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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered.
Claim Amendments
Acknowledgment of receiving amendments to the claims, which were received by the Office on 04/21/2026.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the same combination of references being used in the current rejection. Applicant’s arguments are directed solely to the claimed invention as amended 04/21/2026, which has been rejected under new ground of rejection necessitated by amendment. See rejection below for full detail.
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) 1-5 and 12-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sakita et al. (US 2014/0184859 A1).
Regarding claim 1, Sakita et al. (hereafter referred as Sakita) teaches a camera device (Sakita, Fig. 1), comprising:
a camera imager (Sakita, Fig. 1, imaging element 3, Paragraph 0023) with a noncircular aperture (Sakita, Fig. 1, diaphragm 22/27, Paragraphs 0023 and 0057-0059), the camera imager configured to capture image content at a depth of field in an image (Sakita, Paragraphs 0011, 0025 and 0027) , the image content having an identifiable characteristic resulting from the noncircular aperture of the camera imager (Sakita, Paragraphs 0009-0010, 0023, 0027, 0041 and 0057-0059, The identifiable characteristic may be the modulation of the optical image by the optical transfer function from the diaphragm 22/27 (or additionally the phase plate 21).); and
a content manager (Sakita, Figs. 1 and 6, spatial filter processor unit 6, Paragraphs 0026-0027) implemented at least partially in computer hardware and configured to adaptively adjust a perceived depth of field to sharpen at least a portion of the image content in the image based at least in part on the identifiable characteristic resulting from the noncircular aperture of the camera imager and optical point spread functions (PSFs) calculated for the noncircular aperture (Sakita, Paragraphs 0040-0044 and 0049, Removing “fuzziness” of the image is considered to be adjust a perceived depth of field to sharpen at least a portion of the image content.).
Claim 12 is rejected for the same reasons as claim 1.
Regarding claim 2, Sakita teaches the camera device of claim 1 (see claim 1 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to increase the perceived depth of field to compensate for the identifiable characteristic resulting from the noncircular aperture (Sakita, Paragraphs 0040-0044 and 0049, Removing “fuzziness” of the image is considered to be adjust a perceived depth of field to compensate for the identifiable characteristic resulting from the noncircular aperture.).
Claim 13 is rejected for the same reasons as claim 2.
Regarding claim 3, Sakita teaches the camera device of claim 1 (see claim 1 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to utilize optical properties of at least a camera lens of the camera device used to capture the image content (Sakita, Paragraphs 0049, Optical properties of the lens are used in capturing the image and part of the OTF. The image is used when removing “fuzziness”. Therefore, optical properties of the lens are used when adjust the perceived depth of field.).
Claim 14 is rejected for the same reasons as claim 3.
Regarding claim 4, Sakita teaches the camera device of claim 1 (see claim 1 analysis), wherein the identifiable characteristic resulting from the noncircular aperture of the camera imager is a distortion of at least the portion of the image content (Sakita, Paragraphs 0041-0042 and 0057-0059, Fuzziness caused by the optical transfer characteristics of the noncircular aperture are distortions.).
Regarding claim 5, Sakita teaches the camera device of claim 4 (see claim 4 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to increase the perceived depth of field to compensate for the distortion of at least the portion of the image content (Sakita, Paragraph 0049, Removing fuzziness is considered to be increasing the perceived depth of field to compensate for the distortion of at least the portion of the image content.).
Claim 15 is rejected for the same reasons as claim 5.
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) 1-7, 9-10, 12-16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiasa (US 2020/0388014 A1) in view of Chen et al. (US 2019/0355101 A1).
Regarding claim 18, Hiasa teaches a system (Hiasa, Fig. 2), comprising:
a camera imager (Hiasa, Fig. 2, image-capturing apparatus 102/ image sensor 122, Paragraph 0050) with an aperture (Hiasa, Paragraph 0038 and 0055), the camera imager configured to capture image content at a depth of field in an image (Hiasa, Paragraphs 0004 and 0061, The optical system has states of zoom, F-number and object/focus distance. Therefore, images captured have content at a depth of field.), the image content having an identifiable characteristic resulting from the aperture of the camera imager (Hiasa, Paragraphs 0039-0040, 0046 and 0062, Blurs caused by the optical system (including the aperture) are considered to be identifiable characteristic; and
a machine learning model (Hiasa, Fig. 2, learning apparatus 101, Paragraph 0042) configured to compensate for distortion in the image content in the image, the distortion attributable to the identifiable characteristic resulting from the aperture of the camera imager (Hiasa, Paragraphs 0062-0064, Blurs in the image are sharpened.), wherein, to compensate for the distortion, the machine learning model is configured to adjust a perceived depth of field (Hiasa, Paragraphs 0062-0064, Sharpening the image is considered to be increasing the perceived depth of field.) based at least in part on optical point spread functions (PSFs) calculated for the aperture (Hiasa, Paragraphs 0047-0048, Training the machine learning model is performed by applying blurs to an original image with a PSF to generate training images. The PSF is based on the optical system (including the aperture).).
However, Hiasa does not teach a camera imager with a noncircular aperture, the image content having an identifiable characteristic resulting from the noncircular aperture of the camera imager; the distortion attributable to the identifiable characteristic resulting from the noncircular aperture of the camera imager, nor the optical point spread functions (PSFs) calculated for the noncircular aperture.
In reference to Chen et al. (hereafter referred as Chen), Chen teaches a camera imager with a noncircular aperture (Chen, Paragraphs 0029 and 0033), the camera imager configured to capture image content at a depth of field in an image (Chen, Figs 1-3, Paragraphs 0031-0035), the image content having an identifiable characteristic resulting from the noncircular aperture of the camera imager (Chen, Fig. 2, Paragraph 0032, The shape of the blur/pixel spread/distortion is an identifiable characteristic resulting from the noncircular aperture/aperture shape.); and
the distortion attributable to the identifiable characteristic resulting from the noncircular aperture of the camera imager (Chen, Fig. 2, Paragraph 0032, The shape of the blur/pixel spread/distortion is an identifiable characteristic resulting from the noncircular aperture/aperture shape.), and an optical point spread functions (PSFs) calculated for the noncircular aperture (Chen, Paragraphs 0023-0024 and 0027).
These arts are analogous since they are both related to removing blur from images caused by the camera aperture. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the invention of Hiasa with the teaching of correcting for noncircular apertures as seen in Chen to allow the system to correct for a plurality of different aperture shapes such as hexagram, rectangle, heart or additional shapes (Chen, Paragraph 0033).
Claims 1 and 12 are rejected for the same reasons as claim 18.
Regarding claim 19, the combination of Hiasa and Chen teaches the system of claim 18 (see claim 18 analysis), wherein the machine learning model is configured to at least one of correct a blur or reduce noise of at least a portion of the image content (Hiasa, Paragraph 0062).
Claim 6 and 16 are rejected for the same reasons as claim 19.
Regarding claim 20, the combination of Hiasa and Chen teaches the system of claim 18 (see claim 18 analysis), wherein the machine learning model is configured to estimate an inverse of the PSFs used to degrade reference images that are used to invert an effect of the identifiable characteristic resulting from the noncircular aperture of the camera imager (Hiasa, Paragraphs 0047-0048, The PSF is used to generate blurred training images. The machine learning model is trained to reverse the effects of the PSF (inverse the PSF). Chen, Paragraphs 0023-0024 and 0032).
Regarding claim 2, the combination of Hiasa and Chen teaches the camera device of claim 1 (see claim 1 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to increase the perceived depth of field to compensate for the identifiable characteristic resulting from the noncircular aperture (Hiasa, Paragraphs 0062-0064, Sharpening the image is considered to be increasing the perceived depth of field.).
Claim 13 is rejected for the same reasons as claim 2.
Regarding claim 3, the combination of Hiasa and Chen teaches the camera device of claim 1 (see claim 1 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to utilize optical properties of at least a camera lens of the camera device used to capture the image content (Hiasa, Paragraphs 0061, States of the zoom, F-number, and object distance are used to in sharpening (adjust the perceived depth of field).).
Claim 14 is rejected for the same reasons as claim 3.
Regarding claim 4, the combination of Hiasa and Chen teaches the camera device of claim 1 (see claim 1 analysis), wherein the identifiable characteristic resulting from the noncircular aperture of the camera imager is a distortion of at least the portion of the image content (Chen, Fig. 2, Paragraph 0032, The shape of the blur/pixel spread/distortion is an identifiable characteristic resulting from the noncircular aperture/aperture shape.).
Regarding claim 5, the combination of Hiasa and Chen teaches the camera device of claim 4 (see claim 4 analysis), wherein, to adaptively adjust the perceived depth of field, the content manager is configured to increase the perceived depth of field to compensate for the distortion of at least the portion of the image content (Hiasa, Paragraphs 0062-0064, Sharpening the image is considered to be increasing the perceived depth of field.).
Claim 15 is rejected for the same reasons as claim 5.
Regarding claim 7, the combination of Hiasa and Chen teaches the camera device of claim 6 (see claim 6 analysis), wherein the machine learning model is trained to learn an inverse image of the image content (Hiasa, Paragraphs 0046-0048, The PSF is used to generate blurred training images. The machine learning model is trained to reverse the effects of the PSF (inverse the PSF) to a ground truth image.).
Regarding claim 9, the combination of Hiasa and Chen teaches the camera device of claim 6 (see claim 6 analysis), wherein the machine learning model is trained with one or more training images that have sharp foreground content (Hiasa, Paragraph 0046, Images that have sharp foreground content are ground truth images.) and blurry background content (Hiasa, Paragraphs 0047-0048, Images that have blurry background content are the blurred training images.), and an output of the machine learning model is sharpened image content (Hiasa, Paragraphs 0062-0064).
Regarding claim 10, the combination of Hiasa and Chen teaches the camera device of claim 6 (see claim 6 analysis), wherein the machine learning model is trained with a training image pair, a first image of the training image pair having high resolution, low noise, and a large depth of field (Hiasa, Paragraphs 0046 and 0051, The first image is a “ground truth image with high resolution”. The ground truth image does not include the blurs (noise). Blurs include defocus (Paragraph 0035), an image without defocus is considered to be an image with a large depth of field.) and a second image of the training image pair is generated by blurring and adding noise to the first image (Hiasa, Paragraph 0051-0052, The blurs (noise) are applied to generate the training image.).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiasa (US 2020/0388014 A1) in view of Chen et al. (US 2019/0355101 A1) in view of Wadhwa et al. (US 2021/0183089 A1).
Regarding claim 8, the combination of Hiasa and Chen teaches the camera device of claim 6 (see claim 6 analysis), wherein the machine learning model is trained with one or more training images converted to have characteristics of images captured with a mobile terminal having an asymmetric aperture (Hiasa, Paragraphs 0047-0048 and 0089, Chen, Fig. 2, Paragraph 0032).
However, the combination of Hiasa and Chen does not teach wherein the mobile terminal is a mobile phone camera.
In reference to Wadhwa et al. (hereafter referred as Wadhwa), Wadhwa teaches wherein the machine learning model is trained with one or more training images converted to have characteristics of images captured with a mobile phone camera having varying apertures (Wadhwa, Paragraph 0068).
These arts are analogous since they are all related to imaging devices and image blur. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the combination of Hiasa and Chen with the teaching of using training data having characteristics of images captured with a mobile phone camera as seen in Wadhwa to allow the machine learning model to better correct for images taken with a mobile phone camera.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiasa (US 2020/0388014 A1) in view of Chen et al. (US 2019/0355101 A1) in view of Sun (US 2022/0398704 A1).
Alternatively, regarding claim 9, the combination of Hiasa and Chen teaches the camera device of claim 6 (see claim 6 analysis), wherein an output of the machine learning model is sharpened image content (Hiasa, Paragraphs 0062-0064).
However, the combination of Hiasa and Chen does not teach wherein the machine learning model is trained with one or more training images that have sharp foreground content and blurry background content.
In reference to Sun, sun teaches wherein the machine learning model is trained with one or more training images that have sharp foreground content and blurry background content (Sun, Paragraphs 0042 and 0052).
These arts are analogous since they are all related to imaging devices and image blur. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the combination of Hiasa and Chen with the teaching of using training images that have sharp foreground content and blurry background content as seen in Sun since it is a known type of blurred image to be compared with a in focus image when used for training a machine learning model and would produce similar and expected results.
Claim(s) 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hiasa (US 2020/0388014 A1) in view of Chen et al. (US 2019/0355101 A1) in view of Alon et al. (US 2006/0256226 A1).
Regarding claim 11, the combination of Hiasa and Chen teaches the camera device of claim 1 (see claim 1 analysis) identifiable characteristic resulting from the noncircular aperture (Chen, Fig. 2, Paragraph 0032, The shape of the blur/pixel spread/distortion is an identifiable characteristic resulting from the noncircular aperture/aperture shape.).
However, the combination of Hiasa and Chen does not teach wherein, to adaptively adjust the perceived depth of field, the content manager is configured to segregate the image content into region slices, separately process each region slice to compensate for the identifiable characteristic resulting from the noncircular aperture, and stitch compensated region slices back together to generate a sharpened image of the image content.
In reference to Alon et al. (hereafter referred as Alon), Alon teaches wherein; to adaptively adjust the perceived depth of field, the content manager is configured to segregate the image content into region slices (Alon, Paragraph 0085),
separately process each region slice to compensate for the identifiable characteristic (Alon, Paragraphs 0008 and 0085), and
stitch compensated region slices back together to generate a sharpened image of the image content (Alon, Paragraph 0008 and 0085).
These arts are analogous since they are all related to imaging devices and image deblurring. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the combination of Hiasa and Chen with the teaching processing separate blocks and stitching the blocks together to form the sharpened image to account for varying PSF over the image field.
Claim 17 is rejected for the same reasons as claim 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY JASON CHIU whose telephone number is (571)270-1312. The examiner can normally be reached Mon-Fri: 8am-4pm.
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/WESLEY J CHIU/ Examiner, Art Unit 2639
/TWYLER L HASKINS/ Supervisory Patent Examiner, Art Unit 2639
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