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 .
DETAILED ACTION
This Office Action is in response to the application 19/085,140 filed on 07/30/2026.
In the instant Amendment, claims 1, 2, 6, 7, 11, 12, 16 and 17 have been amended.
Claims 1 – 20 have been examined and are pending in this application. This action is made Final.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/21/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1 – 20 have been considered but are moot because the arguments do not apply to the same combination of references being used in the current rejection. Applicant’s arguments are directed solely to the claimed invention as amended 07/30/2026, which has been rejected under new ground of rejection necessitated by amendment. See rejection below for full detail.
Claim Rejections - 35 USC § 103
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 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 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 1 – 4, 6 – 9, 11 – 14 and 16 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over Aytekin et al. (US 2020/0311551 A1) in view of Cricri et al. (US 2024/0202507 A1).
Regarding claim 1, Aytekin discloses: an apparatus [see abstract: A method, apparatus] comprising:
at least one processor [see para: 0014; at least cause one or more processors to provide a pre-trained neural network]; and
at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform [see para: 0018; In some embodiments, the computer-executable program code instructions stored on the non-transitory computer-readable storage medium can be configured to at least determine when the neural network has been temporarily prefitted on the predetermined number of images and to discontinue the above described steps thereafter]:
determining an updated neural network(NN) filter based at least on a base or pretrained [[)]] filter and on an update [see para: 0018; In some embodiments, after discontinuing the iterative temporary overfitting of the neural network on individual images or other data from the training database, a final updated neural network is formed. In some embodiments, the computer-executable program code instructions stored on the non-transitory computer-readable storage medium can be configured to at least use the final updated neural network as a pretrained neural network. In some embodiments, the pretrained neural network is usable as a starting point when overfitting the neural network to a small set of data to be encoded]; and
Aytekin does not explicitly disclose: “quantizing the updated NN filter by using one or more quantization parameters (QPs), wherein the one or more QPs comprise at least one predetermined QP that is predetermined based on one or more other NN filters that are updated during an offline phase”.
However, Cricri, from the same or similar field of endeavor teaches: “quantizing the updated NN filter by using one or more quantization parameters (QPs) [see para: 0329; In other example, the pixel values may be predicted by using spatial prediction techniques. This prediction technique uses the pixel values around the block to be coded in a specified manner. Secondly, the prediction error, for example, the difference between the predicted block of pixels and the original block of pixels is coded. This is typically done by transforming the difference in pixel values using a specified transform, for example, discrete cosine transform (DCT) or a variant of it; quantizing the coefficients; and entropy coding the quantized coefficients. By varying the fidelity of the quantization process, encoder can control the balance between the accuracy of the pixel representation, for example, picture quality and size of the resulting coded video representation, for example, file size or transmission bitrate], wherein the one or more QPs comprise at least one predetermined QP that is predetermined [see para: 0532; The encoder may encode the topology, weights, and/or weight-update of the NN into the bitstream, or may specify a URI from which the topology, weights, weight-update of the NN may be obtained. In case a weight-update is signaled (either by encoding it into the bitstream, or by including a URI), the encoder-side may also signal an indication of which base NN to update. This indication may be a high-level syntax element, such as ‘base_nn_id’, which may take one out of a set of possible predetermined values. For example, the indicated base NN may be a NN which was pretrained on a training dataset. Signaling One or More NN, as Described in the Example 3, NN from Encoder to Decoder] based on one or more other NN filters that are updated during an offline phase [see para: 0501; In this example, a NN may be used for any test video. The NN may be pretrained on a training dataset, during an offline pretraining phase. The training dataset may not include the video data used at the test stage. No finetuning of the NN on a specific video or frame is performed. The base NN may be a randomly initialized NN]
It would have been obvious to the person of ordinary skill in the art before the effective filing date of the claimed invention to modify the pre-trained neural network with the weight-updates being compressible using at least a weight-update compression loss function disclosed by Aytekin to add the teachings of Cricri as above, in order to quantize an updated NN filter using QPs that are derived from the base/pretrained filter, subset of the QPs is predetermined offline and made available at the decoder and reducing the need to signal everything dynamically [Cricri see para: 0329; 0532; 0501].
Regarding claim 2, Aytekin and Cricri disclose all the limitation of claim 1 and are analyzed as previously discussed with respect to that claim.
Furthermore, Aytekin discloses: “
wherein the at least one predetermined QP is available at the apparatus [see para: 0084; The quantization is usually controlled by a predetermined Quantization Parameter (QP)].
Regarding claim 3, Aytekin and Cricri disclose all the limitation of claim 2 and are analyzed as previously discussed with respect to that claim.
Furthermore, Aytekin discloses: “wherein the at least one predetermined QP is same or substantially same as a QP used to quantize the base or the pretrained NN filter [see para: 0093; an apparatus (e.g., the apparatus 100) can be used to first pre-train a neural network, on a training dataset. This pretrained neural network can be present at both the encoder side (e.g., in the decoding loop, or in post-processing stage) and the decoder side. The pre-training may or may not take into account the compressibility of the neural network, e.g., in one possible implementation the pretrained network itself may be pre-trained by using at least a task loss and a weight-compression loss, in another implementation the pretrained network may be pretrained and then compressed for example by pruning small-valued weights, in yet another implementation there may be no compression happening to the pretrained network].
Regarding claim 4, Aytekin and Cricri disclose all the limitation of claim 1 and are analyzed as previously discussed with respect to that claim.
Furthermore, Aytekin discloses: “wherein the apparatus is caused to perform:
receiving, in or along a bitstream, at least one QP of the one or more QPs from an encoder [see para: 0084; Importantly, the intra- and inter-prediction are processes which are done at the decoder, therefore they need to be done also within the encoder and more specifically in the “decoding loop” of the encoder, in order to compute the residual. The residual is computed as: residual=original−prediction. Then, the residual is quantized and encoded. The decoded residual will have some quantization error and is denoted as q_residual. The reconstructed data is as follows: reconstruction=prediction+q_residual. Since there may be a loss of information due to the quantization of the residual, the reconstruction may be different from the original data and results in lower quality. The quantization is usually controlled by a predetermined Quantization Parameter (QP). The higher the QP, the coarser quantization is performed and thus the lower quality the reconstruction is].
Regarding claim 6, 11 and 16, claim 6, 11 and 16 is rejected under the same art and evidentiary limitations as determined for the method of claim 1.
Regarding claim 7, 12 and 17, claim 7, 12 and 17 is rejected under the same art and evidentiary limitations as determined for the method of claim 2.
Regarding claim 8, 13 and 18, claim 8, 13 and 18 is rejected under the same art and evidentiary limitations as determined for the method of claim 3.
Regarding claim 9, 14 and 19, claim 9, 14 and 19 is rejected under the same art and evidentiary limitations as determined for the method of claim 4.
Allowable Subject Matter
Claims 5, 10, 15 and 20 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi et al (US 2018/0107926 A1).
THIS ACTION IS MADE FINAL. 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 Masum Billah whose telephone number is (571)270-0701. The examiner can normally be reached Mon - Friday 9 - 5 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie J. 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.
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/MASUM BILLAH/Primary Patent Examiner, Art Unit 2486
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