DETAILED ACTION
Notice of Pre-AIA or AIA Status
This is in response to application no. 19/228,298 filed on 06/04/2025. 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 § 102
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.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 6, 7, 12, 17, 18 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ma et al. (WO2021/164176 A1).
Regarding claim 1, Ma teaches the claim as follows:
A video encoding and decoding processing method, performed by a computer device, comprising: extracting a video frame sequence from a sample video, the video frame sequence including a key frame and an estimated frame (Page 6: First, the video is divided into group of pictures (GOP) for encoding, and the adaptively selected key frames in the group of pictures GOP …for non-key frame encoding… use the estimated motion information to generate the inter-frame prediction result). See FIG. 3 the GOP including key frame (I) and non-key frame (P or B); performing encoding and decoding processing on the key frame via a pre-trained key frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding) of a video encoding and decoding model, to obtain a first encoded frame and a first reconstructed frame (Page 5: Using the deep learning-based end-to-end video compression method…the key frames in the image group are subjected to end-to-end intra-coding to obtain Key frame coding: The key frame coding is reconstructed…to obtain the key frame reconstruction frame); performing encoding and decoding processing on the estimated frame via a pre-trained estimated frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding) of the video encoding and decoding model, to obtain a second encoded frame and a second reconstructed frame (Page 5: the non-key frame in the image group is subjected to end-to-end inter-coding to obtain the non-key frame coding; and finally , The non-key frame encoding is reconstructed…to obtain the non-key frame reconstruction frame); performing model optimization on the video encoding and decoding model based on the first encoded frame, the first reconstructed frame, the second encoded frame, and the second reconstructed frame, to obtain a target video encoding and decoding model (Page 5: the key frames in the image group are subjected to end-to-end intra-coding to obtain Key frame coding: The key frame coding is reconstructed … to obtain the key frame reconstruction frame…the non-key frame in the image group is subjected to end-to-end inter-coding to obtain the non-key frame coding; and finally , The non-key frame encoding is reconstructed…to obtain the non-key frame reconstruction frame…this application can realize an end-to-end global optimization video encoder, and can achieve better encoding performance at a low bit rate); and performing encoding and decoding processing on a target video using the target video encoding and decoding model (Note that in Ma the globally optimized video encoder is used for performing video encoding and decoding). Ma states that by establishing a global optimization model of reconstructed video and original video, and using neural networks to model the rate-distortion optimization problem with high-dimensional complex solution space, the video can be realized. Pages 5-6.
Regarding claim 6, Ma teaches the method according to claim 1, wherein performing encoding and decoding processing on the key frame includes: performing encoding processing on the key frame using an encoder in the pre-trained key frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding), to obtain the first encoded frame (Page 5: Using the deep learning-based end-to-end video compression method…the key frames in the image group are subjected to end-to-end intra-coding to obtain Key frame coding…); and performing decoding processing on the first encoded frame using a decoder in the pre- trained key frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding), to obtain the first reconstructed frame (page 5: The key frame coding is reconstructed…to obtain the key frame reconstruction frame).
Regarding claim 7, Ma teaches the method according to claim 1, wherein performing encoding and decoding processing on the estimated frame includes: performing encoding processing on the estimated frame using an encoder in the pre- trained estimated frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding), to obtain the second encoded frame (page 5: the non-key frame in the image group is subjected to end-to-end inter-coding to obtain the non-key frame coding); and performing decoding processing on the second encoded frame using a decoder in the pre- trained estimated frame network, to obtain the second reconstructed frame (page 5: The non-key frame encoding is reconstructed…to obtain the non-key frame reconstruction frame…).
Regarding claim 12, Ma teaches the claim as follows:
A computer device comprising: a processor; and a memory storing computer-readable instructions that, when executed by the processor, cause the computer device to (Page 12: a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes…the combination of processes and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor …): extract a video frame sequence from a sample video, the video frame sequence including a key frame and an estimated frame (Page 6: First, the video is divided into group of pictures (GOP) for encoding, and the adaptively selected key frames in the group of pictures GOP…for non-key frame encoding… use the estimated motion information to generate the inter-frame prediction result). See FIG. 3 the GOP including key frame (I) and non-key frame (P or B); perform encoding and decoding processing on the key frame via a pre-trained key frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding) of a video encoding and decoding model, to obtain a first encoded frame and a first reconstructed frame (Page 5: Using the deep learning-based end-to-end video compression method…the key frames in the image group are subjected to end-to-end intra-coding to obtain Key frame coding: The key frame coding is reconstructed…to obtain the key frame reconstruction frame); perform encoding and decoding processing on the estimated frame via a pre- trained estimated frame network (page 2: a video compression method and system that uses a deep neural network to achieve end-to-end video encoding) of the video encoding and decoding model, to obtain a second encoded frame and a second reconstructed frame (Page 5: the non-key frame in the image group is subjected to end-to-end inter-coding to obtain the non-key frame coding; and finally , The non-key frame encoding is reconstructed…to obtain the non-key frame reconstruction frame); perform model optimization on the video encoding and decoding model based on the first encoded frame, the first reconstructed frame, the second encoded frame, and the second reconstructed frame, to obtain a target video encoding and decoding model (Page 5: the key frames in the image group are subjected to end-to-end intra-coding to obtain Key frame coding: The key frame coding is reconstructed … to obtain the key frame reconstruction frame…the non-key frame in the image group is subjected to end-to-end inter-coding to obtain the non-key frame coding; and finally , The non-key frame encoding is reconstructed…to obtain the non-key frame reconstruction frame…this application can realize an end-to-end global optimization video encoder, and can achieve better encoding performance at a low bit rate); and perform encoding and decoding processing on a target video using the target video encoding and decoding model (Note that in Ma the globally optimized video encoder is used for performing video encoding and decoding). Ma states that by establishing a global optimization model of reconstructed video and original video, and using neural networks to model the rate-distortion optimization problem with high-dimensional complex solution space, the video can be realized. Pages 5-6.
Regarding claim 17, the claim is drawn to a computer device claim and recites the limitation analogous to claim 6, and is rejected due to the same reason set forth above with respect to claim 6.
Regarding claim 18, the claim is drawn to a computer device claim and recites the limitation analogous to claim 7, and is rejected due to the same reason set forth above with respect to claim 7.
Regarding claim 20, the claim is drawn to a non-transitory storage medium claim and recites the limitation analogous to claim 12, and is rejected due to the same reason set forth above with respect to claim 12.
Allowable Subject Matter
Claims 2-5, 8-11, 13-16 and 19 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 following is the prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 20180139458 A1) describes methods and systems for optimising the quality of visual data.¶0002
Van Rozendaal et al. (US 20240305785 A1) describes end-to-end deep learning of a neural video coder. ¶0094
Conclusion
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/NATHNAEL AYNALEM/Primary Examiner, Art Unit 2488
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