CTNF 19/267,437 CTNF 89237 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 2. This Office Action is sent in response to Applicant’s Communications received on July 11, 2025 for application number 19/267,437. This Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration, and Claims. 3. Claims 1-20 are presented for examination. Oath/Declaration 4. An inventor’s oath or declaration has not been received. Information Disclosure Statement 5. The information disclosure statement (IDS) submitted on July 11, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 07-30-03-h AIA Claim Interpretation Nonfunctional Descriptive Material 6 . Claim 20 recites “A non-transitory computer-readable recording medium storing a bitstream of a video which is generated by a method performed by a video processing apparatus”. There are no recitations of a processor or other element-merely bitstream content (bitstream decoded by the recited method). Under MPEP 2111.05(III), this claim is merely machine-readable media. The Examiner finds that there is no disclosed or claimed functional relationship between the stored bitstream and the medium. Instead, the medium is merely a support or carrier for the bitstream being stored. Therefore, the bitstream stored and the way such bitstream is generated should not be given patentable weight. See MPEP 2111.05 applying In re Lowry, 32 F.3d 1579, 1583-84, 32 USPQ2d 1031, 1035 (Fed. Cir. 1994); and In re Ngai, 367 F.3d 1336, 70 USPQ2d 1862 (Fed. Cir. 2004) . As such, claim 20 is subject to a prior art rejection based on any non-transitory computer-readable recording medium known before the earliest effective filing date of the present application. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 7. 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. 07-07-aia AIA 07-07 8. 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 – 07-12-aia AIA (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. 07-15-03-aia AIA 9. Claim s 1-3 and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by LIM et al.(US 2022/0086428 A1) (hereinafter Lim) . Regarding claims 1, 18 and 19 , Lim discloses a method for video processing [ See Lim : at least Figs. 1-2, par. 129, 304-306, 417-423 regarding method for video encoding/decoding apparatus] , an apparatus for processing video data comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor [ See Lim : at least Figs. 1-2, par. 129, 304-306, 417-423, 435 regarding video encoding/decoding apparatus. The embodiments of the present invention may be implemented in a form of program instructions, which are executable by various computer components, and recorded in a computer -readable recording medium…Examples of the program instructions include not only a mechanical language code formatted by a compiler but also a high level language code that may be implemented by a computer using an interpreter. The hardware devices may be configured to be operated by one or more software modules or vice versa to conduct the processes according to the present invention. ] , and a non-transitory computer-readable storage medium storing instructions that cause a processor to perform acts [ See Lim : at least Figs. 1-2, par. 129, 304-306, 417-423, 435 regarding The embodiments of the present invention may be implemented in a form of program instructions, which are executable by various computer components, and recorded in a computer -readable recording medium…] , comprising / cause the processor to / comprising: determining / determine, for a conversion between a current video block of a video and a bitstream of the video, a coding tool for the current video block based on a regression model [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 247-249, 257-267, 272-298, 304-306, 417-421 regarding An image encoding method according to another embodiment of the present invention may comprise determining an intra-prediction mode of a current chroma block, deriving at least one representative value based on a neighboring sample of the current chroma block and a neighboring sample of a corresponding luma block corresponding to the current chroma block in case the intra-prediction mode is a CCLM (Cross-Component Linear Model) mode, deriving a parameter of CCLM by using the at least one representative value, and generating a prediction block of the current chroma block by using the parameter of CCLM … encoding apparatus 100 may generate a bitstream including encoded information through encoding the input image, and output the generated bitstream... CCLM, for example, may be performed by using Equation (1). In an embodiment of this invention, the linear model may mean a linear regression model…] ; and performing / perform the conversion based on the coding tool, wherein the regression model is associated with at least one of: a division-free operation, or a coefficient determination operation [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM mode, parameters a and b of a linear model may be calculated by using a correlation and/or a maximum value and a minimum value between neighboring pixels of a luma block and neighboring pixels of a chroma block. The parameters a and b may mean linear parameters concerning a straight line equation. Then, a prediction block of a chroma block may be generated by using an encoded/decoded pixel value within a luma block and a linear model. CCLM, for example, may be performed by using Equation (1). In an embodiment of this invention, the linear model may mean a linear regression model. Prediction for a current chroma block of CCLM mode may be performed based on Equation (1). In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived. The parameters a and b may be derived by using at least one among N representative values (a first representative value, a second representative value, a third representative value and a fourth representative value) which are derived by various methods of this invention. For example, the parameters a and b may be derived by using Equation (14) and Equation (15). Since Equation (14) includes a division operation, implementation in integer units may be difficult. Accordingly, the division operation of Equation (14) may be replaced by at least one of multiplication and right shift operation. Alternatively, in order to avoid the division operation of Equation (14) , as in Equation (16), the parameter a may be derived by using at least one among Log 2, subtraction and right shift operation… Alternatively, in order to avoid the division operation of Equation (14) , the parameter a may be derived by using at least one among a table with predefined constant values for division, multiplication and right shift operation… encoding apparatus 100 may generate a bitstream including encoded information through encoding the input image, and output the generated bitstream... The decoding apparatus 200 may receive a bitstream output from the encoding apparatus 100. The decoding apparatus 200 may receive a bitstream stored in a computer readable recording medium, or may receive a bitstream that is streamed through a wired/wireless transmission medium. The decoding apparatus 200 may decode the bitstream by using an intra mode or an inter mode. In addition, the decoding apparatus 200 may generate a reconstructed image generated through decoding or a decoded image, and output the reconstructed image or decoded image…] . Regarding claim 2, Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Further on, Lim discloses wherein the regression model comprises at least one of: a linear regression model, a non-linear regression model, or a polynomial regression model [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM mode, parameters a and b of a linear model may be calculated by using a correlation and/or a maximum value and a minimum value between neighboring pixels of a luma block and neighboring pixels of a chroma block. The parameters a and b may mean linear parameters concerning a straight line equation. Then, a prediction block of a chroma block may be generated by using an encoded/decoded pixel value within a luma block and a linear model. CCLM, for example, may be performed by using Equation (1). In an embodiment of this invention, the linear model may mean a linear regression model. Prediction for a current chroma block of CCLM mode may be performed based on Equation (1). In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived…] . Regarding claim 3 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Further on, Lim discloses wherein the coding tool comprises at least one of: a cross-component coding tool building a relationship between luma block samples and chroma block samples, an inter-prediction coding tool building a relationship between the current video block or a current template or current area samples and a reference block or a reference template or reference area samples, an intra-prediction coding tool building a relationship between the current video block or a current template or current area samples and a reference block or a reference template or reference area samples, an inter or intra prediction coding tool building a relationship between a prediction value and a reconstruction value of a reference or a template or neighbor samples of the current video block, a screen content coding (SCC) prediction building a relationship between the current video block or a current template or current area samples and a reference block or a reference template or reference area samples, a coding tool based on template matching, a coding tool based on a template cost, a fusion or blending based coding tool, a cross-component linear model (CCLM) or a variant of CCLM, a multi-model linear model (MMLM) or a variant of MMLM, a convolutional cross-component model (CCCM) or a variant of CCCM, a gradient linear model (GLM) or a variant of GLM, a local illumination compensation (LIC) or a variant of LIC, or an intra template matching prediction (intraTMP) coding tool or a variant of intraTMP [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM mode, parameters a and b of a linear model may be calculated by using a correlation and/or a maximum value and a minimum value between neighboring pixels of a luma block and neighboring pixels of a chroma block. The parameters a and b may mean linear parameters concerning a straight line equation. Then, a prediction block of a chroma block may be generated by using an encoded/decoded pixel value within a luma block and a linear model. CCLM, for example, may be performed by using Equation (1). In an embodiment of this invention, the linear model may mean a linear regression model…]. Regarding claim 17 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Further on, Lim discloses wherein the conversion comprises encoding the current video block into the bitstream, or wherein the conversion comprises decoding the current video block from the bitstream [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding encoding apparatus 100 may generate a bitstream including encoded information through encoding the input image, and output the generated bitstream… The decoding apparatus 200 may receive a bitstream output from the encoding apparatus 100. The decoding apparatus 200 may receive a bitstream stored in a computer readable recording medium, or may receive a bitstream that is streamed through a wired/wireless transmission medium. The decoding apparatus 200 may decode the bitstream by using an intra mode or an inter mode. In addition, the decoding apparatus 200 may generate a reconstructed image generated through decoding or a decoded image, and output the reconstructed image or decoded image…] . Regarding claim 20 , Claim 20 has been interpreted above as nonfunctional descriptive material under MPEP 2111.05(III) and the case law cited therein because claim 20 recites “A non-transitory computer-readable recording medium storing a bitstream of a video which is generated by a method performed by a video processing apparatus”. As such, claim 20 is subject to a prior art rejection based on any non-transitory computer readable medium known before the earliest effective filing date of the present application. In other words, the proper interpretation of claim 20 is merely a machine-readable media in which the media is merely support or carrier for the bitstream being stored wherein the bitstream stored and the way such bitstream is decoded should not be given patentable weight. Lim which is analogous art discloses a non-transitory computer-readable recording medium storing a bitstream stored thereon [ See Lim : at least par. 28-29, 129, 153, 435]. As such, Lim clearly anticipated the non-transitory computer-readable recording medium storing a bitstream . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 10. 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 11. 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 12. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over LIM et al.(US 2022/0086428 A1) (hereinafter Lim) in view of LI et al.(US 2023/0217026 A1) (hereinafter Li) . Regarding claim 4 , Lim discloses all of the limitations of claim 3, and are analyzed with respect to that claim. Lim does not explicitly disclose wherein the fusion or blending based coding tool comprises a blending weight determination of at least one of: a template-based intra mode derivation (TIMD), a decoder side intra mode derivation (DIMD), s combined inter and intra prediction (CIIP), a geometric partitioning mode (GPM), a spatial GPM (SGPM), a multi-hypothesis prediction (MHP), a bi-prediction with coding unit (CU)-level weight (BCW), a chroma fusion, or a luma fusion. However, Li teaches or suggests wherein the fusion or blending based coding tool comprises a blending weight determination of at least one of: a template-based intra mode derivation (TIMD), a decoder side intra mode derivation (DIMD), s combined inter and intra prediction (CIIP), a geometric partitioning mode (GPM), a spatial GPM (SGPM), a multi-hypothesis prediction (MHP), a bi-prediction with coding unit (CU)-level weight (BCW), a chroma fusion, or a luma fusion [ See Li : at least par. 82, 98-117 regarding In the Enhanced Compression Model (ECM), several video compression technologies beyond VVC are explored. Two luma intra prediction modes are proposed, namely, decoder-side intra mode derivation (DIMD) mode and template-based intra mode derivation (TIMD) mode. When DIMD is applied, two intra prediction modes from 65 angular modes are derived from the reconstructed neighbor samples, and those two predictors are combined with the planar mode predictor with the weights derived from the gradients. When TIMD is applied, for each intra prediction mode in a list, the SATD between the predicted and reconstructed samples of a template is calculated. First two intra prediction modes with the minimum SATD are selected and fused with the weights derived from the SATD…Consistent with the disclosed embodiments, the weights of the n chroma intra prediction modes participating in the fusion can be determined according to different methods…]. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim with Li’s teachings by including “ wherein the fusion or blending based coding tool comprises a blending weight determination of at least one of: a template-based intra mode derivation (TIMD), a decoder side intra mode derivation (DIMD), s combined inter and intra prediction (CIIP), a geometric partitioning mode (GPM), a spatial GPM (SGPM), a multi-hypothesis prediction (MHP), a bi-prediction with coding unit (CU)-level weight (BCW), a chroma fusion, or a luma fusion ” because this combination has the benefit of improving the accuracy of prediction and coding efficiency when processing video [ See Li : at least par. 89, 95-96] . 07-21-aia AIA 13. Claim s 5-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over LIM et al.(US 2022/0086428 A1) (hereinafter Lim) in view of JHU et al.(US 2025/0280120 A1) (hereinafter Jhu) . Regarding claim 5 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Lim does not explicitly disclose wherein the regression model is updated based on a slope adjustment parameter determined from the division-free operation, wherein the slope adjustment parameter is applied to update a prediction mode based on the regression model. However, Jhu teaches or suggests wherein the regression model is updated based on a slope adjustment parameter determined from the division-free operation, wherein the slope adjustment parameter is applied to update a prediction mode based on the regression model [ See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’(31) where a′=a+u, and b′=b−u*y r …]. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim with Jhu’s teachings by including “ wherein the regression model is updated based on a slope adjustment parameter determined from the division-free operation, wherein the slope adjustment parameter is applied to update a prediction mode based on the regression model ” because this combination has the benefit of improving coding efficiency [ See Jhu : at least par. 2, 4, 121, 188-194] Regarding claim 6, Lim and Jhu teach all of the limitations of claim 5, and are analyzed as previously discussed with respect to that claim. Further on, Jhu teaches or suggests further comprising: determining the slope adjustment parameter based on an average of a plurality of sample values associated with the current video block, wherein the plurality of sample values comprises at least one of: at least one neighboring luma sample value of the current video block, or at least one neighboring chroma sample value of the current video block, or wherein the plurality of sample values comprises at least one of: at least one neighboring luma sample value of a reference block of the current video block, or at least one neighboring chroma sample value of the reference block [ See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’(31) where a′=a+u, and b′=b−u*y r . With this selection, the mapping function is tilted or rotated around the point with luminance value y r . It is proposed to use the average of the reference luma samples used in the model creation as y r in order to provide a meaningful modification to the model. FIGS. 12A to 12B illustrate the effect of the scale adjustment parameter “u”, wherein FIG. 12A illustrates the model created without the scale adjustment parameter “u”, and FIG. 12B illustrates the model created with the scale adjustment parameter “u”…] . Regarding claim 7, Lim and Jhu teach all of the limitations of claim 5, and are analyzed as previously discussed with respect to that claim. Further on, Jhu teaches or suggests wherein the slope adjustment parameter is determined based on the division- free operation, and the slope adjustment parameter using for at least one of: a cross-component linear model (CCLM) or a multi-model linear model (MMLM) [ See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’ (31) where a′=a+u, and b′=b−u*y r …] . Regarding claim 8, Lim and Jhu teach all of the limitations of claim 5, and are analyzed as previously discussed with respect to that claim. Further on, Lim and Jhu teach wherein the division-free operation is based on a multiplication with a scale factor and a shift operation [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM mode, parameters a and b of a linear model may be calculated by using a correlation and/or a maximum value and a minimum value between neighboring pixels of a luma block and neighboring pixels of a chroma block. The parameters a and b may mean linear parameters concerning a straight line equation. Then, a prediction block of a chroma block may be generated by using an encoded/decoded pixel value within a luma block and a linear model. CCLM, for example, may be performed by using Equation (1). In an embodiment of this invention, the linear model may mean a linear regression model. Prediction for a current chroma block of CCLM mode may be performed based on Equation (1). In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived. The parameters a and b may be derived by using at least one among N representative values (a first representative value, a second representative value, a third representative value and a fourth representative value) which are derived by various methods of this invention. For example, the parameters a and b may be derived by using Equation (14) and Equation (15). Since Equation (14) includes a division operation, implementation in integer units may be difficult. Accordingly, the division operation of Equation (14) may be replaced by at least one of multiplication and right shift operation. Alternatively, in order to avoid the division operation of Equation (14) , as in Equation (16), the parameter a may be derived by using at least one among Log 2, subtraction and right shift operation… Alternatively, in order to avoid the division operation of Equation (14) , the parameter a may be derived by using at least one among a table with predefined constant values for division, multiplication and right shift operation… See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’ (31) where a′=a+u, and b′=b−u*y r …] . Regarding claim 9, Lim and Jhu teach all of the limitations of claim 5, and are analyzed as previously discussed with respect to that claim. Further on, Lim and Jhu teach wherein a division operation is replaced by the division-free operation comprising a multiplication operation with a scale factor and a shift operation [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived. The parameters a and b may be derived by using at least one among N representative values (a first representative value, a second representative value, a third representative value and a fourth representative value) which are derived by various methods of this invention. For example, the parameters a and b may be derived by using Equation (14) and Equation (15). Since Equation (14) includes a division operation, implementation in integer units may be difficult. Accordingly, the division operation of Equation (14) may be replaced by at least one of multiplication and right shift operation. Alternatively, in order to avoid the division operation of Equation (14) , as in Equation (16), the parameter a may be derived by using at least one among Log 2, subtraction and right shift operation… Alternatively, in order to avoid the division operation of Equation (14) , the parameter a may be derived by using at least one among a table with predefined constant values for division, multiplication and right shift operation… See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’ (31) where a′=a+u, and b′=b−u*y r …] . Regarding claim 10, Lim and Jhu teach all of the limitations of claim 9, and are analyzed as previously discussed with respect to that claim. Further on, Lim and Jhu teach wherein at least one of the scale factor or a shift value of the shift operation is determined based on a denominator of the division operation, and/or wherein the shift value is determined based on a log value of the denominator [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived. The parameters a and b may be derived by using at least one among N representative values (a first representative value, a second representative value, a third representative value and a fourth representative value) which are derived by various methods of this invention. For example, the parameters a and b may be derived by using Equation (14) and Equation (15). Since Equation (14) includes a division operation, implementation in integer units may be difficult. Accordingly, the division operation of Equation (14) may be replaced by at least one of multiplication and right shift operation. Alternatively, in order to avoid the division operation of Equation (14) , as in Equation (16), the parameter a may be derived by using at least one among Log 2, subtraction and right shift operation… See Jhu : at least Figs. 12A-12B, par. 188-194 regarding During ECM development, scale ( slope ) adjustment for CCLM are proposed as further improvements, for example, as described in JVET-Y0055/Z0049. As discussed above, CCLM uses a model with 2 parameters to map luma values to chroma values. The scale parameter “a” and the bias parameter “b” define the mapping as follows: chromaVal=a*lumaVal+b (30) It is proposed to signal an adjustment “u” to the scale parameter to update the model to the following form: chromaVal=a’*lumaVal+b’ (31) where a′=a+u, and b′=b−u*y r …] . Regarding claim 11, Lim and Jhu teach all of the limitations of claim 10, and are analyzed as previously discussed with respect to that claim. Further on, Lim teaches or suggests wherein the denominator is normalized to a predetermined range by applying the shift operation, wherein the predetermined range is from 1.0 to 2.0, and/or wherein the scale factor is determined based on a fractional part of the normalized denominator, the factional part being of a predefined precision, wherein the predefined precision comprises a precision corresponding to a predefined number of bits, wherein the predefined number is 14 [ See Lim: at least Figs. 1-17, par. 310-318, 417-423 regarding as shown in (a) of FIG. 9, neighboring samples of a luma block corresponding to a neighboring sample c(0,0) of a chroma block may be y(−1,−1), y(0,−1), y(1,−1), y(−1,0), y(0,0) and y(1,0). A candidate value for deriving a representative luma sample may be obtained by calculating a statistical value of at least one or more samples among the six neighboring samples. Herein, a candidate value for deriving a representative luma sample may mean the statistical value. For example, a representative luma sample value corresponding to a neighboring sample c(0,0) of a chroma block may be calculated like (y(−1,−1)+2*y(0,−1)+y(1,−1)+y(−1,0)+2*y(0,0)+y(1,0)+4)>>3. Here, “>>” means a right shift operator…An example shown in FIG. 9, describes a reference line of a chroma block corresponding to a reference line of a luma block, when a CCLM mode is available in YUV4:2:0 format… For example, the parameters a and b may be derived by using Equation (14) and Equation (15). a =( C MAX− C MIN)/( L MAX− L MIN) Equation (14) ; b=C MIN− a*L MIN Equation (15). A first representative value (LMAX) may be an x-axis value of a maximum value. In addition, a second representative value (CMAX) may be a y-axis value of a maximum value. In addition, a third representative value (LMIN) may be an x-axis value of a minimum value. In addition, a fourth representative value (CMIN) may be a y-axis value of a minimum value. In Equation (15), CMIN and LMIN may be used to derive the parameter b. However, it is not limited to this, and LMAX and CMAX may be used to derive the parameter b. Since Equation (14) includes a division operation, implementation in integer units may be difficult. Accordingly, the division operation of Equation (14) may be replaced by at least one of multiplication and right shift operation. Alternatively, in order to avoid the division operation of Equation (14), as in Equation (16), the parameter a may be derived by using at least one among Log 2, subtraction and right shift operation. a =Log 2 ( C MAX− C MIN)−Log 2 ( L MAX− L MIN)>> k Equation (16) Alternatively, in order to avoid the division operation of Equation (14), the parameter a may be derived by using at least one among a table with predefined constant values for division , multiplication and right shift operation. In Equation (16), since Log 2 is used, the value of a may be normalized by performing a k right shift operation…]. Regarding claim 13, Lim and Jhu teach all of the limitations of claim 8, and are analyzed as previously discussed with respect to that claim. Further on, Lim teaches or suggests wherein the division-free operation is based on an integer look-up table (LUT) [ See Lim : at least Figs. 1-17, par. 8, 18, 28, 129, 151-161, 247-249, 257-267, 272-298, 304-306, 417-423 regarding In order to perform a CCLM, the parameters a and b of a linear model of Equation (1) need to be derived. The parameters a and b may be derived by using at least one among N representative values (a first representative value, a second representative value, a third representative value and a fourth representative value) which are derived by various methods of this invention… Alternatively, in order to avoid the division operation of Equation (14) , the parameter a may be derived by using at least one among a table with predefined constant values for division, multiplication and right shift operation…] . Regarding claim 14 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Lim does not explicitly disclose wherein the regression model comprises an operation: sampVa1=a 0 Y 0 + a 1 Y 1 + a 2 Y 2 + a 3 Y 3 + . . . + a i Y i +a i+1 B, wherein Y 0 , Y 1 , Y 2 , Y 3 , . . . Yi denote values based on reconstruction or prediction samples in a input area, B denotes a bias term, a 0 , a 1 , a 2 , a 3 , . . . a i+1 denote filter coefficients, and sampVa1 denotes a prediction value in an output area, wherein the input area comprises a template or a reference block of the current video block, and the output area comprises a template or the current video block. However, Jhu teaches wherein the regression model comprises an operation: sampVa1=a 0 Y 0 + a 1 Y 1 + a 2 Y 2 + a 3 Y 3 + . . . + a i Y i +a i+1 B, wherein Y 0 , Y 1 , Y 2 , Y 3 , . . . Yi denote values based on reconstruction or prediction samples in a input area, B denotes a bias term, a 0 , a 1 , a 2 , a 3 , . . . a i+1 denote filter coefficients, and sampVa1 denotes a prediction value in an output area, wherein the input area comprises a template or a reference block of the current video block, and the output area comprises a template or the current video block [ See Jhu : at least par. 213-222 regarding The proposed convolutional 7-tap filter consists of a 5-tap plus sign shape spatial component, a non-linear term and a bias term. The input to the spatial 5-tap component of the filter consists of a center (C) luma sample which is collocated with the chroma sample to be predicted and its above/north (N), below/south(S), left/west (W) and right/east (E) neighbors as illustrated in FIG. 17… The bias term B represents a scalar offset between the input and output (similarly to the offset term in CCLM) and is set to middle chroma value (512 for 10-bit content). Output of the filter is calculated as a convolution between the filter coefficients c i and the input values and clipped to the range of valid chroma samples: predChromaVal=c 0 C+c 1 N+c 2 S+c 3 E+c 4 W+c 5 P+c 6 B…]. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim with Jhu’s teachings by including “ wherein the regression model comprises an operation: sampVa1=a 0 Y 0 + a 1 Y 1 + a 2 Y 2 + a 3 Y 3 + . . . + a i Y i +a i+1 B, wherein Y 0 , Y 1 , Y 2 , Y 3 , . . . Yi denote values based on reconstruction or prediction samples in a input area, B denotes a bias term, a 0 , a 1 , a 2 , a 3 , . . . a i+1 denote filter coefficients, and sampVa1 denotes a prediction value in an output area, wherein the input area comprises a template or a reference block of the current video block, and the output area comprises a template or the current video block ” because this combination has the benefit of improving coding efficiency [ See Jhu : at least par. 2, 4, 121, 188-194] . 07-21-aia AIA 14. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over LIM et al.(US 2022/0086428 A1) (hereinafter Lim) in view of JHU et al.(US 2025/0280120 A1) (hereinafter Jhu) in further view of GHAZNAVI YOUVALARI et al.(US 2025/0056017 A1) (hereinafter Ghaznavi Youvalari) . Regarding claim 12, Lim and Jhu teach all of the limitations of claim 8, and are analyzed as previously discussed with respect to that claim. Lim and Jhu do not explicitly disclose wherein the division-free operation is based on a piece-wise polynomial metric, wherein the scale factor of the division-free operation is determined based on an M-piece polynomial model, M being a predefined integer equal to a power of 2, wherein at least one parameter of the polynomial model in a plurality of pieces of the polynomial model is predetermined and stored in a look-up table, wherein a coefficient of power of 1 term is implemented by the shift operation and no storage is for a value of the coefficient. However, performing a polynomial regression model for prediction in video coding systems was well known in the art at the time of the invention was filed as evident from the teaching of Ghaznavi Youvalari [ See Ghaznavi Youvalari : at least Fig. 11, par. 227-241, 243-247 regarding determining (1106) said at least one prediction model as a polynomial and/or exponential prediction model… Thus, the encoder aims to define an optimal prediction model for improving the performance of cross-component intra prediction and/or illumination compensation in inter frames. The encoder may utilize the principles of the CCLM parameter calculation, and use one or more of the following: One or more reconstructed samples from the spatial neighboring blocks in the current channel/frame; One or more reconstructed samples from the spatial neighboring blocks in the reference channel and/or reference frame; One or more reconstructed samples from the co-located block in the reference channel and/or reference frame for training or calculating the parameters of the prediction model. The neighboring samples may be immediate neighboring samples from the block or they may locate in certain distance (horizontally and/or vertically) from the block. The calculation of the parameters may be carried out in different ways, for example using linear or polynomial regression with least square approach or any other method… According to an embodiment, said prediction model is subjected to one or more bit shifting operations. Thus, the prediction model with one or more bit shifting operations..]. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim and Jhu with Ghaznavi Youvalari teachings by including “ wherein the regression model is updated based on a slope adjustment parameter determined from the division-free operation, wherein the slope adjustment parameter is applied to update a prediction mode based on the regression model ” because this combination has the benefit of improving the encoder performance [ See Ghaznavi Youvalari : par. 226, 232] . 07-21-aia AIA 15. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over LIM et al.(US 2022/0086428 A1) (hereinafter Lim) in view of CHEN et al.(US 2023/0011286 A1) (hereinafter Chen) . Regarding claim 15 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Lim does not explicitly disclose wherein the coefficient determination operation comprises: determining at least one coefficient of the regression model based on a regression based mean square error (MSE) minimization, wherein the regression based MSE minimization is based on at least one of: minimizing a metric value between predicted and reconstructed samples in a reference area or a template of the current video block, or minimizing a metric value between reconstructed samples in a reference template of the current video block and reconstructed samples in a current template of the current video block, wherein the metric value comprises at least one of: a mean square error (MSE), a sum of squared error (SSE), a sum of absolute differences (SAD), a sum of absolute transformed differences (SATD), or a difference. However, Chen teaches or suggests wherein the coefficient determination operation comprises: determining at least one coefficient of the regression model based on a regression based mean square error (MSE) minimization, wherein the regression based MSE minimization is based on at least one of: minimizing a metric value between predicted and reconstructed samples in a reference area or a template of the current video block, or minimizing a metric value between reconstructed samples in a reference template of the current video block and reconstructed samples in a current template of the current video block, wherein the metric value comprises at least one of: a mean square error (MSE), a sum of squared error (SSE), a sum of absolute differences (SAD), a sum of absolute transformed differences (SATD), or a difference [ See Chen : at least Figs. 1-13, par. 100-127, 139-140 regarding In RMVF mode, motion of the current block is defined by a 6-parameter motion model. These parameters a xx , a xy , a yx , a yy , b x and b x are calculated by solving a linear regression model in mean square error (MSE) method. The inputs to the regression model consist of the center locations (x, y) and motion vectors (mv x and mv y ) of the available neighboring 4×4 sub-blocks as defined above…In one embodiment, the model parameters a, b, c, d, e and f may be calculated by solving a linear regression model as the RMVF tool above, while the input to the regression model may be a configurable subset of motion information of neighboring blocks from the five conventional regions (bottom-left, left, top-left, top, top-right) as shown in FIG. 10. Note that the subset may be predefined (e.g., only the above and left regions, or only the neighboring blocks located around the current block corners) or signaled in the slice header…]. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim with Chen’s teachings by including “ wherein the coefficient determination operation comprises: determining at least one coefficient of the regression model based on a regression based mean square error (MSE) minimization, wherein the regression based MSE minimization is based on at least one of: minimizing a metric value between predicted and reconstructed samples in a reference area or a template of the current video block, or minimizing a metric value between reconstructed samples in a reference template of the current video block and reconstructed samples in a current template of the current video block, wherein the metric value comprises at least one of: a mean square error (MSE), a sum of squared error (SSE), a sum of absolute differences (SAD), a sum of absolute transformed differences (SATD), or a difference ” because this combination has the benefit of improving the coding efficiency [ See Chen : at least par. 2-8] . 07-21-aia AIA 16. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over LIM et al.(US 2022/0086428 A1) (hereinafter Lim) in view of ASTOLA et al.(US 2024/0064311 A1) (hereinafter Astola) . Regarding claim 16 , Lim discloses all of the limitations of claim 1, and are analyzed with respect to that claim. Lim does not explicitly disclose wherein the regression model with filter coefficients determined based on the coefficient determination operation is applied to determine a prediction sample value in at least one of: the current video block, a template of the current video block, or an area, and/or wherein the coefficient determination operation is based on at least one of: an LDL decomposition or a variant of LDL decomposition, an LU decomposition or a variant of LU decomposition, a Cholesky decomposition or a variant of Cholesky decomposition, a Gaussian elimination or a variant of Gaussian elimination, or a least square tool or a variant of least square tool. However, Astola teaches wherein the regression model with filter coefficients determined based on the coefficient determination operation is applied to determine a prediction sample value in at least one of: the current video block, a template of the current video block, or an area, and/or wherein the coefficient determination operation is based on at least one of: an LDL decomposition or a variant of LDL decomposition, an LU decomposition or a variant of LU decomposition, a Cholesky decomposition or a variant of Cholesky decomposition, a Gaussian elimination or a variant of Gaussian elimination, or a least square tool or a variant of least square tool [ See Astola : par. 28, 62-63, 67-78,99-114 regarding the use of cross-component linear predictive modelling for exploiting correlation in a selected colorspace…3. obtain at least one set of prediction coefficients using a method of linear regression on the samples of the regressor area. The method can be for example ordinary least-squares estimation, orthogonal matching pursuit, optimized orthogonal matching pursuit, etc. The prediction coefficients correspond to the a, b, c, . . . , z terms in the predictive model of Equation 2…The samples of the regressor area are placed in N.sub.s long column vector y. The full system of equations can be expressed as Aw=y+ϵ where w represents a set of coefficients and ϵ represents prediction noise. The coefficient w can be obtained using many types of linear regression tools such as ordinary least-squares estimation, orthogonal matching pursuit, optimized orthogonal matching pursuit, ridge regression , least absolute shrinkage and selection operator (LASSO), etc…] Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Lim with Astola’s teachings by including “ wherein the regression model with filter coefficients determined based on the coefficient determination operation is applied to determine a prediction sample value in at least one of: the current video block, a template of the current video block, or an area, and/or wherein the coefficient determination operation is based on at least one of: an LDL decomposition or a variant of LDL decomposition, an LU decomposition or a variant of LU decomposition, a Cholesky decomposition or a variant of Cholesky decomposition, a Gaussian elimination or a variant of Gaussian elimination, or a least square tool or a variant of least square tool ” because this combination has the benefit of providing an alternate configuration of the regression model determination with filter coefficients. Conclusion 17. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANA J PICON-FELICIANO whose telephone number is (571)272-5252. The examiner can normally be reached Monday-Friday 9:00-5:00. 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, Christopher Kelley can be reached at 571 272 7331. 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. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Ana Picon-Feliciano/ Examiner, Art Unit 2482 /CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482 Application/Control Number: 19/267,437 Page 2 Art Unit: 2482 Application/Control Number: 19/267,437 Page 3 Art Unit: 2482 Application/Control Number: 19/267,437 Page 4 Art Unit: 2482 Application/Control Number: 19/267,437 Page 5 Art Unit: 2482 Application/Control Number: 19/267,437 Page 6 Art Unit: 2482 Application/Control Number: 19/267,437 Page 7 Art Unit: 2482 Application/Control Number: 19/267,437 Page 8 Art Unit: 2482 Application/Control Number: 19/267,437 Page 9 Art Unit: 2482 Application/Control Number: 19/267,437 Page 10 Art Unit: 2482 Application/Control Number: 19/267,437 Page 11 Art Unit: 2482 Application/Control Number: 19/267,437 Page 12 Art Unit: 2482 Application/Control Number: 19/267,437 Page 13 Art Unit: 2482 Application/Control Number: 19/267,437 Page 14 Art Unit: 2482 Application/Control Number: 19/267,437 Page 15 Art Unit: 2482 Application/Control Number: 19/267,437 Page 16 Art Unit: 2482 Application/Control Number: 19/267,437 Page 17 Art Unit: 2482 Application/Control Number: 19/267,437 Page 18 Art Unit: 2482 Application/Control Number: 19/267,437 Page 19 Art Unit: 2482 Application/Control Number: 19/267,437 Page 20 Art Unit: 2482 Application/Control Number: 19/267,437 Page 21 Art Unit: 2482 Application/Control Number: 19/267,437 Page 22 Art Unit: 2482 Application/Control Number: 19/267,437 Page 23 Art Unit: 2482 Application/Control Number: 19/267,437 Page 24 Art Unit: 2482 Application/Control Number: 19/267,437 Page 25 Art Unit: 2482 Application/Control Number: 19/267,437 Page 26 Art Unit: 2482 Application/Control Number: 19/267,437 Page 27 Art Unit: 2482 Application/Control Number: 19/267,437 Page 28 Art Unit: 2482 Application/Control Number: 19/267,437 Page 29 Art Unit: 2482 Application/Control Number: 19/267,437 Page 30 Art Unit: 2482
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