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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/20/2024 is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang (US 20250029255 A1).
Regarding claims 1, 12 and 13, Wang discloses at least one memory configured to store instructions ([0008] The present disclosure provides a machine-readable storage medium storing computer instructions, and when the computer instructions are called by one or more processors,); and
at least one processor configured to execute the instructions to ([0008] The present disclosure provides a machine-readable storage medium storing computer instructions, and when the computer instructions are called by one or more processors,):
acquire an endoscopic image obtained by photographing an examination target by an endoscope ([0028] For example, an endoscope may be inserted into a designated location (i.e., the location to be inspected, that is, the region inside the patient that needs to be inspected, and this designated location is not limited) inside a target object (e.g., a patient or other subjects), to collect images of the designated location inside the target object, and output the images of the designated location inside the target object to a display device and a storage device.);
acquire a proportion of an interest part region in the endoscopic image where a part of interest in the examination target exists (fig. 4, ref 402); and
generate, based on the proportion, a detection result relating to the part of interest in the endoscopic image (fig. 4, ref 403).
Regarding claim 2, Wang discloses acquire a map indicating scores of confidence level that regions in the endoscopic image are the part of interest ([0082] At step S13, the sample image is input to an initial segmentation model, such that the initial segmentation model determines, based on a pixel value corresponding to each pixel point in the sample image respectively, a predicted label value and a predicted probability corresponding to the predicted label value that correspond to each pixel point respectively, where the predicted label value corresponding to the pixel point in the sample image may be the first value or the second value.); and
set a threshold value of the scores for determining a presence or absence of the part of interest ([0072] the target segmentation model determines, based on a pixel value corresponding to each pixel point in the to-be-detected image respectively, a predicted label value corresponding to each pixel point in the to-be-detected image respectively, where the predicted label value corresponding to each pixel point in the to-be-detected image is the first value or the second value, the first value is configured to indicate that the pixel point is a pixel point corresponding to the diseased tissue, and the second value is configured to indicate that the pixel point is not a pixel point corresponding to the diseased tissue.).
Regarding claim 3, Wang discloses wherein the at least one processor is configured to execute the instructions to set the threshold value such that the proportion of the interest part region to be determined by the map and the threshold value is equal to or smaller than, or equal to or larger than, the acquired proportion ([0064] if the pixel value (e.g., brightness value) corresponding to the pixel point is larger than the first threshold, determining that the pixel point is a target pixel point; if the pixel value corresponding to the pixel point is smaller than or equal to the first threshold, determining that the pixel point is not a target pixel point.).
Regarding claim 4, Wang discloses wherein the at least one processor is configured to execute the instructions to set the threshold value such that the proportion of the interest part region to be determined by the map and the threshold value is equal to or smaller than, or equal to or larger than, a predetermined proportion in accordance with the acquired proportion ("[0058] For example, the color-mapped fluorescence image may be superimposed on the visible light image for display, thereby obtaining the fused image, i.e., the visible light image is dyed based on the fluorescence image, and then achieving the effect of displaying the development region of the fluorescence image on the visible light image.
[0070] Therefore, for each pixel point in the to-be-detected image, if the pixel value corresponding to the pixel point is smaller than the second threshold, it may be determined that the pixel point is the target pixel point, and if the pixel value corresponding to the pixel point is larger than or equal to the second threshold, it may be determined that the pixel point is not a target pixel point.").
Regarding claim 5, Wang discloses wherein the at least one processor is configured to execute the instructions to set a value predetermined in accordance with the proportion as the threshold value ([0067] Based on the above principle, a threshold may be pre-configured as the first threshold. The first threshold may be configured based on experiences and is not limited. Based on the first threshold, the boundary between the development region and the non-development region may be represented, and the development region and the non-development region in the to-be-detected image may be distinguished based on the first threshold. Based on this, since the brightness value corresponding to the diseased tissue is relatively large and the brightness value corresponding to the normal tissue is relatively small, the pixel point with a large brightness value may be used as a target pixel point. Therefore, for each pixel point in the to-be-detected image, if the pixel value corresponding to the pixel point is larger than the first threshold, it may be determined that the pixel point is the target pixel point. and if the pixel value corresponding to the pixel point is smaller than or equal to the first threshold, it may be determined that the pixel point is not a target pixel point.).
Regarding claim 6, Wang discloses wherein the at least one processor is configured to execute the instructions to acquire one or more candidates for the interest part region in the endoscopic image ([0069] Method 2, if the fluorescence image is a fluorescence image in a negative development mode, i.e., a fluorescence image acquired in the negative development mode, where in the negative development mode, the non-development region of the fluorescence image corresponds to the diseased tissue, then, for each pixel point in the to-be-detected image (i.e., the fluorescence image or the fused image), if the pixel value (e.g., brightness value) corresponding to the pixel point is smaller than the second threshold, determining that the pixel point is a target pixel point, and
to generate the detection result based on the proportion and the candidates ([0074] FIG. 5A relates to the training process and the testing process of the target segmentation model. In the training process, the network training is performed based on the sample image, calibration information, a loss function and a network structure, to obtain the target segmentation model. In the testing process, the testing image (i.e., the to-be-detected image) may be input to the trained target segmentation model, and the target segmentation model performs network reasoning on the to-be-detected image to obtain the segmentation result corresponding to the to-be-detected image, i.e., to distinguish the target pixel points corresponding to the diseased tissue from the to-be-detected image.).
Regarding claim 8, Wang discloses wherein the at least one processor is configured to execute the instructions to generate the detection result in which the candidate is corrected based on the proportion ([0151] input the sample image to the initial segmentation model, such that the initial segmentation model determines a predicted label value corresponding to each pixel point in the to-be-detected image respectively based on the pixel value corresponding to each pixel point in the sample image respectively; where the predicted label value corresponding to the pixel point in the to-be-detected image is the first value or the second value; determine a target loss value based on the calibration label value and the predicted label value corresponding to each pixel point in the to-be-detected image, and train the initial segmentation model based on the target loss value to obtain the target segmentation model.).
Regarding claim 9, Wang discloses wherein the at least one processor is configured to execute the instructions to calculate the proportion based on the endoscopic image and a model in which the endoscopic image is inputted ([0089] For example, the network parameters of the initial segmentation model are adjusted based on the target loss value to obtain an adjusted segmentation model, and the goal of adjusting the network parameters is to make the target loss value smaller and smaller. After obtaining the adjusted segmentation model, the adjusted segmentation model is used as the initial segmentation model, and steps S13-S14 are re-executed until the target loss value meets an optimization goal, and the adjusted segmentation model is used as the target segmentation model.), and
wherein the model is a model which learned, based on machine learning, a relation between an endoscopic image inputted to the model and the interest part region in the inputted endoscopic image ([0089] For example, the network parameters of the initial segmentation model are adjusted based on the target loss value to obtain an adjusted segmentation model, and the goal of adjusting the network parameters is to make the target loss value smaller and smaller. After obtaining the adjusted segmentation model, the adjusted segmentation model is used as the initial segmentation model, and steps S13-S14 are re-executed until the target loss value meets an optimization goal, and the adjusted segmentation model is used as the target segmentation model.).
Regarding claim 10, Wang discloses wherein the at least one processor is configured to execute the instructions to acquire, as the proportion, the proportion of an area of a lesion part of the examination target in the endoscopic image ([0115] For example, the target boundary feature may include but is not limited to a target color and/or a target line type, and the target boundary feature is not limited, which may be any feature used to display the to-be-cut boundary.), and
wherein the at least one processor is configured to execute the instructions to generate a detection result relating to the lesion part as the detection result ([0074] the testing image (i.e., the to-be-detected image) may be input to the trained target segmentation model, and the target segmentation model performs network reasoning on the to-be-detected image to obtain the segmentation result corresponding to the to-be-detected image, i.e., to distinguish the target pixel points corresponding to the diseased tissue from the to-be-detected image.).
Regarding claim 11, Wang discloses wherein the at least one processor is configured to execute the instructions to display information based on the endoscopic image and the detection result on a display device ([0112] After the target image is obtained, the target image may be displayed on the screen for medical staff to view. Since the to-be-cut boundary is the boundary of the focus region, medical staff may clearly see the boundary of the focus region from the target image, and the boundary of the focus region may provide a reference for cutting.).
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.
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.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20250029255 A1) and further in view of Sugita (US 20210000326 A1).
Regarding claim 7, Wang does not explicitly disclose but in a similar field of endeavor of endoscopic imaging, Sugita teaches wherein, if there are a plurality of the candidates, the at least one processor is configured to execute the instructions to select, based on the proportion, a candidate therefrom as the detection result ([0100] More specifically, for example, the display control section 134 sets, based on the evaluation result of step S33 in FIG. 9, a marker image M32 for highlighting a position of the lesion candidate region L32 having the highest seriousness degree among the lesion candidate regions L31, L32, and L33.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the known system of lesion detection using endoscopic imaging, as discloses by Wang, with the known teaching of multiple lesion detection, as taught by Sugita in order to yield the predictable results of detecting multiple lesions and highlighting the lesion of most concern so that a doctor may determine the seriousness of the lesion.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210398304 A1 with respect to claim 4: [0095] The map generation section 22 next detects feature points, which are characteristic pixels, out of the image data K(x,y,t) and the image data K(x,y,t+Δt). The term, “feature point” means, for example, a pixel having a pixel value different by a predetermined value or greater from that of the adjacent pixels. It is to be noted that the feature points are desirably points which stably exist even after an elapse of time, and that as the feature points, pixels defining edges in the images are frequently used, for example.
US 20140247977 A1 with respect to claim 2: [0067] FIGS. 7(a)-(c) depict an example of multi-atlas ABAS segmentation results. FIG. 7(a) shows the estimated structure borders from different atlas label maps (where the structure is a right parotid gland) as contours 700. FIG. 7(b) generally indicates where these different atlas label maps disagree as region 702. FIG. 7(c) shows how the various atlas label maps can be combined to generate a structure probability map via label fusion techniques.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED A NASHER whose telephone number is (571)272-1885. The examiner can normally be reached Mon - Fri 0800 - 1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AHMED A NASHER/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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