DETAILED ACTION
This office action is responsive to the Request for Continued Examination filed 9/23/2026. 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 .
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.
Claims 1-2, 4-8, 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over HUANG et al . [US 2018/0249158 A1, hereinafter Hu] in view of Wang et al . [US 2022/0103864 A1, hereinafter Wang] as evidenced by RTP Payload Format for High Efficiency Video Coding (HEVC) RFC 7798 “RFC 7798 - RTP Payload Format for High Efficiency Video Coding (HEVC)” hereinafter D1.
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”, ¶¶40-41, “receives a video bitstream corresponding to one or more pictures in a video sequence in step 510. Each picture is decoded using a decoding process comprising … at least one filtering process applied to the reconstructed picture in step 520”),
wherein the performing of the conversion includes selectively applying a convolutional neural network filter during the conversion based on a rule (¶29, “According to the present invention, the DNN is utilized as an image restoration technique in the video coding system“, “the DNN is used for making a pixel-wise in-loop filter on/off decision”, “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”, ¶32, “As mentioned before, there are lots of parameters or variables in DNN, such as the number of layers or depth, filter coefficients, filter footprints, non-linear operations, and so on”, ¶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, ¶14);
wherein the rule specifies whether and/or how the convolutional neural network filter is applied (¶33, “However, in order to adapt to the characteristics of variant input pictures, more than one set of pre-defined parameters can be supported in a video coding system”, “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. The on/off control flag can be signalled at the sequence level, picture level, slice level, CTU-row level, CTU level or CU level”, ¶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”, ¶10, “At the decoder side, an Entropy Decoding unit 160 is used to recover coded symbols or syntaxes from the bitstream”), and
the use of the convolutional neural network filter is conditioned on a slice type (¶33, “The implicit selection can be dependent on the slice type”, claim 12, “the selection is dependent on a slice type, quantization parameter, prediction mode, quantized coefficients, the reconstructed residual, predictors, reconstructed pixels, motion information, or any combination thereof”).
Hu does not explicitly teach
Wang teach a method of processing visual media data (¶6), comprising:
rule specifies that use of the convolutional neural network filter is implicitly
derived based on decoded information (¶151, “video decoder 300 may explicitly or implicitly determine whether to perform neural network model filtering using NN models 322, e.g., using any or all of the various techniques discussed herein”, ¶83, “ video encoder 200 and/or video decoder 300 may select one or more of the multiple filtering models to perform filtering. Video encoder 200 and video decoder 300 may implicitly derive the selection of NN models based on information in bitstreams (e.g., based on quantization parameter (QP))”, ¶89, “for each picture, video encoder 200 and video decoder 300 may derive a ‘model selection QP’ and select the models with the QP value that is closest to the ‘model selection QP’ for the current picture. In this case, no additional information needs to be signaled”, ¶91, “As noted above, switching off NN based modeling can be used as a candidate of choice, in any or all of the various examples discussed above”, ¶85, “Switching off the filtering process can be used as a candidate. When ‘switching off’ is selected for a target area, video encoder 200 and video decoder 300 do not apply any filter and the output signal is the same as the input signal”), and
the use of the convolutional neural network filter is conditioned on a temporal layer id (¶155, “ filter unit 312 may further determine whether to enable or disable the use of NN models 322 for, e.g., a particular region of a decoded picture, an entire decoded picture, a sequence of decoded pictures, or the like”, “Entropy decoding unit 302 may decode the data representing whether the use NN models 322 is enabled or disabled, e.g., in one or more of a video parameter set (VPS), a sequence parameter set (SPS), a picture parameter set (PPS), a picture header, a slice header, an adaptation parameter set (APS), an intra period level, a group of pictures (GOP) level, a temporal layer level in the GOP, a picture level, a slice level …”, ¶86, “The possible levels at which the filter model index is signaled include … group of pictures (GOP) level, temporal layer level in the GOP, picture level, slice level, CTU level …”, ¶72, “FIG. 4 is a conceptual diagram illustrating a hierarchical prediction structure 150 using a group of pictures (GOP) size of 16”, In High Efficiency Video Coding (HEVC / H.265), every temporal sub-layer is explicitly identified using a 3-bit syntax element called nuh_temporal_id_plus1 located directly inside the 2-byte Network Abstraction Layer (NAL) unit header accordingly a decoder operating according to HEVC as disclosed in at least (¶20, ¶38), used to identify the temporal layer of each picture using its decoded temporal layer as evidenced by D1 See sections “3.1.1 Definitions from the HEVC Specification“, sub-layer: A temporal scalable layer of a temporal scalable bitstream consisting of VCL NAL units with a particular value of the TemporalId variable, and the associated non-VCL NAL units. “1.1.4 NAL Unit Header“, TID field, therefore a decoder operating according to HEVC as disclosed by Wang identifies the temporal layer of each picture from its decoded temporal layer ID, and Wang usage of the neural network filter at “a temporal layer level in the GOP” condition that usage on the temporal layer ID (¶¶ 86, 129, 155)).
Hu and Wang are analogous art to the claimed invention because they are from a similar field of endeavor of neural network filtering of pictures in video coding. 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 solve the problem of pre-trained neural network filters that may not be optimal for specific sequences of video data and may even degraded quality (Wang, ¶21, ¶82, ¶94).
With regard to Claim 2,
Hu-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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-Wang 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 (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 “).
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 Wang et al . [US 2022/0103864 A1, hereinafter Wang] as evidenced by RTP Payload Format for High Efficiency Video Coding (HEVC) RFC 7798 “RFC 7798 - RTP Payload Format for High Efficiency Video Coding (HEVC)” hereinafter D1 in view of Hu et al [US20200204800A1, hereinafter Hu1]
With regard to Claim 22,
Hu-Wang 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 (Hu, ¶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 (Hu ¶¶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-Wang 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-Wang 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-Wang 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-Wang 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 Wang et al . [US 2022/0103864 A1, hereinafter Wang] as evidenced by RTP Payload Format for High Efficiency Video Coding (HEVC) RFC 7798 “RFC 7798 - RTP Payload Format for High Efficiency Video Coding (HEVC)” hereinafter D1 in view of GALPIN et al [US 2020/0244997 A1, hereinafter Gal].
With regard to Claim 10,
Hu-Wang teach the method of claim 1.
Hu-Wang 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-Wang 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-Wang 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-Wang 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 implicit selection of pre-defined parameter sets of DNN can be dependent on the slice type, and that an on/off control flag indicates whether to enable the DNN can be signaled at the slice level. However, neither the implicit selection of pre-defined parameter sets nor the signaling level (i.e., signaling granularity) of the on/off control flag corresponds to the use (or application) of a convolutional neural network filter.
Examiner respectfully disagrees,
First, Applicant’s argument presumes that “use” of the CNN means only whether the filter is turned on. The claims does not support that narrow reading for the term. Consistent with the applicant’s specification, which describe “whether and/or how to use the CNN filters” maybe controlled at video unit (Publication of the application ¶166). The CNN information may comprise not only an indication of enabling or disabling the CNN filter, but also which kind of CNN filter is applied, CNN filter parameters, and CNN models (Publication of the application ¶¶167-174). CNN information maybe signaled at video unit level (Publication of the application ¶175). Thus the usage of the CNN filter may be implicitly derived as disclosed in C is an alternative to signalling the CNN information, which include how the filter is used, and not only if it on or off. The on/off introduced after as an example and do not limit the claim (Publication of the application ¶¶180-187)
Second, Huang expressly teaches that more than one set of pre-defined DNN parameters maybe supported. Each predefined parameter set effectively operates as a distinct DNN. Huang disclose that “selection of pre-defined parameter sets can be explicitly signalled to the decoder or implicitly decided at decoder”, where “implicit selection can be dependent on the slice type, quantization parameter, prediction mode, quantized coefficients, reconstructed residual, predictors, reconstructed pixels, or motion information” (¶33). Each predefined parameter set is a distinct DNN filter. Therefore, Huang Implicit selection at the decoder based on slice type the DNN filter to apply is use of the filter that is implicitly derived from decoded information and conditioned on slice type. Also, Huang disclose alternatives and "[t]he prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004) (MPEP 2123 II).
Third, even under applicant’s narrow definition for the term “use”, Wang teached that the decoder “may explicitly or implicitly determine whether to perform neural network model filtering using NN models” (¶151) including deriving the NN model selection from information in bitstream (¶83, ¶89, ¶91).
Fourth, regarding the signaling level of Huang on/off control flag does not correspond to the use of CNN filter. The rejection does not rely on the signaling level of Huang on/off flag to teach the slice type. Huang ¶34 is relied on to show that the DNN is selectively applied based on a rule (whether DNN is enabled). The conditioning on slice type is disclosed in ¶33 as clarified earlier.
Applicant argue that Huang discloses fails to disclose that the value of the on/off control flag indicates whether to enable the DNN is dependent on the slice type.
Examiner respectfully disagrees,
The claim does not recite or require a value of an on/off control flag to indicate whether to enable the DNN is dependent on the slice type. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., value of the on/off control flag indicates whether to enable the DNN is dependent on the slice type) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argue that Huang disclosure is totally silent with respect to the temporal layer id.
Examiner respectfully disagrees,
Huang is not relied on to teach the argued limitation. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant argue that Hu fails to make up for the shortcomings of Huang. Indeed, Hu discloses multiple slice types (e.g., I slice, B slice, and P slice) and a temporal layer ID. However, the temporal layer ID recited in Hu is used for determining which ALF sets (i.e., ALF parameter sets) are not included in a buffer of a video decoder. That is, the temporal ID in Hu is not used for determining the use of the convolutional neural network filter. Galpin fails to make up for the shortcomings of Huang and/or Hu. Indeed, Galpin is totally silent with respect to a temporal layer id. As such, Galpin fails to disclose that the use of the convolutional neural network filter is conditioned on a slice type and a temporal layer id. Therefore, the combination of Huang, Hu, and Galpin fails to disclose at least one element of independent claims 1, 17, and 19, and consequently fails to render obvious claims 1-2, 4-8 and 10-22
Applicant’s arguments with respect to claims independent claims 1, 17, and 19, and consequently fails to render obvious claims 1-2, 4-8 and 10-22have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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)).
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
