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 .
Election/Restrictions
Applicant's election of Group I (claims 1-8 and 20) in Page 1, filed April 24,2026 is acknowledged, the election has been treated as an election without traverse (M.P.E.P. § 818.03(a)).
Claims 9-19 are withdrawn from further consideration pursuant to 37 C.F.R. § 1.142(b) as being drawn to nonelected Species, there being no allowable generic or linking claim(s). Election was made without traverse .
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Based upon consideration of all the relevant factors with respect to the claim as a whole, claims 1-8 are held to claim an abstract idea, and is therefore rejected as ineligible subject matter under 35 USC 101. The rationale for this finding is explained below:
The claimed “obtaining an input image ……. obtaining a feature of low-resolution images ………obtaining, based on the fusion residual feature……………” is drawn to the disembodied concept of human analysis and judgment (i.e., mental activity in the form of forming a judgment, observation, evaluation, or opinion), without any tangible implementation (e.g., no machine implementation or transformation of an article), and absent any observable and verifiable steps (i.e., all steps may be performed mentally). Thus, the claim is drawn to an abstract idea and is thus non-statutory .
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.
Claim(s) 1-8, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al ( Deep Reparameterization of Multi-Frame Super-Resolution and Denoising) In view of Tang et al (US20210241470)
As to claim 1, Bhat et al teaches the image processing method of an image processing model, the image processing method comprising:
obtaining an input image group comprising a plurality of low-resolution images ( RAW LR burst input, figure 1) corresponding to a plurality of different viewpoints (The degradation operator H and the scene motion mi take different forms depending on the addressed task. For example, in the super-resolution task, H acts as the down sampling kernel. Similarly, the scene motion mi can denote the parameters of an affine transformation, or represent a per-pixel optical flow in case of dynamic scenes, section 3.1);
obtaining a feature of low-resolution images by extracting a feature for each low- resolution image of the plurality of low-resolution images of the input image group (The features e1 extracted from the reference image x1 are then warped to i-th image to compute the residual, which indicates possible alignment errors, section D.1. Raw burst super resolution) While Bhat teaches the limitation above Bhat fails to teach “
obtaining a fusion residual feature by fusing the feature of the low-resolution images;
and obtaining, based on the fusion residual feature, a super-resolution image corresponding to the input image group. “ Tang teaches a predicted image residual is added to an image obtained by directly upsampling the original image frame, so that a high-resolution frame may be obtained ( paragraph [0131]). Tang teaches The plurality of pieces of aligned feature data are fused by a fusion convolutional network according to the weight information of each of the plurality of pieces of aligned feature data, to obtain the fused information of the image frame sequence. Then, spatial feature data is generated based on the fused information of the image frame sequence; and the spatial feature data is modulated based on spatial attention information of each element in the spatial feature data to obtain modulated fused information. The modulated fused information is configured to acquire the processed image frame corresponding to the image frame to be processed ( paragraph [0136]).
It would have been obvious to one skilled in the art before filing of the claimed invention to use fused residual feature of Tang et al in order to enhance the quality of multi-frame fusion and restore high-quality output frames from a series of low-quality input frames. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, Tang et al teaches the image processing method of claim 1, wherein the obtaining the fusion residual feature by fusing the feature of the low-resolution images comprises: obtaining an alignment feature of the low-resolution images by aligning the feature of the low-resolution images (performing image alignment on the image frame to be processed and each of image frames in the image frame sequence to obtain a plurality of pieces of aligned feature data; paragraph [006]); and obtaining the fusion residual feature by fusing the alignment feature of low-resolution images through an attention-based residual feature fusion network of the image processing model (fusing the plurality of pieces of aligned feature data according to the weight information of each of the plurality of pieces of aligned feature data, to obtain fused information of the image frame sequence, the fused information being configured to acquire a processed image frame corresponding to the image frame to be processed, paragraph [006]).
As to claim 3, Tang et al teaches the image processing method of claim 2, wherein the obtaining the fusion residual feature by fusing the alignment feature of the low-resolution images comprises: obtaining a fusion weight of each low-resolution image of the plurality of low- resolution images based on the alignment feature of the low-resolution images (The plurality of pieces of aligned feature data are fused according to the weight information of each of the plurality of pieces of aligned feature data, paragraph [0027]); and obtaining the fusion residual feature by obtaining a weight for the alignment feature of low-resolution images based on the fusion weight of the low-resolution images (a predicted image residual is added to an image obtained by directly upsampling the original image frame, so that a high-resolution frame may be obtained. Like an existing manner image/video restoration processing, the addition is intended for learning the image residual, so as to accelerate the convergence of training and improve the effect of training, paragraph [0131]).
As to claim 4, Tang et al teaches the image processing method of claim 2, wherein the obtaining the alignment feature of the low-resolution images by aligning the feature of the low-resolution images comprises: obtaining an optical flow of the input image group; and obtaining the alignment feature of the low-resolution images by aligning the feature of the low-resolution images based on the optical flow (he PCD alignment module may learn together with the whole network framework without additional supervision or pre-training another task such as an optical flow, paragraph [0091-0092]).
As to claim 5, Tang et al teaches the image processing method of claim 4, wherein the optical flow is a pre- obtained optical flow (he PCD alignment module may learn together with the whole network framework without additional supervision or pre-training another task such as an optical flow, paragraph [0091-0093]).
As to claim 6, Tang et al teaches the image processing method of claim 1, wherein the extracting the feature for each of the plurality of low-resolution images of the input image group comprises: extracting a feature for each low-resolution image of the plurality of low-resolution images of the input image group through a heterogeneous convolution kernel of a feature extraction network of the image processing model feature data obtained after feature extraction is performed on the image frame, in a pyramid structure, subsampling convolution may be performed on feature data of an (L−1).sup.th layer by a convolutional filter to obtain feature data of an L.sup.th layer. For the feature data of the L.sup.th layer, alignment prediction may be performed by the feature data of an upper (L+1).sup.th layer, paragraph [0079]).
As to claim 7, Bhat et al teaches the image processing method of claim 1, wherein the plurality of low- resolution images corresponding to the plurality of different viewpoints of the input image group are a plurality of raw format images corresponding to a plurality of different viewpoints obtained simultaneously( RAW LR burst input, figure 1).
As to claim 8, Tang et al teaches the image processing method of claim 1, wherein the obtaining the super- resolution image corresponding to the input image group based on the fusion residual feature comprises: obtaining a reconstruction feature by reconstructing the fusion residual feature a feature reconstruction network of the image processing model(image reconstruction may further be performed according to the fused information to obtain the processed image frame corresponding to the image frame to be processed, paragraph [0069]); and obtaining the super-resolution image corresponding to the input image group by refining the reconstruction feature through a feature refinement network of the image processing model (Additional deformable convolution (the part with shaded background in FIG. 3) for alignment adjustment may be cascaded after the pyramid structure to further refine preliminarily aligned features. In such a coarse-to-fine manner, the PCD alignment module may improve image alignment in a sub-pixel level, paragraph [0090]).
The limitation of claim 20 has been addressed above.
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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