CTNF 18/991,939 CTNF 74498 DETAILED ACTION 1. This is a first office action in response to application no. 18/991,939 filed on December 23, 2024 in which claims 1-20 are presented for examination. 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. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 2. 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 3. 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. 07-21-aia AIA 4. Claim s 1, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Application Publication no. 2019/0132591) in view of Thiagarajan et al. (US Patent Application Publication no. 2018/0084280) . Regarding claim 1, Zhang discloses a method comprising: generating a feature vector for a current frame of a video sequence (See Zhang Fig. 3, item 313 and [0033]); inputting the feature vector and a target bitrate to a machine learning model to generate a modeled quantization parameter (QP) for the current frame (See Zhang Fig. 3, items 313, 102 and 114); generating a further feature vector for a subsequent frame of the video sequence (See Zhang [0055]-[0057] for subsequent implementation ); inputting the further feature vector and the target bitrate to the machine learning model to generate a further modeled QP for the subsequent frame (See Zhang [0055], [0046]); determining an estimated group QP for the current frame based on the modeled QP and the further modeled QP (See Fig. 7 with P1 and Pn for the group QP, and [0050]); determining the estimated group QP is a within a particular sub-range of a plurality of sub-ranges of an available QP range (See Zhang [0050] The QP values may be each QP value in a range of QP values allowed by a standard (e.g., 1 to 51 for HEVC), a subset thereof , or any suitable selection of QP values . ). It is noted that Zhang is silent about selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream. However, Thiagarajan teaches selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream (See Thiagarajan [0054] “the quantization matrix may be selected from among 52 quantization matrices provided for by the H.264 standard and specified by a quantization parameter, such as a QP value, that ranges between 0 and 51 and is expressed in a header of a compressed video bitstream” ). Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Zhang to incorporate Thiagarajan’s teachings to selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream. The motivation for performing such a modification in Zhang is to dynamically forming a modified quantization matrix during video compression in order to minimize compression noise. As per claim 13, Zhang discloses an apparatus comprising a memory to store a current frame and a subsequent frame of a video sequence; and one or more processors coupled to the memory, the one or more processors (See Zhang[0021]) to: generate a feature vector for the current frame (See Zhang Fig. 3, item 313 and [0033]); input the feature vector and a target bitrate to a machine learning model to generate a modeled quantization parameter (QP) for the current frame (See Zhang Fig. 3, items 313, 102 and 114); generate a further feature vector for the subsequent frame (See Zhang [0055]-[0057] for subsequent implementation ); input the further feature vector and the target bitrate to the machine learning model to generate a further modeled QP for the subsequent frame (See Zhang [0055], [0046]); determine an estimated group QP for the current frame based on the modeled QP and the further modeled QP (See Fig. 7 with P1 and Pn for the group QP, and [0050]); determine the estimated group QP is a within a particular sub-range of a plurality of sub-ranges of an available QP range (See Zhang [0050] The QP values may be each QP value in a range of QP values allowed by a standard (e.g., 1 to 51 for HEVC), a subset thereof , or any suitable selection of QP values . ). It is noted that Zhang is silent about selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream. However, Thiagarajan teaches selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream (See Thiagarajan [0054] “the quantization matrix may be selected from among 52 quantization matrices provided for by the H.264 standard and specified by a quantization parameter, such as a QP value, that ranges between 0 and 51 and is expressed in a header of a compressed video bitstream” ). Regarding claim 18, Zhang discloses one or more non-transitory machine readable media comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device (See Zhang[0021]) to perform video coding by: generating a feature vector for a current frame of a video sequence (See Zhang Fig. 3, item 313 and [0033]); inputting the feature vector and a target bitrate to a machine learning model to generate a modeled quantization parameter (QP) for the current frame (See Zhang Fig. 3, items 313, 102 and 114); generating a further feature vector for a subsequent frame of the video sequence (See Zhang [0055]-[0057] for subsequent implementation ); inputting the further feature vector and the target bitrate to the machine learning model to generate a further modeled QP for the subsequent frame (See Zhang [0055], [0046]); determining an estimated group QP for the current frame based on the modeled QP and the further modeled QP (See Fig. 7 with P1 and Pn for the group QP, and [0050]); determining the estimated group QP is a within a particular sub-range of a plurality of sub-ranges of an available QP range (See Zhang [0050] The QP values may be each QP value in a range of QP values allowed by a standard (e.g., 1 to 51 for HEVC), a subset thereof , or any suitable selection of QP values . ) It is noted that Zhang is silent about selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream. However, Thiagarajan teaches selecting a quantization matrix for the current frame from a plurality of available quantization matrices based on the estimated group QP being within the particular sub-range; and encoding the current frame using the selected quantization matrix to generate at least a portion of a bitstream (See Thiagarajan [0054] “the quantization matrix may be selected from among 52 quantization matrices provided for by the H.264 standard and specified by a quantization parameter, such as a QP value, that ranges between 0 and 51 and is expressed in a header of a compressed video bitstream” ) . 07-22-aia AIA 5. Claim s 2-4 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Application Publication no. 2019/0132591) in view of Thiagarajan et al. (US Patent Application Publication no. 2018/0084280) as applied to claim s 1 and 13 above, and further in view of Nagaraj et al. (US Patent Application Publication no. 2010/0150223) . Regarding claims 2 and 14, the combination of Zhang and Thiagarajan is silent about applying look ahead video analysis on the current frame and the subsequent frame to generate analytics data, wherein the vector is generated based on the analytics data. However, Nagaraj teaches applying look ahead video analysis on the current frame and the subsequent frame to generate analytics data, wherein the vector is generated based on the analytics data (See Nagaraj Fig. 3, look ahead parser 310, and [0033]). Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying the combination of Zhang and Thiagarajan to incorporate Nagaraj’s teachings to apply look ahead video analysis on the current frame and the subsequent frame to generate analytics data, wherein the vector is generated based on the analytics data. The motivation for performing such a modification in the combination of Zhang and Thiagarajan is to determine video portions that are referenced frequently using the analysis of the motion vectors. As per claims 3-4 and 15-16, the combination of Zhang, Thiagarajan and Nagaraj further teaches wherein the analytics data/feature vector includes one or more of: a number of generated bits, a proportion of syntax bits, a proportion of intra coded blocks, and a prediction distortion (See Zhang [0024], [0030] and [0038]). As per claim 17, the combination of Zhang, Thiagarajan and Nagaraj further teaches wherein the further feature vector includes: a number of generated bits, a proportion of syntax bits, a proportion of intra coded blocks, and a prediction distortion (See Zhang [0024], [0030] and [0038]) . 07-22-aia AIA 6. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Application Publication no. 2019/0132591) in view of Thiagarajan et al. (US Patent Application Publication no. 2018/0084280) as applied to claim s 1 and 13 above, and further in view of Zhang et al. (US Patent Application Publication no. 2020/0120342) . Regarding claim 5, most of the limitations of this claim have been noted in the above rejection of claim 1. It is noted that the combination of Zhang’ 591 and Thiagarajan is silent about applying look ahead video analysis on downsampled versions of the current frame and the subsequent frame to generate analytics data, wherein the feature vector is generated based on the analytics data. However, Zhang teaches applying look ahead video analysis on downsampled versions of the current frame and the subsequent frame to generate analytics data, wherein the feature vector is generated based on the analytics data (See Zhang’ 342 [0019], [0024] and [0030]). Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying the combination of Zhang’ 591 and Thiagarajan to incorporate Zhang’ 342’s teachings to apply look ahead video analysis on downsampled versions of the current frame and the subsequent frame to generate analytics data, wherein the feature vector is generated based on the analytics data. The motivation for performing such a modification in the combination of Zhang’ 591 and Thiagarajan to convert a video into more than one compressed copy while dividing each picture into evenly divided regions and calculating the total number of macroblocks that use zero motion vectors in each region . 07-22-aia AIA 7. Claim s 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Patent Application Publication no. 2019/0132591) in view of Thiagarajan et al. (US Patent Application Publication no. 2018/0084280) as applied to claim s 1 and 13 above, and further in view of Ribas-Corbera et al. (US Patent no. 6111991) . Regarding claim 6, it is noted that the combination of Zhang and Thiagarajan is silent about wherein determine the estimated group QP for the current frame based on the modeled QP and the further modeled QP comprises averaging the modeled QP and the further modeled QP. However, Ribas-Corbera teaches wherein determine the estimated group QP for the current frame based on the modeled QP and the further modeled QP comprises averaging the modeled QP and the further modeled QP (See col. 2, lines 47-58, col. 8. Lines 27-33). Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying the combination of Zhang and Thiagarajan to incorporate Ribas-Corbera’s teachings to determine the estimated group QP for the current frame based on the modeled QP and the further modeled QP comprises averaging the modeled QP and the further modeled QP. The motivation for performing such a modification in the combination of Zhang and Thiagarajan is determine the optimum quantization values in order to minimize distortion. As per claim 7, it is noted that the combination of Zhang and Thiagarajan is silent about wherein the plurality of available quantization matrices include one or more flat quantization matrices. However, Ribas-Corbera teaches wherein the plurality of available quantization matrices include one or more flat quantization matrices (See Ribas-Corbera col. 5, lines 3-26). Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying the combination of Zhang and Thiagarajan to incorporate Ribas-Corbera’s teachings wherein the plurality of available quantization matrices include one or more flat quantization matrices. The motivation for performing such a modification in the combination of Zhang and Thiagarajan is to improve the image quality of the quantized image . 12-151-08 AIA 07-43 12-51-08 8. Claim s 8-12 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The claims are allowable over the prior art of record since the cited references taken individually or in combination fail to teach or suggest, in addition to the limitations of the independent claim, determining test sets of quantization matrices having different DC and low frequency AC values; encoding one or more test video sequences using the test sets of quantization matrices to produce encoded test video sequences; and determining a set of final quantization matrices to be used as the plurality of available quantization matrices based on perceptual qualities of the encoded test video sequences. 07-96 AIA 9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See the Notice of References Cited (PTO-892). 10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIMS S PHILIPPE whose telephone number is (571)272-7336. The examiner can normally be reached Maxi Flex. 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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. /GIMS S PHILIPPE/Primary Examiner, Art Unit 2424 Application/Control Number: 18/991,939 Page 2 Art Unit: 2424 Application/Control Number: 18/991,939 Page 3 Art Unit: 2424 Application/Control Number: 18/991,939 Page 4 Art Unit: 2424 Application/Control Number: 18/991,939 Page 5 Art Unit: 2424 Application/Control Number: 18/991,939 Page 6 Art Unit: 2424 Application/Control Number: 18/991,939 Page 7 Art Unit: 2424 Application/Control Number: 18/991,939 Page 8 Art Unit: 2424 Application/Control Number: 18/991,939 Page 9 Art Unit: 2424 Application/Control Number: 18/991,939 Page 10 Art Unit: 2424
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