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 .
Status of Claims
2. This is a final action on the merits in response to the reply received 11/11/2025.
Response to Arguments
Applicant’s arguments have been considered but are not persuasive. Applicant argues that Wang fails to indicate a second granularity for applying the machine learning model. The examiner respectfully disagrees. Wang discloses in [0100], a finer granularity grid is employed (say 8×8), whereas for a higher resolution sequence, a coarser granularity grid (say 128×128) is used. The examiner notes that finer nd coarser implies that there is more than one granularity. [0086] discloses The granularity of selecting and signaling the model(s) can be designed at different levels. The possible levels at which the filter model index is signaled include video parameter set/sequence parameter set/picture parameter set (VPS/SPS/PPS) level, intra period level, group of pictures (GOP) level, temporal layer level in the GOP, picture level, slice level, CTU level, or a grid size N*N designed specifically for filter signaling. The selection of the level used for filter model signaling can be fixed. [0126 disclose Filter unit 216 may be configured to perform the various techniques of this disclosure, e.g., to determine one or more of neural network models (NN models) 232 to be used to filter a decoded picture and/or whether to apply NN model filtering. [0092] discloses Video encoder 200 and video decoder 300 may be configured to apply an on/off control for multi-model based filtering as discussed above. Therefore, the granularity is used to apply the machine learning model. Rejection is maintained.
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.
Claim(s) 1-19, 21 are rejected under 35 U.S.C. 102A2 as being anticipated by US 20220103864 A1-Wang et al (Hereinafter referred to as “Wang”)
Regarding claim 1, Wang discloses a method for video processing (Fig. 8-9), comprising:
obtaining a first granularity of selection of a machine learning model for processing a video ([0086], wherein granularity of selecting the modes can be designated at different levels) and a second granularity of applying the machine learning model ([0092], wherein to apply an on/off control for multimodal filtering; [0100], a finer granularity grid is employed (say 8×8), whereas for a higher resolution sequence, a coarser granularity grid (say 128×128) is used. The examiner notes that finer nd coarser implies that there is more than one granularity. [0086] discloses The granularity of selecting and signaling the model(s) can be designed at different levels. The possible levels at which the filter model index is signaled include video parameter set/sequence parameter set/picture parameter set (VPS/SPS/PPS) level, intra period level, group of pictures (GOP) level, temporal layer level in the GOP, picture level, slice level, CTU level, or a grid size N*N designed specifically for filter signaling. The selection of the level used for filter model signaling can be fixed. [0126 disclose Filter unit 216 may be configured to perform the various techniques of this disclosure, e.g., to determine one or more of neural network models (NN models) 232 to be used to filter a decoded picture and/or whether to apply NN model filtering. [0092] discloses Video encoder 200 and video decoder 300 may be configured to apply an on/off control for multi-model-based filtering as discussed above. Therefore, the granularity is used to apply the machine learning model.)); and
performing, based on the first and second granularities, a conversion between a current video block of the video and a bitstream of the video ([0039-0040]; [0061-0062], encoding and decoding))
Regarding claim 2, Wang discloses the method of claim 1, wherein the first granularity is the same as or different from the second granularity; wherein at least one of the first and second granularities is indicated in the bitstream; or wherein at least one of the first and second granularities is derived during processing of the video (The examiner notes that the claim only requires at least one limitation. Thus, Wang discloses the indication of the granularity is signaled in the bitstream in [0083)
Regarding claim 3, Wang discloses the method of claim 1, wherein the second granularity is indicated in the bitstream or derived during processing of the video ([0092], where ApplyFilter is a control operation that may be determined by video encoder 200, which may signal data representative of the decision (e.g., a flag or other syntax element) in the bitstream (applying of filter is interpreted as the second granularity)), and the first granularity is determined to be the same as the second granularity ([0087], wherein the selection of the level used for filter model signaling can be fixed, signaled as an syntax element in bitstreams The model selection is interpreted as the same as the first granularity because they both can be signaled in the bitstream); wherein the first granularity is indicated in the bitstream or derived during processing of the video, and the second granularity is determined to be the same as the first granularity; or wherein the first granularity comprises a third granularity of selecting the machine learning model from a set of machine learning models and a fourth granularity of enabling usage of a machine learning model, and the third granularity is the same as or different from the fourth granularity.
Regarding claim 4, Wang discloses the method of claim 1, wherein first information regarding selecting the machine learning model from a set of machine learning models and/or whether usage of the machine learning model is enabled is indicated in the bitstream in at least one of: a level of a coding tree unit (CTU) ([0087, CTU), or a level of a coding tree block (CTB).
Regarding claim 5, Wang discloses the method of claim 4, wherein the first information for a CTU is coded before the first information for a next CTU, and/or the first information for a CTB is coded before the first information for a next CTB; wherein the first information for a unit corresponding to the second granularity is presented together with one of the CTUs and/or the CTBs covered by the unit; or wherein a scheme to code the first information depends on a relationship between a size of the CTU and/or the CTB and a size of a unit corresponding to the second granularity ([0088], wherein the size of the subset is referred to here as M. M can be any value that is pre-defined or signaled in bitstreams at a lower level (e.g., slice header, picture header, CTU level).
Regarding claim 6, Wang discloses the method of claim 5, wherein a z-scan order is used to code the first information for the CTUs and/or CTBs; or wherein the second granularity is not larger than the CTU and/or the CTB ([0054], wherein raster scan is a z scan).
Regarding claim 7, Wang discloses the method of claim 5, wherein the first information is presented together with the first CTU and/or the first CTB covered by the unit; or wherein the second granularity is larger than the CTU and/or the CTB ([0086], information is presented at CTU level).
Regarding claim 8, Wang discloses the method of claim 5, wherein coding of the first information for the units is performed together if sizes of the units are smaller than a size of the CTU and/or the CTB; or wherein coding of the first information for all units within a CTU or a CTB is performed together if sizes of the units are not greater than a size of the CTU and/or the CTB ([0086], wherein information is encoded or decoded. The examiner notes that then claims are not required due to optional language).
Regarding claim 9, Wang discloses the method of claim 1, wherein first information regarding selecting the machine learning model from a set of machine learning models and/or whether usage of the machine learning model is enabled is indicated in the bitstream independently from coding of the CTU and/or the CTB ([0129], enable or disable).
Regarding claim 10, Wang discloses the method of claim 9, wherein coding of the first information for units each corresponding to the second granularity is performed together; or wherein a raster scan order is used to code the first information for each unit corresponding to the second granularity ([0054], raster scan).
Regarding clam 11, Wang discloses the method of claim 1, wherein first information regarding selecting the machine learning model from a set of machine learning models and/or whether usage of the machine learning model is enabled is indicated in at least one of: a sequence header, a picture header, a slice header, a sequence parameter set (SPS), a picture parameter set (PPS), or an adaptation parameter set (APS), and/or the first information is indicated together with coding tree unit (CTU) syntax ([0086]).
Regarding claim 12, Wang discloses the method of claim 11, wherein all the first information is indicated in at least one of: the sequence header, the picture header, the slice header, the SPS, the PPS, or the APS; wherein a part of the first information is indicated in in at least one of: the sequence header, the picture header, the slice header, the SPS, the PPS, or the APS and another part of the first information is indicated together with the CTU syntax; or wherein all the first information is indicated together with the CTU syntax ([0088], subset is indicated in SPS).
Regarding claim 13, Wang discloses the method of claim 11, wherein if at least one part of the first information is indicated together with the CTU syntax and the first granularity is smaller than a size of the CTU, the at least one part of the first information is indicated in a z-scan order together with the CTU syntax; or wherein the first information is indicated in a raster scan order together with the CTU syntax ([0054], raster scan).
Regarding clam 14, Wang discloses the method of claim 1, wherein second information regarding usage of the machine learning model is indicated in the bitstream at different levels ([0084], wherein hen multiple models are selected, the models are used jointly to filter the target area of the input picture. [0086] discloses that granularities are designed at different levels. Thus, usage is indicated at different levels).
Regarding claim 15, Wang discloses the method of claim 14, wherein whether the second information at a level is indicated depends on a condition; or wherein whether the second information at a first level is indicated depends on the second information at a second level higher than the first level ([0086]).
Regarding claim 16, Wang discloses the method of claim 1, wherein the machine learning model comprises a neural network ([0126]).
Regarding claim 17, Wang discloses the method of claim 1, wherein the conversion includes encoding the current video block into the bitstream; or wherein the conversion includes decoding the current video block from the bitstream ([0039-40]).
Regarding claim 18, analyses are analogous to those presented for claim 1 and are applicable for claim 18, wherein processor and memory (fig. 1)
Regarding claim 19, analyses are analogous to those presented for claim 1 and are applicable for claim 1.
Regarding claim 21, analyses are analogous to those presented for claim 1 and are applicable for claim 21.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LERON BECK whose telephone number is (571)270-1175. The examiner can normally be reached M-F 8 am-5pm.
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, David Czekaj can be reached at (571) 272-7327. 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.
LERON . BECK
Examiner
Art Unit 2487
/LERON BECK/Primary Examiner, Art Unit 2487