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 Arguments
Applicant's arguments filed 1/16/2026 have been fully considered but they are not persuasive.
Applicant argues that Zhang does not explicitly teach processing the transformed secondary channel based on the transformed primary channel via a second neural network to obtain a modified transformed secondary channel.
In response, the examiner respectfully disagrees. Zhang teaches in the middle of Fig. 4 (Ug channel) that the first CNN (second neural network) processes the output of DWT (that is considered to be the transformed secondary channel). The output of the first CNN is considered to be a modified transformed secondary channel. The processing is based on the transformed primary channel because prior to the DWT, the input Ug is concatenated with the Output_1. Output_1 is generated based on the transformed primary channel (output of DWT in the Ur channel, top of Fig. 4).
Claim Rejections - 35 USC § 102
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) 1-4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang, M., Yuan, Y., Zhang, F., Wang, S., Wang, S., & Liu, Q. (2019). Multi-noise and multi-channel derived prior information for grayscale image restoration. IEEE Access, 7, 150082-150092. (hereinafter “Zhang”).
Consider claim 1, Zhang teaches a method of modifying an image region represented by two or more image channels, the method comprising: processing a primary channel (Top of Fig. 4: red channel, Ur) of the two or more image channels based on a first spatial frequency transform (Top of Fig. 4: DWT) to obtain a transformed primary channel (top of Fig. 4: output of DWT. Section B: Multi-Channel Enhancement); processing a secondary channel (middle of Fig. 4: Ug) of the two or more image channels different from the primary channel based on a second spatial frequency transform (middle of Fig. 4: DWT) to obtain a transformed secondary channel (middle of Fig. 4: output of DWT. Section B: Multi-Channel Enhancement); processing the transformed primary channel (Top of Fig. 4: input of first CNN) via a first neural network (Top of Fig. 4: first CNN) to obtain a modified transformed primary channel (Top of Fig. 4: output of first CNN (160). Section B: Multi-Channel Enhancement); processing the transformed secondary channel (middle of Fig. 4: input of first CNN) based on the transformed primary channel (middle of Fig. 4: first CNN) via a second neural network to obtain a modified transformed secondary channel (middle of Fig. 4: output of first CNN (160). Section B: Multi-Channel Enhancement); processing the modified transformed primary channel based on a first inverse spatial frequency transform (top of Fig. 4: IWT) to obtain a modified primary channel (top of Fig. 4: output_1); processing the modified transformed secondary channel based on a second inverse spatial frequency transform (middle of Fig. 4: IWT) to obtain a modified secondary channel (middle of Fig. 4: output_2); and obtaining a modified image region based on the modified primary channel and the modified secondary channel (Fig. 4: Output).
Consider claim 2, Zhang teaches one or both of the first spatial frequency transform and the second spatial frequency transform are selected from the group consisting of: a wavelet transform, a Discrete Fourier Transform, a Fast Fourier Transform, and an energy compacting transform comprising a Discrete Cosine Transform (Fig. 4: DWT & IWT. Section B: Multi-Channel Enhancement).
Consider claim 3, Zhang teaches both the first spatial frequency transform and the second spatial frequency transform are one of: a wavelet transform, a Discrete Fourier Transform, a Fast Fourier Transform, an energy compacting transform, and a Discrete Cosine Transform (Fig. 4: DWT & IWT. Section B: Multi-Channel Enhancement).
Consider claim 4, Zhang teaches one or both of the first spatial frequency transform and the second spatial frequency transform are a wavelet transform selected from the group consisting of: a discrete wavelet transform and a stationary wavelet transform (Fig. 4: DWT & IWT. Section B: Multi-Channel Enhancement).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 5-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, M., Yuan, Y., Zhang, F., Wang, S., Wang, S., & Liu, Q. (2019). Multi-noise and multi-channel derived prior information for grayscale image restoration. IEEE Access, 7, 150082-150092. (hereinafter “Zhang”) in view of Cui et al. (WO 2021/249684 A1).
Consider claim 5, Zhang teaches all the limitations in claim 1 but does not explicitly teach selecting the primary channel from the two or more image channels.
Cui teaches selecting the primary channel from the two or more image channels (p. 2, lines 22-27; p. 32, lines 20-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 6, Cui teaches selecting the secondary channel from the two or more image channels (p. 2, lines 22-27; p. 32, lines 20-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 7, Cui teaches the primary channel and the secondary channel are selected from the two or more image channels based on an output of a classifier operating based on another neural network (p. 2, lines 22-27; p. 32, lines 20-23).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 8, Cui teaches the processing of the transformed secondary channel based on the transformed primary channel comprises concatenating a second three-dimensional tensor representing the transformed secondary channel with a first three-dimensional tensor representing the transformed primary channel (Fig. 4: Between stages 6 and 7 shows concatenated channels. P. 27, lines 12-15. Fig. 13 and p. 36, lines 30-34).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 9, Cui teaches a size of the primary channel is different from a size of the secondary channel (p. 35, lines 25-31; Fig. 12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 12, Cui teaches splitting an image into a plurality of image regions that comprise the image region and padding image regions resulting from the splitting that are not square in height and width dimensions of the image regions such that they are square in the height and width dimensions of the image region (p. 23, lines 4-34).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 13, Cui teaches splitting an image into image regions comprising the image region; and wherein when the image cannot be split only into image regions that are square in height and width dimensions of the image regions, padding the image such that the image is split into image regions only that are all square in the height and width dimensions of the image regions comprising the image region (p. 23, lines 4-34).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 14, Cui teaches the first neural network and the second neural network are operated independently from each other, and wherein weights of one of the first neural network and the second neural network are determined and used independently from weights of the other one of the first neural network and the second neural network (Fig. 4, Fig. 6, and Fig. 7. Network (S) and Network (C). p. 25, line 5 – p. 27, line 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 15, Cui teaches each of the first neural network and the second neural network is or comprises a convolutional neural network, wherein the convolutional neural network comprises at least one residual network component, and wherein the convolutional neural network uses a scaling layer represented by one or more scaling values (Fig. 4, Fig. 6, and Fig. 7. Network (S) and Network (C). p. 25, line 5 – p. 27, line 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 16, Cui teaches a method for encoding an image or a video sequence of images, the method comprising: obtaining an original image region, encoding the obtained original image region into a bitstream, and applying the method according to claim 1 for modifying an image region obtained by reconstructing the encoded original image region (claim 8).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 17, Cui teaches a method for decoding an image or a video sequence of images from a bitstream, the method comprising: reconstructing an image region from the bitstream (claim 12); and applying the method according to claim 1 for modifying the reconstructed image region (claim 12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 18, Cui teaches a method for decoding an image or a video sequence of images from a bitstream, the method comprising: parsing the bitstream to obtain at least one of: an indication that the method according to claim 1 for modifying an obtained image region is not to be applied for the image region, an indication of the primary channel for the region, an adaption of one or more weights of at least one of the first neural network and the second neural network; and reconstructing an image region from the bitstream, and modifying, when the indication of the primary channel for the region indicates a selected primary channel, the reconstructed image region according to the method according to claim 1 with the indicated primary channel as the selected primary channel (claim 13).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 19, the combination of Zhang and Cui teaches a non-transitory computer readable medium having stored thereon processor executable instructions (p. 5, lines 25-30; p. 41, lines 11-26; p. 42, line 15-p. 43, line 8 of Cui) that, when executed by one or more processors, cause the one or more processors to perform the method according to claim 1 (see rejection of claim 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of selecting the primary channel from the two or more image channels by a classifier because such incorporation would enable training or designing such classifier to properly select the image channel to be the primary channel so that the quality of image modification (such as image enhancement) may be improved. P. 2, lines 22-27.
Consider claim 20, the combination of Zhang and Cui teaches an apparatus for modifying an image region represented by two or more image channels, the apparatus comprising: processing circuitry (p. 5, lines 11-30; p. 38, lines 17-20; p. 41, lines 11-26; Fig. 15) that performs the method recited in claim 1 (see rejection of claim 1).
Allowable Subject Matter
Claims 10-11 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TAT C CHIO/ Primary Examiner, Art Unit 2486