CTNF 18/727,505 CTNF 101541 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on 7/9/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1, 5, 14-16, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bross et. al. (United States Patent Application Publication US 2021/0409755 A1) in view of Hannuksela et. al. (European Patent Application EP 3633990 A1) . Regarding claim 1, Bross et. al. discloses a method for video decoding, comprising: receiving a video frame reconstructed based on data received from a bitstream; extracting, from the bitstream, and responsive to the first syntax element indicating that the spatial partition for partitioning the video frame is active: determining a configuration of the spatial partition for partitioning the video frame, wherein the video frame is spatially divided based on the determined configuration of the spatial partition for partitioning the video frame into a plurality of portions (Bross et. al. see abstract, description paragraphs [0023]-[0274]: an encoder for hybrid video coding, the encoder for providing an encoded representation of a video sequence on the basis of an input video content, and the encoder configured to: determine one or more syntax elements related to a portion of the video sequence; select a processing scheme to be applied to the portion of the video sequence based on a property described by one or more syntax elements, the processing scheme for obtaining a sample for a motion-compensated prediction at an integer and/or fractional location within the portion of the video sequence; encode an index indicating the selected processing scheme such that a given encoded index value represents different processing schemes depending on the property described by the one or more syntax elements; and provide, as the encoded representation of the video sequence, a bit stream comprising the one or more syntax elements and the encoded index.). The distinguishing feature a first syntax element indicating whether a spatial partition for partitioning the video frame is active is well known in the art. Hannuksela et. al. explains the process for partitioning the video frame as coded slices, and defining slice boundaries is an operation of the encoder that is commonly known, and Bross et. al. may obtain the portion of the video sequence by splitting the video sequence or video frame or by obtaining the portion from a module for partitioning the video sequence (KSR, Rationale C, Hannuksela et. al. [0069]-[0071], Bross et. al. [0260], Fig. 3)). Bross et. al. fails to disclose determining a plurality of parameter sets of a neural network, and applying the neural network to the video frame, determining a plurality of parameter sets of the neural network is applied to the plurality of portions in accordance with the determined plurality of parameter sets. Hannuksela et. al. teaches determining a plurality of parameter sets of a neural network, and applying the neural network to the video frame, determining a plurality of parameter sets of the neural network is applied to the plurality of portions in accordance with the determined plurality of parameter sets (Hannuksela et. al. abstract, description paragraphs [0064]-[0202]). This is important to the claimed invention because the neural network parameters need to be defined to work properly and this improves the efficiency of encoding the video frame with the neural network. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al. and Hannuksela et. al. so that the neural network is included in the video encoding solution. Regarding claim 15, which is an apparatus claim for video decoding, comprising circuitry configured to carry out the method of claim 1, which the rejection analysis is incorporated herein. Regarding claim 5, Bross et. al. and Hannuksela et. al. disclose the method of claim 1, and Bross et. al. further discloses wherein the step of determining the configuration further comprises using a predefined configuration as the determined configuration, and the predefined configuration is: a horizontal partition, by which the video frame is divided into an upper portion and a lower portion, a vertical partition, by which the video frame is divided into a left portion and a right portion, a quadrant partition, by which the video frame is divided into an upper left portion, an upper right portion, a lower left portion, and a lower right portion, or a block-wise partition, by which the video frame is divided into a predefined number of portions, and the predefined number is neither 2 nor 4 (Bross et. al. see abstract, description paragraphs [0023]-[0274]: an encoder for hybrid video coding, the encoder for providing an encoded representation of a video sequence on the basis of an input video content, and the encoder configured to: determine one or more syntax elements related to a portion of the video sequence; select a processing scheme to be applied to the portion of the video sequence based on a property described by one or more syntax elements, the processing scheme for obtaining a sample for a motion-compensated prediction at an integer and/or fractional location within the portion of the video sequence; encode an index indicating the selected processing scheme such that a given encoded index value represents different processing schemes depending on the property described by the one or more syntax elements; and provide, as the encoded representation of the video sequence, a bit stream comprising the one or more syntax elements and the encoded index.). Regarding claim 14, Bross et. al. and Hannuksela et. al. disclose the method of claim 1, and Hannuksela et. al. further discloses wherein the video frame is received from an output of a reconstruction unit (REC), an adaptive loop filter (ALF), a sample adaptive offset filter (SAO), or a deblocking filter (DF) (Hannuksela et. al. abstract, description paragraphs [0064]-[0202]: the video frame is received from an output of ALF or SAO. The filtering may include one more of the following: deblocking, sample adaptive offset (SAO), and/or adaptive loop filtering (ALF). The adaptive loop filter (ALF) is another method to enhance quality of the reconstructed samples)). Regarding claim 16, Bross et. al. discloses a method for video encoding, comprising: receiving data representing a video frame; determining a configuration of a spatial partition for partitioning the video frame; wherein the video frame is spatially divided based on the determined configuration into a plurality of portions, and the neural network is applied to the plurality of portions in accordance with the determined plurality of parameter sets (Bross et. al. see abstract, description paragraphs [0023]-[0274]: an encoder for hybrid video coding, the encoder for providing an encoded representation of a video sequence on the basis of an input video content, and the encoder configured to: determine one or more syntax elements related to a portion of the video sequence; select a processing scheme to be applied to the portion of the video sequence based on a property described by one or more syntax elements, the processing scheme for obtaining a sample for a motion-compensated prediction at an integer and/or fractional location within the portion of the video sequence; encode an index indicating the selected processing scheme such that a given encoded index value represents different processing schemes depending on the property described by the one or more syntax elements; and provide, as the encoded representation of the video sequence, a bit stream comprising the one or more syntax elements and the encoded index.). Bross et. al. fails to disclose determining a plurality of parameter sets of a neural network; and applying the neural network to the video frame and signaling a plurality of syntax elements associated with the spatial partition for partitioning the video frame. Hannuksela et. al. teaches determining a plurality of parameter sets of a neural network; and applying the neural network to the video frame and signaling a plurality of syntax elements associated with the spatial partition for partitioning the video frame (Hannuksela et. al. abstract, description paragraphs [0064]-[0202]). This is important to the claimed invention because the neural network parameters need to be defined to work properly, along with the syntax elements for spatial partitioning the video frame, and this improves the efficiency of encoding the video frame with the neural network. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al. and Hannuksela et. al. so that the neural network is included in the video encoding solution. Regarding claim 20, Bross et. al. and Hannuksela et. al. discloses the method of claim 17, and Hannuksela et. al. further discloses wherein the data representing the reconstructed video frame is obtained from an output of a reconstruction unit (REC), an adaptive loop filter (ALF), a sample adaptive offset filter (SAO), or a deblocking filter (DF) (Hannuksela et. al. abstract, description paragraphs [0064]-[0202]: the video frame is received from an output of ALF or SAO. The filtering may include one more of the following: deblocking, sample adaptive offset (SAO), and/or adaptive loop filtering (ALF). The adaptive loop filter (ALF) is another method to enhance quality of the reconstructed samples)) . 07-22-aia AIA Claim (s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bross et. al. (United States Patent Application Publication US 2021/0409755 A1) in view of Hannuksela et. al. (European Patent Application EP 3633990 A1) as applied to claim 1 above, and further in view of Xiu et. al. (United States Patent US 12,088,850 B2) . Regarding claim 2, Bross et. al. and Hannuksela et. al. disclose the method of claim 1, however Bross et. al. and Hannuksela et. al. fail to disclose wherein the step of determining the configuration further comprises: extracting, from the bitstream, a second syntax element indicating whether a new configuration is available, when the second syntax element indicates that no new configuration is available, using a configuration determined for a previous video frame as the determined configuration, and when the second syntax element indicates that a new configuration is available, obtaining the new configuration from the bitstream, and using the obtained configuration as the determined configuration. Xiu et. al. teaches wherein the step of determining the configuration further comprises: extracting, from the bitstream, a second syntax element indicating whether a new configuration is available, when the second syntax element indicates that no new configuration is available, using a configuration determined for a previous video frame as the determined configuration, and when the second syntax element indicates that a new configuration is available, obtaining the new configuration from the bitstream, and using the obtained configuration as the determined configuration (Xiu et. al. abstract, Fig. 7-8). This is important to the claimed invention because the second syntax element is a defined piece of coded information that the encoder sends and the decoder interprets. This further adds to the machine learning model for video coding based on the sliced video frame. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al., Hannuksela et. al. and Xiu et. al. to include the new configuration based on the second syntax element as part of the solution of the method. Regarding claim 3, Bross et. al., Hannuksela et. al., and Xiu et. al. disclose the method of claim 2, and Xiu et. al. further discloses wherein the step of obtaining the new configuration further comprises: extracting, from the bitstream, a first group of one or more further syntax elements indicating a particular configuration, and identifying the particular configuration as the new configuration (Xiu et. al. col. 38, lines 1-36, Fig. 8, the second syntax element equal to 1 specifies that the picture referring to, corresponding to, or associated with the PH is an IRAP picture or a GDR picture, and the second syntax element equal to 0 specifies that the picture referring to, corresponding to, or associated with the PH is neither an IRAP picture nor a GDR picture)). Regarding claim 4, Bross et. al, Hannuksela et. al, and Xiu et. al. disclose the method of claim 3, and Bross et. al. further discloses wherein the particular configuration is: a horizontal partition, by which the video frame is divided into an upper portion and a lower portion, a vertical partition, by which the video frame is divided into a left portion and a right portion, a quadrant partition, by which the video frame is divided into an upper left portion, an upper right portion, a lower left portion, and a lower right portion, or a block-wise partition, by which the video frame is divided into a particular number of portions, and the particular number is neither 2 nor 4 (Bross et. al. see abstract, description paragraphs [0023]-[0274]: an encoder for hybrid video coding, the encoder for providing an encoded representation of a video sequence on the basis of an input video content, and the encoder configured to: determine one or more syntax elements related to a portion of the video sequence; select a processing scheme to be applied to the portion of the video sequence based on a property described by one or more syntax elements, the processing scheme for obtaining a sample for a motion-compensated prediction at an integer and/or fractional location within the portion of the video sequence; encode an index indicating the selected processing scheme such that a given encoded index value represents different processing schemes depending on the property described by the one or more syntax elements; and provide, as the encoded representation of the video sequence, a bit stream comprising the one or more syntax elements and the encoded index.) . 07-22-aia AIA Claim (s) 6-8, 10, 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Bross et. al. (United States Patent Application Publication US 2021/0409755 A1) in view of Hannuksela et. al. (European Patent Application EP 3633990 A1) as applied to claim 1 above, and further in view of Zhang et. al. (United States Patent US 9,615,090 B2) . Regarding claim 6, Bross et. al. and Hannuksela et. al. disclose the method of claim 1, however, Bross et. al. and Hannuksela et. al. fail to disclose wherein the step of determining the plurality of parameter sets further comprises: extracting, from the bitstream, a third syntax element indicating whether a new plurality of parameter sets are available, when the third syntax element indicates that no new parameter sets are available, using a plurality of parameter sets determined for a previous video frame as the determined plurality of parameter sets, and when the third syntax element indicates that a new plurality of parameter sets are available, obtaining the new plurality of parameter sets from the bitstream, and using the obtained plurality of parameter sets as the determined plurality of parameter sets. Zhang et. al. teaches wherein the step of determining the plurality of parameter sets further comprises: extracting, from the bitstream, a third syntax element indicating whether a new plurality of parameter sets are available, when the third syntax element indicates that no new parameter sets are available, using a plurality of parameter sets determined for a previous video frame as the determined plurality of parameter sets, and when the third syntax element indicates that a new plurality of parameter sets are available, obtaining the new plurality of parameter sets from the bitstream, and using the obtained plurality of parameter sets as the determined plurality of parameter sets (Zhang et. al. col. 9, lines 10-45, a video decoder performs a parsing operation to obtain syntax elements from the bitstream. In general, a syntax element is an element of data represented in a bitstream. In addition, the video decoder performs a decoding operation that uses syntax elements obtained from the bitstream to reconstruct sample blocks of the video data). This is important to the claimed invention because it further defines the structure and format of the video data that is transmitted. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to combine the teachings of Bross et. al., Hannuksela et. al., and Zhang et. al. so that additional syntax elements are included in the method of video coding. Regarding claim 7, Bross et. al., Hannuksela et. al., and Zhang et. al. disclose the method of claim 6, and Zhang et. al. further disclose wherein the step of obtaining the new plurality of parameter sets further comprises: extracting, from the bitstream, a second group of one or more further syntax elements indicating a particular plurality of parameter sets, and identifying the particular plurality of parameter sets as the new plurality of parameter sets (Zhang et. al. col. 9, lines 10-45, a video decoder performs a parsing operation to obtain syntax elements from the bitstream. In general, a syntax element is an element of data represented in a bitstream. In addition, the video decoder performs a decoding operation that uses syntax elements obtained from the bitstream to reconstruct sample blocks of the video data). Regarding claim 8, Bross et. al., Hannuksela et. al., and Zhang et. al. disclose the method of claim 6, and Zhang et. al. further discloses wherein the step of obtaining the new plurality of parameter sets further comprises: extracting, from the bitstream, a second group of one or more further syntax elements indicating a particular previous video frame and a replacement specification, the replacement specification defining a replacement to some of a plurality of parameter sets determined for the particular previous video frame, and generating, based on the plurality of parameter sets determined for the particular previous video frame and the replacement specification, the new plurality of parameter sets (Zhang et. al. col. 1, lines 32-45, video compression techniques perform spatial (intra-picture) prediction and/or temporal (inter-picture) prediction to reduce or remove redundancy inherency in video sequences). Regarding claim 10, Bross et. al., Hannuksela et. al, and Zhang et. al. disclose the method of claim 6, and Zhang et. al. further discloses wherein the step of obtaining the new plurality of parameter sets further comprises: extracting, from the bitstream, a second group of one or more further syntax elements indicating a particular plurality of parameter sets, extracting, from the bitstream, a third group of one or more further syntax elements indicating a sharing specification, the sharing specification defining that some of the particular plurality of parameter sets are shared among two or more of the plurality of portions, and generating the new plurality of parameter sets based on the particular plurality of parameter sets, in accordance with the sharing specification (Zhang et. al. col 24, lines 43-54, because neighboring blocks are likely to share almost the same motion and disparity information in video coding, the current block can use the motion vector information in the neighboring blocks as predictors of the disparity vector of the current block). Regarding claim 12, Bross et. al., Hannuksela et. al., and Zhang et. al. disclose the method of claim 6, Zhang et. al. further disclose wherein the step of obtaining the new plurality of parameter sets further comprises: extracting, from the bitstream, a second group of one or more further syntax elements indicating a particular plurality of parameter sets, and generating the new plurality of parameter sets based on the particular plurality of parameter sets, in accordance with a predefined sharing specification defining that some of the particular plurality of parameter sets are shared among two or more of the plurality of portions (Zhang et. al. col 24, lines 43-54, because neighboring blocks are likely to share almost the same motion and disparity information in video coding, the current block can use the motion vector information in the neighboring blocks as predictors of the disparity vector of the current block). Regarding claim 13, Bross et. al. and Hannuksela et. al. disclose the method of claim 1, however, Bross et. al. and Hannuksela et. al. fail to disclose wherein the step of determining the plurality of parameter sets further comprises: extracting, from the bitstream, a fourth group of one or more further syntax elements indicating a correspondence specification, the correspondence specification defining a correspondence between one of the plurality of portions and each of the determined plurality of parameter sets, and the step of applying the neural network further comprises: applying, based on the correspondence specification, the neural network having one of the determined plurality of parameter sets to a corresponding one of the plurality of portions. Zhang et. al. teaches wherein the step of determining the plurality of parameter sets further comprises: extracting, from the bitstream, a fourth group of one or more further syntax elements indicating a correspondence specification, the correspondence specification defining a correspondence between one of the plurality of portions and each of the determined plurality of parameter sets, and the step of applying the neural network further comprises: applying, based on the correspondence specification, the neural network having one of the determined plurality of parameter sets to a corresponding one of the plurality of portions (Zhang et. al. col 21, lines 25-48). This is important to the claimed invention because it further defines the structure and format of the video data that is transmitted. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to combine the teachings of Bross et. al., Hannuksela et. al., and Zhang et. al. so that additional syntax elements are included in the method of video coding along with the correspondence specification . 07-22-aia AIA Claim (s) 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bross et. al. (United States Patent Application Publication US 2021/0409755 A1), Hannuksela et. al. (European Patent Application EP 3633990 A1), and Zhang et. al. (United States Patent US 9,615,090 B2) as applied to claim s 8 and 10 above, and further in view of Letunovskiy et. al. (United States Patent Application Publication US 2024/0064319 A1) . Regarding claim 9, Bross et. al., Hannuksela et. al., and Zhang et. al. disclose the method of claim 8, however the combination of Bross et. al., Hannuksela et. al., and Zhang et. al. fail to disclose wherein the replacement is at a layer-level, a filter-level, or an element-of-filter-level of the neural network. Letunovskiy et. al. teaches wherein the replacement is at a layer-level, a filter-level, or an element-of-filter-level of the neural network (Letunovskiy et. al. [0232]-[0248], Figure 21, 13, train the CNN filter on the input-reconstructed picture(s), output-input video/picture(s) with a mean square error (MSE) loss function, or other loss function. In other words, the input to the NN should be the reconstructed picture (or pictures) and the output of the NN should be the original picture (or pictures) which is being encoded. The encoder may determine which layers will be updated, layers 1310 are selected to be updated (updatable layers), meaning their respective parameters (e.g. weights or weight changes) may later (may be updated) during the training of the NN.). This is important to the claimed invention because it describes how the neural network is trained. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al., Hannuksela et. al., Zhang et. al. and Letunovskiy et. al. to show how the neural network is trained. Regarding claim 11, Bross et. al., Hannuksela et. al., and Zhang et. al. disclose the method of claim 10, however the combination of Bross et. al., Hannuksela et. al., and Zhang et. al. fail to disclose wherein the some of the particular plurality of parameter sets are shared among the two or more of the plurality of portions at a layer-level, a filter-level, or an element-of-filter-level of the neural network. Letunovskiy et. al. teaches wherein the some of the particular plurality of parameter sets are shared among the two or more of the plurality of portions at a layer-level, a filter-level, or an element-of-filter-level of the neural network (Letunovskiy et. al. [0232]-[0248], Figure 21, 13, train the CNN filter on the input-reconstructed picture(s), output-input video/picture(s) with a mean square error (MSE) loss function, or other loss function. In other words, the input to the NN should be the reconstructed picture (or pictures) and the output of the NN should be the original picture (or pictures) which is being encoded. The encoder may determine which layers will be updated, layers 1310 are selected to be updated (updatable layers), meaning their respective parameters (e.g. weights or weight changes) may later (may be updated) during the training of the NN.). This is important to the claimed invention because it describes how the neural network is trained. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al., Hannuksela et. al., Zhang et. al. and Letunovskiy et. al. to show how the neural network is trained . 07-22-aia AIA Claim (s) 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bross et. al. (United States Patent Application Publication US 2021/0409755 A1) in view of Hannuksela et. al. (European Patent Application EP 3633990 A1) as applied to claim 16 above, and further in view of Letunovskiy et. al. (United States Patent Application Publication US 2024/0064319 A1) . Regarding claim 17, Bross et. al. and Hannuksela et. al. disclose the method of claim 16. However, Bross et. al. and Hannuksela et. al. fail to disclose further comprising: training the neural network through a cascade of N (N ≥2) training stages, so as to learn each of the plurality of parameter sets, wherein each training stage comprises the neural network to be trained, given 2 ≤ n ≤ N, an input of an n-th training stage is derived based on an output of an (n-1)- th training stage, data representing a reconstructed video frame is used as training data inputted to a first training stage, data representing an original video frame of the reconstructed video frame is used as a ground truth, and a total loss is calculated as a weighted sum of losses of the N training stages. Letunovskiy et. al. further comprising: training the neural network through a cascade of N (N ≥2) training stages, so as to learn each of the plurality of parameter sets, wherein each training stage comprises the neural network to be trained, given 2 ≤ n ≤ N, an input of an n-th training stage is derived based on an output of an (n-1)- th training stage, data representing a reconstructed video frame is used as training data inputted to a first training stage, data representing an original video frame of the reconstructed video frame is used as a ground truth, and a total loss is calculated as a weighted sum of losses of the N training stages (Letunovskiy et. al. [0232]-[0248], Figure 21, 13, train the CNN filter on the input-reconstructed picture(s), output-input video/picture(s) with a mean square error (MSE) loss function, or other loss function. In other words, the input to the NN should be the reconstructed picture (or pictures) and the output of the NN should be the original picture (or pictures) which is being encoded. The encoder may determine which layers will be updated, layers 1310 are selected to be updated (updatable layers), meaning their respective parameters (e.g. weights or weight changes) may later (may be updated) during the training of the NN.). This is important to the claimed invention because it describes how the neural network is trained. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Bross et. al., Hannuksela et. al. and Letunovskiy et. al. to show how the neural network is trained. This is well known in the art. Regarding claim 18, Bross et. al., Hannuksela et. al. and Letunovskiy et. al. disclose the method of claim 17, and Letunovskiy et. al. further disclose wherein the neural networks in the N training stages are developed with a same parameter set, or with N parameter sets specific to individual training stages (Letunovskiy et. al. [0232]-[0248], Figure 21, 13, train the CNN filter on the input-reconstructed picture(s), output-input video/picture(s) with a mean square error (MSE) loss function, or other loss function. In other words, the input to the NN should be the reconstructed picture (or pictures) and the output of the NN should be the original picture (or pictures) which is being encoded. The encoder may determine which layers will be updated, layers 1310 are selected to be updated (updatable layers), meaning their respective parameters (e.g. weights or weight changes) may later (may be updated) during the training of the NN.). Regarding claim 19, Bross et. al., Hannuksela et. al. and Letunovskiy et. al. disclose the method of claim 17, and Letunovskiy et. al. further disclose wherein the neural networks in the N training stages are developed with parameter sets partially shared among the N training stages at a layer-level, a filter-level, or an element-of-filter level (Letunovskiy et. al. [0232]-[0248], Figure 21, 13, train the CNN filter on the input-reconstructed picture(s), output-input video/picture(s) with a mean square error (MSE) loss function, or other loss function. In other words, the input to the NN should be the reconstructed picture (or pictures) and the output of the NN should be the original picture (or pictures) which is being encoded. The encoder may determine which layers will be updated, layers 1310 are selected to be updated (updatable layers), meaning their respective parameters (e.g. weights or weight changes) may later (may be updated) during the training of the NN.). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JESSICA YIFANG LIN/Examiner, Art Unit 2668 April 16, 2026 /VU LE/Supervisory Patent Examiner, Art Unit 2668 Application/Control Number: 18/727,505 Page 2 Art Unit: 2668 Application/Control Number: 18/727,505 Page 3 Art Unit: 2668 Application/Control Number: 18/727,505 Page 4 Art Unit: 2668 Application/Control Number: 18/727,505 Page 5 Art Unit: 2668 Application/Control Number: 18/727,505 Page 6 Art Unit: 2668 Application/Control Number: 18/727,505 Page 7 Art Unit: 2668 Application/Control Number: 18/727,505 Page 8 Art Unit: 2668 Application/Control Number: 18/727,505 Page 9 Art Unit: 2668 Application/Control Number: 18/727,505 Page 10 Art Unit: 2668 Application/Control Number: 18/727,505 Page 11 Art Unit: 2668 Application/Control Number: 18/727,505 Page 12 Art Unit: 2668 Application/Control Number: 18/727,505 Page 13 Art Unit: 2668 Application/Control Number: 18/727,505 Page 14 Art Unit: 2668 Application/Control Number: 18/727,505 Page 15 Art Unit: 2668 Application/Control Number: 18/727,505 Page 16 Art Unit: 2668 Application/Control Number: 18/727,505 Page 17 Art Unit: 2668 Application/Control Number: 18/727,505 Page 18 Art Unit: 2668
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