Prosecution Insights
Last updated: April 19, 2026
Application No. 18/994,855

Method and Apparatus of Improving Performance of Convolutional Cross-Component Model in Video Coding System

Non-Final OA §102§103§112
Filed
Jan 15, 2025
Examiner
BOYLAN, JAMES T
Art Unit
2486
Tech Center
2400 — Computer Networks
Assignee
MediaTek Inc.
OA Round
1 (Non-Final)
63%
Grant Probability
Moderate
1-2
OA Rounds
2y 9m
To Grant
74%
With Interview

Examiner Intelligence

Grants 63% of resolved cases
63%
Career Allow Rate
305 granted / 487 resolved
+4.6% vs TC avg
Moderate +12% lift
Without
With
+11.8%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
34 currently pending
Career history
521
Total Applications
across all art units

Statute-Specific Performance

§101
1.8%
-38.2% vs TC avg
§103
50.3%
+10.3% vs TC avg
§102
13.0%
-27.0% vs TC avg
§112
23.7%
-16.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 487 resolved cases

Office Action

§102 §103 §112
DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claim Objections Claims 1 and 9-10 are objected to because of the following informalities: Please correct the spelling of “colour”. Appropriate correction is required. Claim 5 is objected to because of the following informalities: Please correct the spelling of “signalled”. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 10-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claim 10, states “determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied (performing CCCM prediction).”. However, it is unclear how this enabling condition is determined. The specification does not define the block size. How is the enabling condition being satisfied? Claim 13 is also vague and unclear. Claim 13 is performing a logarithmic combination of block width, block height and block area. However, block area is equivalent to block width and height. It is unclear why two identical terms are being included in a logarithmic combination. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1-3 and 9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Astola (US 20250373846). Regarding claim 1, Astola discloses a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Astola [0191] CCCM. Also, see Fig. 1, Codec (54).] receiving input data associated with a current block comprising a luma block and a chroma block, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side, and wherein the chroma block has a lower resolution than the luma block; [See Astola [0191] CCCM. Also, see Fig. 4a-4b for encoding/decoding. Also, see 0002, chroma has lower resolution than luminance.] generating a down-sampled luma block sample by applying a target down-sampling kernel to the luma block, wherein the target down-sampling kernel is selected from a filter set comprising multiple down-sampling kernels; [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.] determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor comprises a term generated by applying a convolutional filter to a location of target down-sampled luma sample; [See Astola [0191-0200] CCCM (applicants background/related art further specifies that CCCM generates terms (Pg. 14 of the original specification)).] generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and [See Astola [0191-0200] CCCM. Also, see 0241, Multi-model CCCM.] encoding or decoding the target chroma sample using the final predictor. [See Astola [0191] CCCM. Also, see Fig. 4a/4b for encoding/decoding process using prediction.] Regarding claim 2, Astola discloses the method of claim 1. Furthermore, Astola discloses wherein the multiple down-sampling kernels correspond to different filter coefficient sets. [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape. Also, see 0008, determining filter coefficients based on a shape of the filter.] Regarding claim 3, Astola discloses the method of claim 1. Furthermore, Astola discloses wherein the multiple down-sampling kernels correspond to different filter shapes. [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.] Regarding claim 9, see examiners rejection for claim 1 which is analogous and applicable for the rejection of claim 9. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Laroche et al. (herein after will be referred to as Laroche) (US 20200389650). Regarding claim 4, Astola discloses the method of claim 1. Furthermore, Astola does not explicitly disclose wherein the multiple down-sampling kernels are associated with multiple cross-component prediction modes. However, Laroche does disclose wherein the multiple down-sampling kernels are associated with multiple cross-component prediction modes. [See Laroche [0144] MMLM modes differ from each other by five different down-sampling filters.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola to add the teachings of Laroche, in order to reduce the complexity of the derivation of the model parameters for the computation of chroma predictor [See Laroche [0020]]. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Laroche (US 20200389650) and in further view of Choi et al. (herein after will be referred to as Choi) (US 20250267285). Regarding claim 5, Astola (modified by Laroche) disclose the method of claim 4. Furthermore, Astola does not explicitly disclose wherein a best mode from the multiple cross-component prediction modes is signalled or parsed. However, Choi does disclose wherein a best mode from the multiple cross-component prediction modes is signalled or parsed. [See Choi [0223] Multi-model CCCM. Also, see 0163, Select an optimal mode and signal that mode.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Laroche) to add the teachings of Choi, in order to reduce the burden at the decoder-side by incorporating coding parameters determined at an encoder side. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Laroche (US 20200389650) and in further view of Wang et al. (herein after will be referred to as Wang) (US 20240187576). Regarding claim 6, Astola (modified by Laroche) disclose the method of claim 4. Furthermore, Astola does not explicitly disclose wherein a best mode from the multiple cross-component prediction modes is determined implicitly by comparing matching costs associated with the multiple cross-component prediction modes measured using one or more reference areas of the current block. However, Wang does disclose wherein a best mode from the multiple cross-component prediction modes is determined implicitly by comparing matching costs associated with the multiple cross-component prediction modes measured using one or more reference areas of the current block. [See Wang [0255] Derive the CCIP mode at the decoder using the reconstructed samples of neighboring blocks. Also, see 0260, all CCIP modes are listed in terms of a template cost and the mode with the minimum cost is selected as the derived mode. Also, see 0268, MMLM is derived implicitly.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Laroche) to add the teachings of Wang, in order to reduce bandwidth by implicitly determining coding parameters at the decoder side. Claims 7-8 rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao et al. (herein after will be referred to as Zhao) (US 20240015279). Regarding claim 7, Astola discloses the method of claim 1. Furthermore, Astola does not explicitly disclose wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples. However, Zhao does disclose wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples. [See Zhao [0234] Multiple prediction blocks of a current block are generated by multiple cross-component modes and a final prediction block is determined based on a weighted average.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola to add the teachings of Zhao, in order to improve upon chroma prediction by taking a weighted average of multiple modes. Regarding claim 8, Astola (modified by Zhao) disclose the method of claim 7. Furthermore, Astola does not explicitly disclose wherein the different down-sampled luma blocks samples are generated by different target down-sampling filters from the filter set. However, Zhao does disclose wherein the different down-sampled luma blocks samples are generated by different target down-sampling filters from the filter set. [See Zhao [0233] Different filters are applied for multiple groups of samples in a coding block.] Applying the same motivation as applied in claim 7. Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Jhu et al. (herein after will be referred to as Jhu) (US 20250126284). Regarding claim 10, Astola discloses a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Astola [0191] CCCM. Also, see Fig. 1, Codec (54).] receiving input data associated with a current block comprising a luma block and a chroma block, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side, and wherein the chroma block has a lower resolution than the luma block; [See Astola [0191] CCCM. Also, see 0002, chroma has lower resolution than luminance.] generating a down-sampled luma block sample by applying a target down-sampling kernel to the luma block; [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.] determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor comprises a term generated by applying a convolutional filter to a location of target down-sampled luma sample; [See Astola [0191] CCCM.] generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and[See Astola [0191] CCCM.] encoding or decoding the target chroma sample using the final predictor. [See Astola [0191] CCCM.] Astola does not explicitly disclose determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied: However, Jhu does disclose determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied: [See Jhu [0441] CCCM is only used for samples larger than or equal to a predefined number, such as the first value, for single model. For another example, FLM/GLM/ELM/CCCM is only used for samples larger than a predefined number, such as the second value, for multi model.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola to add the teachings of Jhu, in order to improve upon coding efficiency [See Jhu [0002]]. Regarding claim 11, Astola (modified by Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose wherein the current block size corresponds to current block width, current block height, or both. However, Jhu does disclose wherein the current block size corresponds to current block width, current block height, or both. [See Jhu [0362] Size restriction according to the CU area/width/height/depth.] Applying the same motivation as applied in claim 10. Regarding claim 12, Astola (modified by Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose wherein the current block size corresponds to current block area. However, Jhu does disclose wherein the current block size corresponds to current block area. [See Jhu [0362] Size restriction according to the CU area/width/height/depth.] Applying the same motivation as applied in claim 10. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Jhu (US 20250126284) and in further view of Lim et al. (herein after will be referred to as Lim) (US 20220109846). Regarding claim 13, Astola (modified by Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose wherein the enabling condition is derived based on a logarithmic combination of current block width, current block height and current block area. However, Lim does disclose wherein the enabling condition is derived based on a logarithmic combination of current block width, current block height and current block area. [See Lim [0564-0565] Threshold value is defined by log 2 (Area), wherein area is calculated by multiplying width and height of the block. CCLM is performed/not performed based on the threshold value.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Jhu) to add the teachings of Lim, in order to utilize analogous teachings from cross-component prediction modes such as CCLM and apply it to a newer cross-component prediction mode such as CCCM. Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Jhu (US 20250126284) and in further view of Choi (herein after will be referred to as Choi ‘982) (US Patent No. 12,113,982). Regarding claim 14, Astola (modified by Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose wherein if an above line of the current block is across a CTU (Coding Tree Unit) row boundary, the enabling condition is not satisfied. However, Choi ‘982 does disclose wherein if an above line of the current block is across a CTU (Coding Tree Unit) row boundary, the enabling condition is not satisfied. [See Choi ‘982 [Equation 2 and 5, and Col. 14 lines 46-55] Downsampling is performed uses 6 reference samples and downsampling is performed using 3 reference samples when the CU crosses a CTU boundary.] It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Jhu) to add the teachings of Choi ‘982, in order to improve upon coding efficiency [See Choi ‘982 [Col. 1 lines 42-43]]. Regarding claim 15, Astola (modified by Jhu and Choi ‘982) disclose the method of claim 14. Furthermore, Astola does not explicitly disclose wherein if the enabling condition is not satisfied, a shorter-tap convolutional filter is applied to generate the convolutional cross-component model predictor. However, Choi ‘982 does disclose wherein if the enabling condition is not satisfied, a shorter-tap convolutional filter is applied to generate the convolutional cross-component model predictor. [See Choi ‘982 [Equation 2 and 5, and Col. 14 lines 46-55, Downsampling is performed uses 6 reference samples and downsampling is performed using 3 reference samples when the CU crosses a CTU boundary.] Applying the same motivation as applied in claim 14. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T BOYLAN whose telephone number is (571)272-8242. The examiner can normally be reached Monday-Friday 7am-3pm. 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, JAMIE ATALA can be reached at 571-272-7384. 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. /JAMES T BOYLAN/Examiner, Art Unit 2486
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Prosecution Timeline

Jan 15, 2025
Application Filed
Feb 26, 2026
Non-Final Rejection — §102, §103, §112 (current)

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Prosecution Projections

1-2
Expected OA Rounds
63%
Grant Probability
74%
With Interview (+11.8%)
2y 9m
Median Time to Grant
Low
PTA Risk
Based on 487 resolved cases by this examiner. Grant probability derived from career allow rate.

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