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 amendments filed on 06/26/2026 have been entered in full. Claims 1-4 and 10-14 remain pending in this application. The amendments to the claims have overcome each U.S.C. 112(b) rejection previously presented. The amendment to claim 14 overcomes the previously presented U.S.C. 101 rejection.
Response to Arguments
Applicants’ arguments, see pages 7-9, filed 06/26/2026 have been fully considered but they are not persuasive. In the argument the applicant argues Ahn does not teach the image separation unit separates the image into high resolution and low-resolution regions. However, examiner respectfully disagrees as in Figure 6 and paragraphs 0131-032 the image segmentation module is used to recognize instances as seen in the instance map which includes the instances of the person, sky, basketball, and basketball goal stand. In paragraph 0050 and 0052, the multiple instances of the instance map are described as being of different levels of quality, with instances of poor image quality and good image quality. The inclusion of multiple levels of image quality can be seen as the output from scoring shows different scores where the high numbers depict high quality and the low numbers depict low quality. The image segmentation module uses the features from the feature extraction module, wherein the features may be referred to as a feature map and the feature extraction module extracts a feature map corresponding to high-level features and a feature map corresponding to low-level features, thus including quality information in the feature maps.
Applicants’ arguments, see page 9, filed 06/26/2026 have been fully considered but they are not persuasive. In the argument the applicant argues Ahn does not teach an image quality conversion unit that processes only the high-quality region. However, Examiner respectfully disagrees as Claims 1 nor 12 require the conversion unit must only process the high-quality region. Additionally, as seen in the cited paragraph 0114 Ahn includes multiple instance image quality processing modules which correspond to each instance thus the individual instance image quality processing module for the high-quality instance would be a dedicated conversion module for the high-quality region.
Applicant’s arguments, see page 8, filed 06/26/2026, with respect to the rejection(s) of claim(s) 1 and 12 under U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ahn US 20220180625 in view of Zhu US20220368829.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 and 11-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 8 recites “to transmit them”. It is unclear what “them” refers to, whether the term refers a single combined entity or the two separate components.
Claim 10 line 7 recites “to transmit them”. It is unclear what “them” refers to, whether the term refers a single combined entity or the two separate components.
Claim 14 line 8 recites “to transmit them”. It is unclear what “them” refers to, whether the term refers a single combined entity or the two separate components.
Claims 2-4 and 11-13 are rejected due to their dependency on the rejected based claim.
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.
Claims 1, 4, 10, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ahn US 20220180625 (hereinafter “Ahn”) in view of Li US12514832 (hereinafter “Li”) in view of Qi CN111654621 (hereinafter “Qi”).
Regarding claim 1, Ahn teaches a non-uniform super-resolution device of an image, the device comprising (see Fig 1 and paragraph 0050, the computing device 100 and the input image which includes instances with motion and without, leading the object with no motion have good image quality and the object may have poor image quality [the inclusion of areas with good quality and poor quality is interpreted as non-uniform]):
an image separation unit which separates an input image into a high-quality region and a low-quality region based on a quality map image (see paragraph 0132 and 0134 and Fig 6, the use of the segmentation module to receive the features [see paragraph 0129 and 0151, the features may be referred to as a feature map to recognize objects of the input image and wherein the feature extraction module extracts a feature map corresponding to high-level features and a feature map corresponding to low-level features, thus including quality information in the feature maps] of the input image to create an instant map, which is then used to predict quality scores and generate an instance quality score map. In paragraph 0050 and 0052, the multiple instances of the instance map are described as being of different levels of quality, with instances of poor image quality and good image quality. The inclusion of multiple levels of image quality can be seen as the output from scoring shows different scores where the high numbers depict high quality and the low numbers depict low quality);
an image quality conversion unit which acquires a converted high-quality region by converting image quality of the high-quality region (see paragraph 0114, the instance image quality processing module which processes (converts) the instances (regions) based on the corresponding image quality control parameters (0113, based on the quality score), and see paragraph 0053, the method of processing the high-quality regions. See paragraph 0114, multiple instance image quality processing modules which correspond to each instance thus the individual instance image quality processing module for the high-quality instance would be a dedicated conversion module for the high-quality region);
and a first image combination unit which combines the converted high-quality region (see paragraph 0116, the merging
module receives and merges the image quality processed instances from the different processing modules to generate an output image. The merging module merges converted high-quality regions with converted low-quality region; thus, Ahn does not teach the limitation, and an additional reference will show obviousness).
Ahn does not teach a super-resolution device and a first image combination unit which combines the converted high-quality region and the low-quality region to transmit them to a super-resolution network.
Li teaches a super-resolution device of an image (see col 1 lines 47-48, implementation of methods and apparatus of image frame super-resolution), and to transmit them to a super-resolution network (see col 35 lines 17-23, the input of the image frame into the super-resolution network)
Ahn nor Li teaches a first combination unit which combines the converted high-quality and the low-quality region.
Ahn and Li are analogous art because they are both from the field of endeavor separating images based on quality scores for the purpose of quality processing of an image.
Before the effective filling data of the invention, it would have been obvious to one of ordinary skill in the art to modify Ahn to use the super-resolution network in image processing as taught by Li. The motivation for doing so would have been to obtain an output image with a high resolution than the input image (Li, col 35 lines 20-23).
Qi teaches a first image combination unit which combines the converted high-quality region and the low-quality region to transmit them (see page 9 steps 2.2.8, 2.3.1 and 2.3.2 and Figure 4, the low resolution region and the high resolution region are fused to obtain a fused image I.sub.trans. which is processed through the image repairing sub-network [See page 10 paragraph 5 through page 11 paragraph 1, by the invention does not design according to an image super-resolution network when designing the convolutional network model, but realizes digital zooming from the viewpoint of image restoration, which includes both the convolutional network model and the functional objective of super-resolution]. In Figure 4, the high-resolution image I.sub.H goes through a plurality of convolutional layers downsampling the image, shown as white boxes 3-6 [interpreted as converting the I.sub.H into R.sub.H thus the high-resolution region is converted]).
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Regarding claim 4, Ahn, Li and Qu teach the device of Claim 1.
Ahn teaches the conversion of the high-quality region of the image quality conversion unit (see paragraph 0114, the instance image quality processing module which processes (converts) the instances (regions) based on the corresponding image quality control parameters (0113, based on the quality score), and see paragraph 0053, the method of processing the high-quality regions. See paragraph 0114, multiple instance image quality processing modules which correspond to each instance thus the individual instance image quality processing module for the high-quality instance would be a dedicated conversion module for the high-quality region).
Ahn does not teach conversion is performed by using any one of the method of downscaling,
blurring, or repacking.
Li teaches the conversion of the high-quality region of the image quality conversion unit is(see Figure 2 and col 14 lines 43-46, the use of down sampling [a method of downscaling] on the high-resolution image frames)
Claims 10 and 14 are analogous method to the apparatus of claim 1, thus are analyzed and rejected similar to claim 1.
Claim 13 is analogous method to the apparatus of claim 4, thus is analyzed and rejected similar
to claim 4.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ahn in view of Li in view of Qi in view of Putnam US10169852 (hereinafter “Putnam”).
Regarding claim 2, Ahn, Li and Qi teach the device of Claim 1. Ahn and Li teach the non-uniform super-resolution device of the image
Ahn, Li, and Qi do not teach a second image combination unit which selectively replaces a high-quality region of the image obtained from the super-resolution network with an existing high-quality region.
Putnam teaches a second image combination unit which selectively replaces a high-quality
region of the image obtained from the super-resolution network with an existing high-quality region
(see col 20 lines 4-7, the assembly module can replace the super-resolution image tile (region) with
the high-resolution image resolution image tile (region)).
Putnam, Ahn, Li and Qi are analogous art because they are all from the field of endeavor processing of images with different processing based on level of resolution to produce a higher-resolution image.
Before the effective filling date of the invention, it would have been obvious to one of ordinary
skill in the art to modify Ahn, Li, and Qi to use of replacing super-resolution output regions with high-
resolution regions as taught by Putnam. The motivation for doing so would have been to replace tiles of
an image that may be unsuitable for super-resolution imaging (Putnam, col 19 line 41 through col 20
line 9).
Claim 11 is analogous method to the apparatus of claim 2, thus is analyzed and rejected similar
to claim 2.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Ahn in view of Li in view of Qi in view of Laroche US20230281756 (hereinafter “Laroche”).
Regarding claim 3, Ahn, Li and Qi teach the device of Claim 1. Qi teaches the first image
combination unit
(see page 9 steps 2.2.8, 2.3.1 and 2.3.2 and Figure 4, the low resolution region and the high resolution region are fused to obtain a fused image I.sub.trans. which is processed through the image repairing sub-network).
Ahn, Li, and Qi do not teach teaches downsample a size of a combined image according to an input format of the super-resolution network.
Laroche teaches downsample a size of a combined image according to an input format of the
super-resolution network (see paragraph 0028, after summing regions the resulting image is down-
sampled according to the desired scaling factor and noise [the scaling factor is given to the neural
network for super-resolution, Figure 4]).
Laroche, Ahn, Li, and Qi are analogous art because they are both from the field of endeavor processing of images of different quality using neural networks.
Before the effective filling date of the invention, it would have been obvious to one of ordinary
skill in the art to modify Ahn, Li, and Qi to use down-sampling combined regions according to the input of the super-resolution neural network as taught by Laroche. The motivation for doing so would have been to obtain image of the desired scale (Laroche, paragraph 0028).
Claim 12 is analogous method to the apparatus of claim 3, thus is analyzed and rejected similar
to claim 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached notice of reference cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY R. HAUK whose telephone number is (571)272-5966. The examiner can normally be reached M-F 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY ROSE HAUK/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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