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 action is responsive to the following communication: Amendment filed Apr. 21, 2026. This Action is made Final.
Claims 1-20 are pending in the case. Claims 1 and 11 are independent claims.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-8 and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Besenbruch et al. (hereinafter Besenbruch) WO 2021/220008 in view of Phan et al. (hereinafter Phan) 2020/0293876.
With respect to independent claim 1, Besenbruch teaches a method for image coding, comprising:
performing, based on one or more input images, an online training of a neural image compression (NIC) framework, the NIC framework being an end-to-end framework (see e.g., Page 8-9 – “the first computer system is a server, e.g. a dedicated server, e.g a machine in the cloud with dedicated GPUs e.g Amazon Web Services, Microsoft Azure, etc, or any other cloud computing services … the recipient device is a laptop computer, desktop computer, a tablet computer, a smart TV or a smart phone.” ) that comprises both (i) one or more first neural networks in an encoding portion and (ii) one or more second neural networks in a decoding portion, the online training determining a plurality of updated values to one or more tunable parameters in the one or more first neural networks, wherein the one or more second neural networks have non-tunable parameters (see e.g., Page 24 – “The method may be one wherein encoding the input image using the first trained neural network includes using one or more univariate or multivariate Padé activation units … The method may be one wherein using the second trained neural network to produce an output image from the quantized latent includes using one or more univariate or multivariate Padé activation units.” Page 94 - “Therefore, we incorporate more flexibility in entropy modelling by using parametric distributions as factorised prior. We achieve this by employing distributions with many degrees of freedom in the parametrisation, including shape, asymmetry and skewness. Note that the innovation is formulated irrespective of the method with which the parameters f are produced; these may be learned directly as fixed parameters (fully factorised prior), predicted by a hypernetwork (hyperprior) or by a context model (conditional model).” Page 132 - “The extent of the penalty can be adjusted with the cr parameter, which becomes a tunable hyperparameter.”);
updating the one or more tunable parameters in the one or more first neural networks according to the plurality of updated values (see e.g., Page 25, 132 – “The method may be one wherein when back-propagating the gradient of the loss function through the second neural network and through the first neural network, parameters of the one or more univariate or multivariate Pade activation units of the first neural network are updated, and parameters of the one or more univariate or multivariate Pade activation units of the second neural network are updated.”); and
encoding, by the encoding portion of the NIC framework with the one or more tunable parameters in the one or more first neural networks being updated, the one or more input images into a bitstream (see e.g. Page 3 – “entropy encoding the quantized latent into a bitstream”).
Besenbruch does not expressly show the one or more first neural networks in the encoding portion and the one or more second neural networks in the decoding portion being trained by an offline training process based on a set of training images, the online training determining a plurality of updated values to one or more tunable parameters in the one or more first neural networks based on the one or more input images that are target images for encoding. However, Phan expressly teaches that neural network can be trained offline (see e.g., Para [4][23][18]-[24] – “neural network compression program 132 receives neural network 112 from server 110. In other embodiments, neural network compression program 132 receives a neural network from another server or computing device (not shown). In an embodiment, neural network 112 is a pre-trained DNN. In several embodiments, neural network 112 may be a pre-trained RNN, a pre-trained FNN, or a pre-trained CNN … In an embodiment, neural network compression program 132 uses the same set of training data for compressing neural network 112. In other embodiments, neural network compression program 132 uses a different set of training data for compressing neural network 112 than was used during pre-training of neural network 112.”) Both Besenbruch and Phan are directed to using neural network program for compression (see e.g., Phan Para [18]-[22] – “Neural network compression program 132 operates as a program for compressing a neural network using an optimization model. “). Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Besenbruch and Phan in front of them to modify the system of Besenbruch to include the above feature. The motivation to combine Besenbruch and Phan comes from Phan. Phan discloses the motivation to incorporate offline training so that the efficiency can be improved (see e.g. Phan Para [18]-[24]). The motivation to combine applies to the dependent claims.
With respect to dependent claim 2, the modified Besenbruch teaches the non-tunable parameters of the one or more second neural networks are fixed at pretrained values that are obtained from the offline training of the NIC framework (see e.g. Page 123 – “We propose to use any combination and number of losses here, for examples, one possible combination is to DMOS using deep pre-trained features whose weights are learnt using linear regression along with PSNR.” See Phan Para [18]-[24]).
With respect to dependent claim 3, the modified Besenbruch teaches the NIC framework comprises a specific neural network in both of the encoding portion and the decoding portion, and the specific neural network comprises first parameters that are fixed during the online training (see e.g. Abstract and Page 10, 123).
With respect to dependent claim 4, the modified Besenbruch teaches the specific neural network comprises a hyper decoder network (see e.g. Page 59 – “”).
With respect to dependent claim 5, the modified Besenbruch teaches the performing the online training of the NIC framework further comprises: performing the online training with each of parameters in a main encoder network and a hyper encoder network of the NIC framework being tunable (see e.g. Page 133 – “' /QN can be initialised at beginning of network training of the original autoencoder, but optimised separately in a two-step training process. After a full forward and backward propagation, firstly the parameters for the autoencoder are updated with the first set of optimisation configurations.”).
With respect to dependent claim 6, the modified Besenbruch teaches the performing the online training of the NIC framework further comprises: performing the online training with a subset of parameters in a main encoder network and a hyper encoder network of the NIC framework being tunable (see e.g. Page 198 – “When only a small portion of latents are needed each iteration, the entire finetuning process can be parallelized. That is, on each iteration a “batch” of many small subsets of the latent vector are processed in parallel.”).
With respect to dependent claim 7, the modified Besenbruch teaches the performing the online training of the NIC framework further comprises: performing the online training with parameters of a layer in a main encoder network or a hyper encoder network of the NIC framework being tunable (see e.g. Page 133 – “Encoder or Decoder layers of a compression network train on rate on the rate distortion loss objective. Essentially we are using the layers of a trained compression network rather then one trained on classification.”).
With respect to dependent claim 8, the modified Besenbruch teaches the performing the online training of the NIC framework further comprises: performing the online training with parameters of a channel in a layer in a main encoder network or a hyper encoder network of the NIC framework being tunable (see e.g. Page 68, 121. 133 – “Figure 111 shows an example of a channel- wise fully connected convolutional network. Network layers (convolutional operations) proceed from top to bottom in the diagram. The output of each layer depends on all previous channels.”).
Claim 11 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 13 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 14 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 8.
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Besenbruch in view of Phan and further in view of Kulkarni (hereinafter Kulkarni) U.S. Patent Publication No. 2013/0293738.
With respect to claim 9, Besenbruch does not expressly show the performing the online training of the NIC framework further comprises: splitting an input image in the one or more input images into a plurality of blocks; assigning respective step sizes to the plurality of blocks; and performing the online training of the NIC framework according to the plurality of blocks with the respective step sizes. However, Kulkarni teaches similar feature (see e.g. Para [19]-[24] – “The fixed-rate bitstream may be a fixed-rate bitstream at the granularity of image blocks. Each image block may be encoded into a portion of the bitstream that has a common size (e.g., a common number of available bits) and that is output at a common rate. However. within each image block, available bits may be allocated differently from the way in which available bits are allocated in other image blocks. The available bits may be allocated based on the content of the image data in that image block.”) Both Besenbruch and Kulkarni are directed to image compression methods. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Besenbruch and Kulkarni in front of them to further modify the modified system of Besenbruch to include the above feature. The motivation to combine Besenbruch and Kulkarni comes from Kulkarni. Kulkarni discloses the motivation to process image in blocks so that bits can be allocated differently based on blocks so that the efficiency can be improved (see e.g. Kulkarni Para [19]-[24]).
With respect to claim 10, the modified Besenbruch teaches the performing the online training of the NIC framework further comprises: assigning a step size to an input image in the one or more input images based on a type of content in the input image; and performing the online training of the NIC framework according to the input image with the step size (see e.g. Kulkarni Para [19]-[24]).
Claim 19 is rejected for the similar reasons discussed above with respect to claim 9.
Claim 20 is rejected for the similar reasons discussed above with respect to claim 10.
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 PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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