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 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)(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) 1-17, 19-20 and 64 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al., "Deep Contextual Video Compression," arXiv:2109.15047v1, 19 pp. (September 2021). It is noted that a copy of this reference was provided by Applicant on 12/16/2024.
Regarding claim 1, Li discloses:
In a computer system that implements a neural video encoder (abs.; section 1, 3, fig. 1, 2), a method comprising:
receiving a current video frame ("xt is the current frame": section 3, fig. 1, 2);
encoding the current video frame to produce encoded data, wherein the encoding the current video frame comprises:
determining a current latent representation for the current video frame (section 3.1 "Through contextual encoder, xt is encoded into latent codes yt"); and
encoding the current latent representation using an entropy model network (section 1, 3.2, fig. 1, 2, 4) that includes one or more convolutional layers ("Subsequently, many works boost the performance by more advanced entropy models and network structures ... For the network structure, some RNN (recurrent neural network)-based methods [21-23] were proposed in the early development stage, but most of recent methods are based on CNN (convolutional neural network)": section 2), wherein the encoding the current latent representation using the entropy model network comprises:
estimating statistical characteristics of a quantized version of the current latent representation ("pyt ( yt) and qyt ( yt) are estimated and true probability mass functions of quantized latent codes yt,respectively ... So our target is designing an entropy model which can accurately estimate the probability distribution of latent codes pyt ( yt) ": section 3.2) based at least in part on a previous latent representation for a previous video frame ("we propose using the context x-t to generate the temporal prior": section 3.2, fig. 4); and
entropy coding the quantized version of the current latent representation based at least in part on the estimated statistical characteristics (section 3.2); and
outputting the encoded data as part of a bitstream (section 3).
Regarding claim 2, Li discloses;
The method of claim 1, further comprising: quantizing the current latent representation, thereby producing the quantized version of the current latent representation (section 3, fig. 4).
Regarding claim 3, Li discloses:
The method of claim 2, wherein the encoding the current latent presentation using the entropy model network further comprises: determining at least some quantization step ("QS")values for the current latent representation based at least in part on the previous latent representation, wherein the quantizing uses the at least some QS values (section 3.1, 3.2).
Regarding claim 4, Li discloses:
The method of claim 1, wherein the current latent representation is a current latent sample value ("SV") representation for the current video frame, wherein the previous latent representation is a previous latent SV representation for the previous video frame, and wherein the determining the current latent representation comprises determining the currentlatent SV representation using a contextual encoder that includes one or more convolutional layers (section 3, fig. 1 ).
Regarding claim 5, Li discloses:
The method of claim 1, wherein the current latent representation is a current latent motion vector ("MV") representation for the current video frame, wherein the previous latent representation is a previous latent MV representation for the previous video frame, and wherein the determining the current latent representation comprises: using motion estimation to determine MV values for the current video frame relative to the previous video frame; and determining the current latent MV representation from the MV values using a MV contextual encoder that includes one or more convolutional layers (section 3, fig. 1 ).
Regarding claim 6-17, 19-20 and 64, Li discloses the method and system limitations of these claims as shown above with respect to the mapped portions of Li above, including the codec of Li in fig 2 and section 3.1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Mohan et al. (US 2025/0056036) teaches temporal attention-based neural networks; Liu et a; (US 2021/0152831) teaches conditional entropy encoding; Mandt (US 2020/0090069) teaches machine learning based video compression.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M PONTIUS whose telephone number is (571)270-7687. The examiner can normally be reached M-Th 8-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath V Perungavoor can be reached at (571)272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M PONTIUS/Primary Examiner, Art Unit 2488