced the classification accuracy ... when the DNN received two or three defocused images as input, it performed better in terms of classification accuracy than using only one defocused image") configured to estimate a direction of movement of a focus of the objective lens relative to an in-focus position of the first subject (caption of Figure 5 states "The in-focus (Δz=0) images of neurons and hair cells, respectively. (a2 and b2) Images that show the same field of view as in a1 and b1 after the objective lens is displaced by 30 µm and −30 µm, respectively. (a3 and b3) Images of the same field of view after the objective is moved according to the network defocus evaluation as shown in a4 and b4. The improved image quality in a3 and b3 indicates that the network can estimate the defocus level and adjust the detection focal plane to improve image quality. In a and b, the white boxes mark the location of the zoom-in images, and the color-coded line profiles in a4 and b4 represent image intensities along the dashed lines in a and b"), estimating the direction of movement (caption of Figure 5); and
processing to move the focus relative to the first subject based on the direction of movement (caption of Figure 5).
However, Li does not disclose using a stop brought to a size smaller than a fully open state.
Akashi teaches using a stop brought to a size smaller than a fully open state (page 6, paragraph 8 of translation states "In normal bright field observation, the coherent factor is generally set to about 0.8 in order to balance the contrast and resolution of the observation image. In contrast, in this embodiment, the aperture stop aperture is adjusted so that the coherent factor is smaller than 0.8. Thereby, the image processing apparatus 102 increases the coherence of the transmitted illumination light, acquires image data including phase information in the specimen, and enables the phase information to be observed as a contrast (phase information image). More preferably, the aperture of the aperture stop 303 is adjusted so that the coherent factor is smaller than 0.5. By making the coherent factor smaller than 0.5, higher contrast can be obtained. Note that bright field observation in a state where the aperture stop is stopped is hereinafter referred to as small aperture bright field observation", page 8, paragraph 2 of translation "the aperture stop is narrowed down to enable small aperture bright field observation").
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the focus adjustment method of Li modified by using a stop brought to a size smaller than a fully open state, as taught by Akashi, in order to obtain a higher contrast (page 6, paragraph 8 of translation).
Regarding claim 32, the combination of Li and Akashi disclose all the limitations of claim 19 and Li further discloses a recording medium (“classification network”) storing a program that causes a computer to execute the focus adjustment method according to claim 19 (caption of Figure 1 states “During image acquisition, two defocused images will be collected and sent to a classification network to estimate the defocus level”, page 3, paragraph 2 states “Yang et al. proposed using a classification network to perform autofocus in thin fluorescence samples using a single shot, and their results outperformed traditional image metrics”, page 3, paragraph 3 states “The use of multiple images accelerates the network’s training and provides results that are more accurate”, page 4, paragraphs 3 and 4).
Regarding claim 33, the combination of Li and Akashi disclose all the limitations of claim 19 and Li further discloses an apparatus comprising a processor configured to execute the focus adjustment method according to claim 19 (page 3, paragraph 2, and page 4, paragraphs 3 and 4).
Allowable Subject Matter
Claims 20-31 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 20, the combination of Li and Akashi disclose all the limitations of claim 19, however Li does not disclose wherein the estimating includes generating a plurality of first partial images by dividing each image of the at least two first microscopic images, and inputting the plurality of first partial images to the learned model to have the learned model estimate a position of the focus of the objective lens by estimating the direction of movement with respect to the plurality of first partial images.
Yamashita (JP 2000193888 A) teaches wherein the estimating includes generating a plurality of first partial images by dividing each image of the at least two first microscopic images (page 2, paragraph 9 of translation states "A microscope apparatus is characterized in that it is divided into a plurality of partial images, each of the partial images is captured by an image sensor, and the plurality of partial images are combined to generate a high-resolution composite image").
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the focus adjustment method of Li modified by wherein the estimating includes generating a plurality of first partial images by dividing each image of the at least two first microscopic images, as taught by Yamashita, in order to generate a high-resolution image (page 2, paragraph 9 of translation).
However, Li “Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy”, Akashi (JP 2017083790 A), and Yamashita (JP 2000193888 A), either singularly or in combination, do not disclose or suggest inputting the plurality of first partial images to the learned model to have the learned model estimate a position of the focus of the objective lens by estimating the direction of movement with respect to the plurality of first partial images, among other claim limitations.
Claims 21-31 depend on claim 20, so they are allowable for the same reasons.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAINA M SWANSON whose telephone number is (703)756-5809. The examiner can normally be reached Mon-Fri, 7:30am-4:00pm.
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/ALAINA MARIE SWANSON/Examiner, Art Unit 2872
/WILLIAM R ALEXANDER/ Primary Examiner, Art Unit 2872
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