DETAILED ACTION
This office action is responsive to the Request for Continued Examination filed 2/13/2026. The application contains claims 1-2, 4-8, 10-22, all examined and rejected.
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 .
Information Disclosure Statement
The Information Disclosure Statement with references submitted 2/16/2026 has been considered and entered into the file.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/13/2026 has been entered.
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.
(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 1-2, 4-8, 11-21 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by HUANG et al . [US 2018/0249158 A1, hereinafter Hu]
With regard to Claim 1,
Hu teach a method of processing visual media data, comprising:
performing a conversion between visual media data and a bitstream of the visual media data (Fig. 2a: encoder, 2b: decoder, ¶9, “The transformed and quantized residues are then coded by Entropy coding unit 122 to be included in a video bitstream corresponding to the compressed video data”, ¶10, “Entropy Decoding unit 160 is used to recover coded symbols or syntaxes from the bitstream”, ¶40),
wherein the performing of the conversion includes selectively applying a convolutional neural network filter during the conversion based on a rule (¶29, “the DNN is used for making a pixel-wise in-loop filter on/off decision”, ¶33, “ implicit selection can be dependent on the slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information”, ¶34, “Moreover, an on/off control flag for indicating whether to enable the DNN can be signalled to the decoder to further improve the performance of this framework”, ¶29);
wherein the rule specifies whether and/or how the convolutional neural network filter is applied (¶33, “The implicit selection can be dependent on the slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information”, ¶34, “Moreover, an on/off control flag for indicating whether to enable the DNN can be signalled to the decoder to further improve the performance of this framework”, ¶35, “If the method is out-loop, a decoder can optionally apply the method. An encoder will not use the restored frame to predict following frames. Therefore, if a decoder does not apply the method for a frame, mismatch between the encoder and the decoder will not propagate “, ¶29, “pixel-wise in-loop filter on/off decision …”), and
wherein the rule specifies that use of the convolutional neural network filter is implicitly derived based on decoded information (¶33, “The selection of pre-defined parameter sets can be explicitly signalled to the decoder or implicitly decided at decoder. The explicit selection can be signalled at a sequence level, picture level, slice level, CTU (Coding Tree Unit)-row level, CTU level, or CU (Coding Unit) level. The implicit selection can be dependent on the slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information”).
With regard to Claim 2,
Hu teach the method of claim 1, wherein the convolutional neural network filter is implemented using a convolutional neural network and applied to at least some samples of a video unit of the visual media data (Hu, ¶19, ¶28, ¶29, “When DNN is used for signal restoration, the DNN output is provided to the next stage”,¶34).
With regard to Claim 4,
Hu teach the method of claim 1, wherein the rule specifies that the convolutional neural network filter and certain types of non-deep learning filtering (NDLF) filters are used in a mutually exclusive manner (Hu, Fig. 3, ¶29, “DNN is used for making a pixel-wise in-loop filter on/off decision for each in-loop filter”, “DNN can be applied after SAO (labelled as point B), DF (labelled as point C), or REC (labelled as point D), with or without other restoration methods in one video coding system”, ¶¶35-36, “DNN is applied after DF, SAO, ALF or other in-loop filters to determine whether the pixels within an in-loop filter enabled region should be modified by the in-loop filter “).
With regard to Claim 5,
Hu teach the method of claim 1, wherein the rule specifies to apply the convolutional neural network filter together with a non-deep learning filtering (NDLF) filter (Hu, ¶29, “DNN is used for making a pixel-wise in-loop filter on/off decision for each in-loop filter enabled region. The DNN can be applied to a point in the video coding chain where the video signal is subject to distortion. For example, the DNN can be applied to the output of ALF (labelled as point A) in the encoder as well as in the decoder as shown in FIG. 2A and FIG. 2B respectively. The DNN can be applied after SAO (labelled as point B), DF (labelled as point C), or REC (labelled as point D), with or without other restoration methods in one video coding system, as shown in FIG. 2A and FIG. 2B”).
With regard to Claim 6,
Hu teach the method of claim 1, wherein the rule specifies to apply the convolutional neural network filter before or after applying a non-deep learning filtering (NDLF) filter (Hu, ¶29, “DNN can be applied to the output of ALF (labelled as point A) in the encoder as well as in the decoder as shown in FIG. 2A and FIG. 2B respectively. The DNN can be applied after SAO (labelled as point B), DF (labelled as point C), or REC (labelled as point D), with or without other restoration methods in one video coding system, as shown in FIG. 2A and FIG. 2B”).
With regard to Claim 7,
Hu teach the method of claim 1, wherein the rule specifies to apply the convolutional neural network filter to samples of a video unit to which a non-deep learning filtering (NDLF) filter is disabled (Hu, ¶34, “on/off control flag for indicating whether to enable the DNN can be signalled to the decoder to further improve the performance of this framework. The on/off control flag can be signalled at the sequence level, picture level, slice level, CTU-row level, CTU level or CU level”).
With regard to Claim 8,
Hu teach the method of claim 1, wherein the rule specifies to apply a non-deep learning filtering (NDLF) filter to samples of a video unit to which the convolutional neural network filter is disabled (Hu, ¶29, “DNN is used for making a pixel-wise in-loop filter on/off decision for each in-loop filter enabled region … with or without other restoration methods in one video coding system, as shown in FIG. 2A and FIG. 2B”, DNN decide not to apply convolutional neural network filter to certain region and the region is still marked as “filter-enabled” meaning other filters are active in that region).
With regard to Claim 11,
Hu teach the method of claim 1, wherein an input information of the convolutional neural network filter includes a mode information and/or other information related to the convolutional neural network filter (Hu, ¶¶33-34, ¶40, “Each picture is decoded using a decoding process comprising one or a combination of a residual decoding process to generate reconstructed residual from the video bitstream, a prediction process to generate a prediction signal related to each picture, a reconstruction process to generate reconstructed picture from the reconstructed residual and the prediction signal, and at least one filtering process applied to the reconstructed picture in step 520. Target signal is processed using DNN (Deep Neural Network) in step 530, where the target signal provided to DNN input corresponds to the reconstructed residual, output from the prediction process, the reconstruction process or said at least one filtering process, or a combination thereof. The output data from DNN output is provided for the decoding process in step”).
With regard to Claim 12,
Hu teach the method of claim 11, wherein the input information includes at least one of reconstructed sample information, partition information, or prediction information (Hu, ¶40, “Each picture is decoded using a decoding process comprising one or a combination of a residual decoding process to generate reconstructed residual from the video bitstream, a prediction process to generate a prediction signal related to each picture, a reconstruction process to generate reconstructed picture from the reconstructed residual and the prediction signal, and at least one filtering process applied to the reconstructed picture in step 520. Target signal is processed using DNN (Deep Neural Network) in step 530, where the target signal provided to DNN input corresponds to the reconstructed residual, output from the prediction process, the reconstruction process or said at least one filtering process, or a combination thereof. The output data from DNN output is provided for the decoding process in step”).
With regard to Claim 13,
Hu teach the method of claim 1, wherein the convolutional neural network filter comprises a loop filter (Hu, Fig. 2a, Fig. 2b).
With regard to Claim 14,
Hu teach the method of claim 1, wherein types of NDLF filters include a deblocking filter or a sample adaptive offset filter or an adaptive loop filter or a cross-component adaptive loop filter (Hu, ¶11, ¶13, “filtering processes may comprise a deblocking filter, SAO (Sample Adaptive Offset), ALF (Adaptive Loop Filter), and any combination of them”).
With regard to Claim 15,
Hu teach the method of claim 1, wherein the performing of the conversion comprises generating the bitstream from the visual media data (Hu, Fig. 2a).
With regard to Claim 16,
Hu teach the method of claim 1, wherein the performing of the conversion comprises generating the visual media data from the bitstream (Hu, Fig. 2b).
With regard to Claim 17,
Claim 17 is similar in scope to claim 1; therefore it is rejected under similar rationale. Hu further disclose an apparatus See at least claim 40.
With regard to Claim 19,
Claim 19 is similar in scope to claim 1; therefore it is rejected under similar rationale. Hu further disclose storage medium storing instruction to be executed using a processor See at least ¶44.
With regard to claims 18 and 20,
Claims 18 and 20 are similar in scope to claim 4; therefore they are rejected under similar rationale.
With regard to Claim 21,
Hu teach the method of claim 1, wherein the rule specifies that the convolutional neural network and each type of non-deep learning filtering (NDLF) filters are used in a mutually exclusive manner (Fig. 3, ¶29, “DNN is used for making a pixel-wise in-loop filter on/off decision for each in-loop filter”, “DNN can be applied after SAO (labelled as point B), DF (labelled as point C), or REC (labelled as point D), with or without other restoration methods in one video coding system”, ¶¶35-36, “DNN is applied after DF, SAO, ALF or other in-loop filters to determine whether the pixels within an in-loop filter enabled region should be modified by the in-loop filter “).
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 22 is rejected under 35 U.S.C. 103 as being unpatentable over by HUANG et al . [US 2018/0249158 A1, hereinafter Hu] in view of Hu et al [US20200204800A1, hereinafter Hu1]
With regard to Claim 22,
Hu teach the method of claim 21, wherein the convolutional neural network filter is applied on I slice, the NDLF filters are turned off for I slice through high level syntax (¶8, “HEVC supports multiple Intra prediction modes and for Intra coded CU, the selected Intra prediction mode is signalled”, ¶¶9-10, ¶30, “ DNN 310 can be applied to the reconstructed residual from IQ+IT 124”, ¶31, “the Intra/Inter prediction signal and the reconstructed residual signal both are the inputs for DNN 410 and the output is the DNN filtered reconstructed pixels as shown in FIG. 4. In this case, the DNN is also used for the reconstruction process”, ¶36, “ DNN is applied … to determine whether the pixels … should be modified by the in-loop filter. … some pixels in the current CTU may remain the same … the other pixels in the current CTU may be modified by SAO”, ¶33, “explicit selection can be signalled at a sequence level, picture level, slice level, CTU (Coding Tree Unit)-row level, CTU level, or CU (Coding Unit) level“, “The implicit selection can be dependent on the slice type”, ¶34, “ an on/off control flag for indicating whether to enable the DNN can be signalled to the decoder to further improve the performance of this framework. The on/off control flag can be signalled at the sequence level, picture level, slice level … ”, ¶29); and
wherein the convolutional neural network filter is applied on B slice (¶¶8, ¶9, “adaptive Intra/Inter video encoder based on HEVC. The Intra/Inter Prediction unit 110 generates Inter prediction based on Motion Estimation (ME)/Motion Compensation (MC) when Inter mode is used”, ¶30, “ DNN 310 can be applied to the reconstructed residual from IQ+IT 124”, ¶36, “ DNN is applied … to determine whether the pixels … should be modified by the in-loop filter. … some pixels in the current CTU may remain the same … the other pixels in the current CTU may be modified by SAO”, ¶31, “the Intra/Inter prediction signal and the reconstructed residual signal both are the inputs for DNN 410 and the output is the DNN filtered reconstructed pixels as shown in FIG. 4. In this case, the DNN is also used for the reconstruction process”, ¶34, “ an on/off control flag for indicating whether to enable the DNN can be signalled to the decoder to further improve the performance of this framework. The on/off control flag can be signalled at the sequence level, picture level, slice level … ”).
Hu does not explicitly teach B slice with temporal layer id equal to 0.
Hu1 teach I slice, B slice with temporal layer id equal to 0, and B slice with temporal layer id equal to 0 (¶4, ¶175, “video slice is coded as an intra-coded (I) slice, intra prediction unit 84 of prediction processing unit 81 may generate prediction data … When the video frame is coded as an inter-coded slice (e.g., B slice or P slice) …”, ¶35, “For instance, the plurality of layers may include a base layer (e.g., with temporal layer ID of 0) and one or more enhancement layers (e.g., each with temporal layer IDs greater than 0)”, ¶39, ¶¶125-126, “ video decoder 30 that only receives or only processes pictures with temporal layer ID of 0, the buffer of video decoder 30 may not include ALF set P4 and ALF set P6. This is because ALF set P4 and ALF set P6 are associated with temporal layer ID 1 and 2, respectively, and this example of video decoder 30 only receives or only processes pictures with temporal layer ID of 0. Accordingly, the buffer of video decoder 30 that stores ALF sets and temporal layer identification values in this example would include: {Alf(P7, layer ID 0), Alf(P5, layer ID 0), and Alf(P3, layer ID 0)}”, ¶127, “The buffer for video decoder 30 may include {Alf(P7, layer ID 0), Alf(P5, layer ID0), and Alf(P3, layer ID 0)} but would also include ALF sets for two earlier decoded pictures since there can be up to 5 ALF sets in the buffer. Therefore, the buffer for video decoder 30 may include {Alf(P7, layer ID 0), Alf(P5, layer ID 0), Alf(P3, layer ID 0), Alf(P2, layer ID0), and Alf(P1, layer ID 0)}”, ¶91, “If the current frame is a P or B frame, then one of the stored set of filters may be used to filter this frame if it leads to better RD cost. A flag is signaled to indicate usage of temporal prediction. If temporal prediction is used, then an index indicating which set of stored filters is used is signaled”).
Hu and Hu1 are analogous art to the claimed invention because they are from a similar field of endeavor of using video encoding and decoding. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hu resulting in resolutions as disclosed by Hu1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Hu as described above to ensure that there is not a mismatch in the ALF sets available to a video encoder and a video decoder and ensure that the list of ALF sets for both the video encoder and the video decoder match (Hu1, ¶7, ¶42).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over by HUANG et al . [US 2018/0249158 A1, hereinafter Hu] in view of GALPIN et al [US 2020/0244997 A1, hereinafter Gal].
With regard to Claim 10,
Hu teach the method of claim 1.
Hu does not explicitly teach bitstream includes information on at least one of a number of different convolutional neural network filters and/or sets of the convolutional neural network filters that are allowed to be applied during the conversion.
Gal teach the bitstream includes information on at least one of a number of different convolutional neural network filters and/or sets of the convolutional neural network filters that are allowed to be applied during the conversion (Abstract, “a multi-branch CNN is used. The multi-branch CNN may include multiple basic CNNs and an identify filter, where each basic CNN or the identity filter is considered as a branch. At the encoder side, the best branch can be chosen, for example, based on RDO. The best branch can be indicated to or be derived at the decoder side”, ¶65).
Hu and Gal are analogous art to the claimed invention because they are from a similar field of endeavor of using video encoding and decoding. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hu resulting in resolutions as disclosed by Gal with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Hu as described above to achieve high compression efficiency, image and video coding schemes (Gal, ¶2).
Response to Arguments
Applicant argue that Huang discloses that the parameters in DNN have to be signalled from the encoder to the decoder. Moreover, paragraph 34 of Huang discloses that an on/off control flag for indicating whether to enable the DNN can be signalled to the decoder. That is, in Huang, the enabling/disabling of the DNN is explicitly signaled. In contrast, the Applicants' independent claims require that the use of the CNN filter to be implicitly derived based on decoding-related information. Therefore, Huang fails to disclose that the rule specifies that use of the convolutional neural network filter is implicitly derived based on decoded information.
Examiner respectfully disagrees, the argument mixes DNN parameters signaling with the determination of whether the DNN is used. Paragraphs 30-32 relate to the structure and parameters of the DNN and do not address how the use of the DNN is determined during decoding. Paragraph 34 discloses that an on/off control flag maybe explicitly signaled, this is only one option, but Huang further explicitly discloses that the use or selection of the DNN maybe implicitly decided at the decoder based on decoded information, including slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information as disclosed in ¶33, “The implicit selection can be dependent on the slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information”. Thus Huang directly teaches the claimed requirement of implicitly deriving CNN usage based on decoded information, regardless of the presence of an alternative explicit signaling. In other words Huang explicitly disclose implicit decoder side selection See at least ¶33, and presence of an alternative explicit signaling (¶34) does not negate the implicit disclosure.
As to the remaining independent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this "Response to Arguments" section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. US 20230254507 A1 filed by Dumas et al. that disclose the usage of Convolutional neural network for encoding and decoding videos See at least Abstract, and ¶11.
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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