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 August 6, 2026 have been fully considered but they are not persuasive.
Regarding claim 28,
(1) applicant states that “the solution of amended claim 28, at least particularly with respect to the previously described features related to the acquisition of the forward hidden variable set of the target video frame as well as features related to the acquisition of the second decoded feature in the second scale, is not disclosed, or suggested by the applied reference.”. Examiner disagrees with this statement. Zhu teaches wherein, based on the third decoding feature, and the encoding feature under the second scale included in the first hidden variable set, acquiring the second decoding feature for the target video frame under the second scale, comprises: upsampling the third decoding feature, to acquire a first upsampled feature (Figure 2(c)
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); processing the second encoding feature through the residual block, to acquire a tenth feature (Figure 2(c)
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); performing additive fusion on the first upsampled feature and the tenth feature, to acquire an eleventh feature (Figure 2(c)
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); processing the eleventh feature through the residual block, to acquire the second decoding feature (Figure 2(c)
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).
Response to Amendment
The Amendment of August 6, 2026 overcomes the following objections:
Objection of the drawings.
Objection of claim 32 because of informalities
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 28-29, 32-40, 42-44 and 46-47 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu (Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring, arXiv:2112.05150v1 [eess.IV] 9 Dec 2021).
Regarding claim 28, Zhu teaches a blurry video repair method, comprising: performing feature extraction (Fig. 2(b) Feature extractor module.
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) on a target video frame of a video to be repaired (Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring. Title), to acquire an intrinsic feature of the target video frame (For a blurry frame It, our method first extracts target features Φt via a feature extractor that contains a convolutional layer and a Channel Attention Block (CAB) from RCAN (Figure 2 (b)). Page 3 right column 3rd paragraph. ); acquiring a forward hidden variable set of the target video frame based on the intrinsic feature (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph) and a first hidden variable set, wherein the first hidden variable set is the forward hidden variable set of a previous video frame of the target video frame (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph); acquiring a backward hidden variable set of the target video frame based on the intrinsic feature (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph) and a second hidden variable set, wherein the second hidden variable set is the backward hidden variable set of a next video frame of the target video frame (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph); acquiring an enhanced feature of the target video frame (
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. Page 4 right column 2nd paragraph) based on the intrinsic feature (For a blurry frame It, our method first extracts target features Φt via a feature extractor that contains a convolutional layer and a Channel Attention Block (CAB) from RCAN (Figure 2 (b)). Page 3 right column 3rd paragraph), the forward hidden variable set of the target video frame (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph), and the backward hidden variable set of the target video frame (Then, a MBP module is developed to gather the extracted features Φt and unaligned neighboring multi-scale hidden states sets (Ft-1 and Bt+1) in both the forward direction and the backward direction, and then outputs the propagated multi-scale hidden state set (Ft and Bt). Page 3 right column last paragraph); and performing additive fusion on the enhanced feature of the target video frame and the target video frame, to acquire a deblurred video frame from the target video frame (
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. Page 4 right column 2nd paragraph), wherein, the forward hidden variable set comprises: an encoding feature and a decoding feature under a first scale (
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Page 4 left column 3rd paragraph), an encoding feature and a decoding feature under a second scale (
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Page 4 left column 3rd paragraph), and an encoding feature and a decoding feature under a third scale (
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Page 4 left column 3rd paragraph), wherein, acquiring the forward hidden variable set of the target video frame based on the intrinsic feature and the first hidden variable set, comprises: based on the intrinsic feature, and the encoding feature and the decoding feature under the first scale included in the first hidden variable set, acquiring a first encoding feature for the target video frame under the first scale (
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Page 4 left column 3rd paragraph); based on the first encoding feature, and the encoding feature and the decoding feature under the second scale included in the first hidden variable set, acquiring a second encoding feature for the target video frame under the second scale (
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Page 4 left column 3rd paragraph); based on the second encoding feature, and the encoding feature and the decoding feature under the third scale included in the first hidden variable set, acquiring a third encoding feature for the target video frame under the third scale (
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Page 4 left column 3rd paragraph); based on the third encoding feature, acquiring a third decoding feature of the target video frame under the third scale (
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Page 4 left column 3rd paragraph); based on the third decoding feature, and the encoding feature under the second scale included in the first hidden variable set, acquiring a second decoding feature for the target video frame under the second scale (
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Page 4 left column 3rd paragraph); based on the second decoding feature and the encoding feature under the first scale included in the first hidden variable set, acquire the first decoding feature of the target video frame under the first scale (
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Page 4 left column 3rd paragraph); combining the first encoding feature, the second encoding feature, the third encoding feature, the first decoding feature, the second decoding feature and the third decoding feature to the forward hidden variable set of the target video frame (
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Page 4 left column 2nd paragraph. Equation (1) can be applied to Ft to combine the encoding and decoding features from different levels at t.), and wherein, based on the third decoding feature, and the encoding feature under the second scale included in the first hidden variable set, acquiring the second decoding feature for the target video frame under the second scale, comprises: upsampling the third decoding feature, to acquire a first upsampled feature (Figure 2(c)
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); processing the second encoding feature through the residual block, to acquire a tenth feature (Figure 2(c)
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); performing additive fusion on the first upsampled feature and the tenth feature, to acquire an eleventh feature (Figure 2(c)
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); processing the eleventh feature through the residual block, to acquire the second decoding feature (Figure 2(c)
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).
Claims 29, 33-34 and 36, unamended and are rejected based on Zhu. The grounds of rejection established in the last Office Action is fully incorporated herein.
Regarding claim 32, Zhu teaches the method of claim [[31]]28, wherein, based on the intrinsic feature, and the encoding feature and the decoding feature under the first scale included in the first hidden variable set, [[the]] acquiring [[a]]the first encoding feature for the target video frame under the first scale, comprises: processing the intrinsic feature through the residual block (Figure 2(b) Channel Attention Block shows a residual block to process the intrinsic feature.
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), to acquire a first feature (For a blurry frame It, our method first extracts target features Φt via a feature extractor that contains a convolutional layer and a Channel Attention Block (CAB) from RCAN (Figure 2 (b)). Page 3 right column 3rd paragraph); processing the encoding feature under the first scale included in the first hidden variable set through the convolutional layer, to acquire a second feature (
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Page 4 left column 3rd paragraph); processing the decoding feature under the first scale included in the first hidden variable set through the convolutional layer, to acquire a third feature (
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Page 4 left column 3rd paragraph); performing additive fusion on the first feature, the second feature and the third feature, to acquire the first encoding feature (
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Page 4 left column 3rd paragraph), and/or (Note: the claim language is interpreted as disjunctive.) wherein, based on the first encoding feature, and the encoding feature and the decoding feature under the second scale included in the first hidden variable set, [[the]] acquiring [[a]]the second encoding feature for the target video frame under the second scale, comprises: downsampling the first encoding feature, to acquire a first downsampled feature; processing the first downsampled feature through the residual block, to acquire a fourth feature; processing the encoding feature under the second scale included in the first hidden variable set through the convolutional layer, to acquire a fifth feature; processing the decoding feature under the second scale included in the first hidden variable set through the convolutional layer, to acquire a sixth feature; performing additive fusion on the fourth feature, the fifth feature and the sixth feature, to acquire the second encoding feature, and/or wherein, based on the second encoding feature, and the encoding feature and the decoding feature under the third scale included in the first hidden variable set, [[the]] acquiring [[a]]the third encoding feature for the target video frame under the third scale, comprises: downsampling the second encoding feature, to acquire a second downsampled feature[[.]]; processing the second downsampled feature through the residual block, to acquire a seventh feature[[.]]; processing the encoding feature under the third scale included in the first hidden variable set through the convolutional layer, to acquire an eighth feature[[,]]; processing the decoding feature under the second scale included in the first hidden variable set through the convolutional layer, to acquire a ninth feature[[,]]; performing additive fusion on the seventh feature, the eighth feature and the ninth feature, to acquire the third encoding feature, and/or wherein, based on the third encoding feature, [[the]] acquiring [[a]]the third decoding feature of the target video frame under the third scale, comprises: processing the third encoding feature through the residual block, to acquire the third decoding feature, and/or wherein, based on the second decoding feature and the encoding feature under the first scale included in the first hidden variable set, [[the]] acquiring the first decoding feature of the target video frame under the first scale, comprises: upsampling the second decoding feature, to acquire a second upsampled feature; processing the first encoding feature through the residual block, to acquire a twelfth feature; performing additive fusion on the second upsampled feature and the twelfth feature, to acquire a thirteenth feature; processing the thirteenth feature through the residual block, to acquire the first decoding feature.
Regarding claim 35, Zhu teaches the method of claim 34, wherein, based on the intrinsic feature, and the encoding feature and the decoding feature under the first scale included in the second hidden variable set, [[the]] acquiring [[a]]the fourth encoding feature for the target video frame under the first scale, comprises: processing the intrinsic feature through the residual block (Figure 2(b) Channel Attention Block shows a residual block to process the intrinsic feature.
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), to acquire a first feature (For a blurry frame It, our method first extracts target features Φt via a feature extractor that contains a convolutional layer and a Channel Attention Block (CAB) from RCAN (Figure 2 (b)). Page 3 right column 3rd paragraph); processing the encoding feature under the first scale included in the second hidden variable set through the convolutional layer, to acquire a fourteenth feature (
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Page 4 left column 3rd paragraph. A person having ordinary skill in the art would recognize equation (2) can be applied to describe the corresponding backward feature by changing t-1 to t+1.); processing the decoding feature under the first scale included in the second hidden variable set through the convolutional layer, to acquire a fifteenth feature (
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Page 4 left column 3rd paragraph. A person having ordinary skill in the art would recognize equation (2) can be applied to describe the corresponding backward feature by changing t-1 to t+1.); performing additive fusion on the first feature, the fourteenth feature and the fifteenth feature, to acquire the fourth encoding feature (
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Page 4 left column 3rd paragraph. A person having ordinary skill in the art would recognize equation (2) can be applied to describe the corresponding backward feature by changing t-1 to t+1.), and/or (Note: the claim language is interpreted as disjunctive.) wherein, based on the fourth encoding feature, and the encoding feature and the decoding feature under the second scale included in the second hidden variable set, [[the]] acquiring [[a]]the fifth encoding feature for the target video frame under the second scale, comprises: downsampling the fourth encoding feature, to acquire a third downsampled feature; processing the third downsampled feature through the residual block, to acquire a sixteenth feature; processing the encoding feature under the second scale included in the second hidden variable set through the convolutional layer, to acquire a seventeenth feature; processing the decoding feature under the second scale included in the second hidden variable set through the convolutional layer, to acquire an eighteenth feature; performing additive fusion on the sixteenth feature, the seventeenth feature and the eighteenth feature, to acquire the fifth encoding feature, and/or wherein, based on the fifth encoding feature, and the encoding feature and the decoding feature under the third scale included in the second hidden variable set, [[the]] acquiring [[a]]the sixth encoding feature for the target video frame under the third scale, comprises: downsampling the fifth encoding feature, to acquire a fourth downsampled feature; processing the fourth downsampled feature through the residual block, to acquire a nineteenth feature; processing the encoding feature under the third scale included in the second hidden variable set through the convolutional layer, to acquire a twentieth feature; processing the decoding feature under the second scale included in the second hidden variable set through the convolutional layer, to acquire a twenty-first feature; performing additive fusion on the nineteenth feature, the twentieth feature and the twenty-first feature, to acquire the sixth encoding feature, and/or wherein, based on the sixth encoding feature, [[the]] acquiring [[a]]the sixth decoding feature of the target video frame under the third scale, comprises: processing the sixth encoding feature through the residual block, to acquire the sixth decoding feature, and/or wherein, based on the sixth decoding feature, and the encoding feature under the second scale included in the second hidden variable set, [[the]] acquiring [[a]]the fifth decoding feature for the target video frame under the second scale, comprises: upsampling the sixth decoding feature, to acquire a third upsampled feature; processing the fifth encoding feature through the residual block, to acquire a twenty-second feature; performing additive fusion on the second upsampled feature and the twenty-second feature, to acquire a twenty-third feature; processing the twenty-third feature through the residual block, to acquire the fifth decoding feature, and/or wherein, based on the fifth decoding feature and the encoding feature under the first scale included in the second hidden variable set, [[the]] acquiring the fourth decoding feature of the target video frame under the first scale, comprises: upsampling the fifth decoding feature, to acquire a fourth upsampled feature; processing the fourth encoding feature through the residual block, to acquire a twenty-fourth feature; performing additive fusion on the fourth upsampled feature and the twenty-fourth feature, to acquire a twenty-fifth feature; processing the twenty-fifth feature through the residual block, to acquire the fourth decoding feature.
Regarding claim 37, Zhu teaches the method of claim 36, wherein, the fusing the intrinsic feature, the first encoding feature, the fourth encoding feature, the first decoding feature and the fourth decoding feature, to acquire [[a]]the twenty-sixth feature, comprises: processing the intrinsic feature through the residual block (Figure 2(b) Channel Attention Block shows a residual block to process the intrinsic feature.
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), to acquire a first feature (For a blurry frame It, our method first extracts target features Φt via a feature extractor that contains a convolutional layer and a Channel Attention Block (CAB) from RCAN (Figure 2 (b)). Page 3 right column 3rd paragraph); performing additive fusion on the first encoding feature and the fourth encoding feature, to acquire a first fusion feature (
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Page 4 right column 2nd paragraph); processing the first fusion feature through the convolutional layer, to acquire a first convolutional feature (
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Page 4 right column 2nd paragraph); performing additive fusion on the first decoding feature and the fourth decoding feature, to acquire a second fusion feature (
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Page 4 right column 2nd paragraph); processing the second fusion feature through the convolutional layer, to acquire a second convolutional feature (
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Page 4 right column 2nd paragraph); performing additive fusion on the first feature, the first convolutional feature, and the second convolutional feature, to acquire the twenty-sixth feature (
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Page 4 right column 2nd paragraph), and/or (Note: the claim language is interpreted as disjunctive.) wherein, the fusing the twenty-sixth feature, the second encoding feature, the fifth encoding feature, the second decoding feature and the fifth decoding feature, to acquire [[a]]the twenty-seventh feature, comprises: processing the twenty-sixth feature through the residual block, to acquire a first residual feature; performing additive fusion on the second encoding feature and the fifth encoding feature, to acquire a third fusion feature; processing the third fusion feature through the convolutional layer, to acquire a third convolutional feature; upsampling the third convolutional feature, to acquire a fifth upsampled feature; performing additive fusion on the second decoding feature and the fifth decoding feature, to acquire a fourth fusion feature; processing the fourth fusion feature through the convolutional layer, to acquire a fourth convolutional feature; upsampling the fourth convolutional feature, to acquire a sixth upsampled feature; performing additive fusion on the first residual feature, the fifth upsampled feature, and the sixth unsampled feature, to acquire the twenty-seventh feature, and/or wherein, the fusing the twenty-seventh feature, the third encoding feature, the sixth encoding feature, the third decoding feature and the sixth decoding feature, to acquire [[a]]the twenty-eighth feature, comprises: processing the twenty-seventh feature through the residual block, to acquire a second residual feature; performing additive fusion on the third encoding feature and the sixth encoding feature, to acquire a fifth fusion feature; processing the fifth fusion feature through the convolutional layer, to acquire a fifth convolutional feature; upsampling the fifth convolutional feature, to acquire a seventh upsampled feature; performing additive fusion on the third decoding feature and the sixth decoding feature, to acquire a sixth fusion feature; processing the sixth fusion feature through the convolutional layer, to acquire a sixth convolutional feature; upsampling the sixth convolutional feature, to acquire an eighth upsampled feature; performing additive fusion on the second residual feature, the seventh upsampled feature, and the eighth unsampled feature, to acquire the twenty-eighth feature.
Apparatus claims 38, 40 and 43 are drawn to the apparatus corresponding to the method of using same as claimed in claims 28, 29 and 36. Therefore apparatus claims 38, 40 and 43 correspond to method claims 28, 29 and 36, and are rejected for the same reasons of anticipation as used above.
Apparatus claim 42 is drawn to the apparatus corresponding to the method of using same as claimed in claims 33 and 34. Therefore apparatus claim 42 corresponds to method claims 33 and 34, and is rejected for the same reasons of anticipation as used above.
Claims 39, 44 and 47 are drawn to a non-transitory computer-readable storage medium having executable instructions stored for carrying out the method of claims 28, 29 and 36. Therefore, claims 39, 44 and 47 correspond to method claims 28, 29 and 36, and are rejected for the same reasons of anticipation as used above.
Claim 46 is drawn to a non-transitory computer-readable storage medium having executable instructions stored for carrying out the method of claims 33 and 34. Therefore, claim 46 corresponds to method claims 33 and 34, and is rejected for the same reasons of anticipation as used above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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