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 of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement 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.
Claim(s) 1, 2, 4, 5, 9, 11, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Min et al. (US 2023/0252755; hereinafter Min).
Regarding claim 1:
Min discloses an image processing device (see Figs. 1 and 6), comprising:
a processor (see Fig. 6, processor 220); and
a storage device (see Fig. 6, storage unit 240) storing one or more programs, which, when executed by the processor, cause the processor to perform:
acquiring a target image in which fibers are captured (see Fig. 1; image is acquired by an imaging device; also see paragraph [0028]; imaging device is capable of capturing objects such as fibers);
generating an individual-object segmentation result in which each of the fibers included in the target image is detected using a trained individual-object segmentation model (see Figs. 1-2 and paragraphs [0029]-[0030] and [0036]; instance head 20 is extracting objects/fibers included in the target image);
generating a category segmentation result in which regions where the fibers are captured are recognized in the target image using a trained category segmentation model (see Fig. 1 and paragraph [0031]; semantic head 30 is generating a category segmentation result of the regions where the objects/fibers are captured);
correcting the individual-object segmentation result with the category segmentation result (see Fig. 1; the output of the semantic head 30 is being inputted into the complexity block 40 which is being inputted into the instance head 20; as such, the CPS and Xthing information are interpreted as “correcting the individual-object segmentation result with the category segmentation result”); and
outputting a correction result of the individual-object segmentation result (see Fig. 1; the result is being outputted from instance head 20).
Regarding claim 2:
Min discloses the image processing device according to claim 1, wherein the correcting includes calculating a logical conjunction of the individual-object segmentation result and the category segmentation result to generate the correction result (see Fig. 1; mask logistics and semantic logistics are calculated).
Regarding claim 4:
Min discloses the image processing device according claim 1, wherein the individual-object segmentation model is a model of performing instance segmentation (see paragraph [0030]), and the category segmentation model is a model of performing semantic segmentation (see paragraph [0031]).
Regarding claim 5:
Min discloses the image processing device according to claim 4, wherein the individual-object segmentation model is Mask R-CNN or YOLACT (see paragraph [0035]; instance segmentation method is a mask-RCNN).
Regarding claim 9:
Min discloses the image processing device according to claim 1, wherein the correcting includes correcting regions where the fibers are detected in the individual-object segmentation result (see Fig. 1; the correcting is inherently being applied to the regions where the fibers/objects are detected).
Regarding claims 11 and 12:
Claims 11 and 12 each recites similar limitations as in claim 1. Hence, claims 11 and 12 are rejected under the same reason as discussed above in claim 1.
Claim Rejections - 35 USC § 103
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.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min in view of Bangalath et al. (US 2024/0203085; hereinafter Bangalath).
Regarding claim 6:
Min discloses all the features in claim 1. Min does not disclose the image processing device, wherein the individual-object segmentation model allows a size of a bounding box to be adjustable.
However, in the same field of endeavor, Bangalath discloses an imaging processing device, wherein the individual-object segmentation model allows a size of a bounding box to be adjustable (see Figs. 8B, 9B or 10A; the bounding box of each object are different for each object).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of Min and Banaglath such that the individual-object segmentation model allows a size of a bounding box to be adjustable. The combination would have yielded a predictable result of accurately detecting the objects in the input image.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Min in view of Wang et al. (US 2023/0401790; hereinafter Wang).
Regarding claim 8:
Min discloses all the features in claim 4. Min does not disclose the image processing device, wherein the category segmentation model is DeepLab or U-Net.
In the same field of endeavor, Wang discloses an image processing device, wherein the category segmentation model is DeepLab or U-Net (see paragraph [0056]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of Min and Wang such that the category segmentation model is DeepLab or U-Net. The combination would have yielded a predictable result of improving the image segmentation by using well known model (see Wang, paragraph [-107]).
Allowable Subject Matter
Claims 3, 7 and 10 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.
In regards to claim 3, none of the reference of record alone or in combination discloses or suggests the image processing device according claim 1, wherein the correcting includes selecting the individual-object segmentation result or the category segmentation result for each unit of the target image based on a result of a comparison between a score of the individual-object segmentation result and a score of the category segmentation result to generate the correction result.
In regards to claim 7, none of the reference of record alone or in combination discloses or suggests the image processing device according to claim 4. wherein the individual-object segmentation model allows a size of a mask of each individual object to be adjustable.
In regards to claim 10, none of the reference of record alone or in combination discloses or suggests the image processing device according to claim 9, wherein the correcting includes expanding a region segmented per individual object through dilation or smoothing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. (US 2022/0261593) discloses a system using neural networks to perform object detection, instance segmentation, and semantic correspondence from bounding box supervision.
Price et al. (US 2022/0237799) discloses a system for segmenting objects in digital images utilizing a multi-object segmentation model framework.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIXI CHOW SIMPSON whose telephone number is (571)272-7571. The examiner can normally be reached Mon-Fri 7:00am-3:00pm.
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/LIXI C SIMPSON/Primary Examiner, Art Unit 2625
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