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 .
Notice to Applicant
This office action is in response to amendment filed on 09/16/2026.
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-2,5-12 and 15-20 are pending.
Response to Amendments
The Amendment filled 09/16/2026 in response to the Non-Final Office Action mailed 06/16/2026 has been entered. Claims 1, 11, and 20 have been amended. Claims 3-4 and 13-14 have been canceled. The previous rejections under 35 USC §103 are withdrawn.
Response to Amendments
Applicant’s arguments with respect to claims 1-20 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 § 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.
(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.
Claims 1-2, 8-9, 11-12, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Torfason” (Torfason, Robert, et al. "Towards image understanding from deep compression without decoding." arXiv preprint arXiv:1803.06131 (2018).).
20. (Currently Amended): Torfason teaches an offloading system comprising:
a terminal device; and a server device (images are transmitted (e.g. from a mobile device) before processing (e.g. in the cloud); Torfason, [§1, p2, ¶4]),
wherein the terminal device comprises: a camera configured to obtain an image corresponding to original data (images are transmitted (e.g. from a mobile device); Torfason, [§1, p2, ¶4]);
a first memory storing one or more instructions at least one first processor configured to execute the one or more instructions stored in the first memory (a cloud photo storage application where every image is processed immediately upon upload for indexing and search purposes; Torfason, [§1, p2, ¶4]) to:
generate latent representation data by using an extractor model having received the original data as an input (the encoder of the convolutional autoencoder produces a compressed representation (feature map), (i.e., latent representation data); Torfason, [§3.1, p 3, ¶1]); and
offload the latent representation data onto the server device, the server device comprises: a second memory storing one or more instructions (images are transmitted (e.g. from a mobile device) before processing (e.g. in the cloud); Torfason, [§1, p2, ¶4];); and
at least one second processor configured to execute the one or more instructions stored in the second memory to:
receive offloaded data from the terminal device (A typical use case is a cloud photo storage application where every image (every image offloaded from a mobile phone to cloud storage) is processed immediately upon upload; Torfason, [§1, p2, ¶4]));
modify the offloaded data to fit a structure of an input to a deep neural network model, by using an input structure modifier (the filters are upsampled instead of downsampling the feature maps; Torfason, [§5.1, p6, ¶1]); and
output inferred data corresponding to the offloaded data by using the deep neural network model having received the modified data as an input (When using the cResNets (the compressed representations) the output feature map (i.e., inferred data) has the same spatial dimensions as the input compressed representation (i.e., offloaded data). This results in comparable sized feature maps for both the compressed representation and the reconstructed RGB images; Torfason, [§5.1, p 6, ¶1]),
the extractor model and the deep neural network model are jointly trained by updating a training parameter set of the extractor model and a training parameter set of the deep neural network model (seen in Fig. 2 b) where all parts, encoder, decoder, and inference network, are trained at the same time…we first initialize the compression network and the classification network from a trained state obtained as described in Section 3 and 4. After initialization the networks are both finetuned jointly; Torfason, [§6.1, p8, ¶¶1-2]; joint training of compression and inference tasks; Torfason, [§A.1, p13, ¶3])
to minimize a value of a loss function of the deep neural network model (optimize for mean-squared reconstruction error; Torfason, [§6.1, p8, ¶1]),
a size of the latent representation data is predefined (Torfason, See Table 1 provides spatial dimensions of architectures), and
to output the inferred data, the at least one second processor is further configured to execute the one or more instructions stored in the second memory to input the modified data into the deep neural network model without decoding (For the compressed representations, we feed the output of the fixed encoder (the compressed representation) as input to the cResNets (decoder is not needed); Torfason, [§4.4, p 5, ¶1]).
Claims 1 and 11 are similarly analyzed as analogous claim 20.
2. (Previously presented): Torfason teaches the method of claim 1. Torfason further teaches wherein the extractor model is implemented as an encoder model of an autoencoder model (of the convolutional autoencoder produces a compressed representation (feature map)); Torfason, [§3.1, p3, ¶1]).
8. (Original): Torfason teaches the method of claim 1. Torfason further teaches wherein the original data includes at least one of an image, a video, an audio, a text, and a sensor value to be used in an application using the deep neural network model (image compression with an inference task; Torfason, [§A.1, p13, ¶3]).
9. (Original): Torfason teaches the method of claim 1. Torfason further teaches wherein the deep neural network model is a model that performs image classification, image segmentation, image captioning, object detection, depth estimation, localization, or pose estimation, based on the original data (evaluate our methods for image classification and semantic segmentation from compressed representations in Sections; Torfason, [§1, p3, ¶1]).
Claim 12 is similarly analyzed as analogous claim 2.
Claim 18 is similarly analyzed as analogous claim 8.
Claim 19 is similarly analyzed as analogous claim 9.
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 5 and 15 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Torfason in view of .”Zhang” (Zhang, et. al., US 20220292654 A1)
5. (Previously presented): Torfason teaches the method of claim 1. Torfason further teaches wherein the input structure modifier includes a single upsampling layer and {a single convolutional layer} (To get the reconstruction
x
^
the compressed representation is fed into the decoder, which mirrors the encoder, but uses upsampling and deconvolutions instead of subsampling and convolutions; Torfason, [§A.1, p13, ¶2]).
While Torfason teaches an upsampling layer, Torfason does not explicitly disclose a single upsampling layer and a single convolution layer.
However, Zhang, a similar field of endeavor of generating images that depict original images using neural networks, discloses wherein the decoder model includes a single upsampling layer and a single convolutional layer (Zhang, ¶[0107]; decoder 606 or neural network decoder 422 includes a number of layers, including ConvBlock layers, Upsampling layers, and a single convolutional layer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a single upsampling layer and a single convolutional layer in the decoder as taught by Zhang to the invention of Torfason. The motivation to do so would be to generate decoded images with the same dimensions as the original image, which may then be used for generating inferred images with the same dimensions as the original, for the purpose of generating lossless images as perceived by human vision.
Claim 15 is similarly analyzed as analogous claim 5.
Claims 6 and 16 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Torfason in view of ”Aytekin” (Aytekin, et. al., WO 2019185987 A1).
6. (Previously presented): Torfason teaches the method of claim 1. Torfason further teaches wherein the deep neural network model is a first deep neural network model, the inferred data is first inferred data (See Fig 2b), “the white block [is] an inference network”; Torfason, [p2]:
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),
{the method further comprises outputting second inferred data corresponding to the offloaded data by using a second deep neural network model having received the modified data as an input, and
the extractor model is jointly trained by using loss information of the first deep neural network model and loss information of the second deep neural network model}.
Torfason does not explicitly disclose the method further comprises outputting second inferred data corresponding to the offloaded data by using a second deep neural network model having received the modified offloaded data as an input, and
the extractor model is jointly trained by using loss information of the first deep neural network model and loss information of the second deep neural network model.
However, Aytekin, a similar field of endeavor of use of neural networks such as autoencoders for compressing and de-compressing data, teaches the method further comprises outputting second inferred data corresponding to the offloaded data by using a second deep neural network model having received the modified offloaded data as an input (Aytekin, Fig 5B, shown below, and ¶[0062]; neural networks 700A, 700B, and 700C, used in an inference phase), and
the extractor model is jointly trained by using loss information of the first deep neural network model and loss information of the second deep neural network model (Aytekin, ¶[0035], and See Fig 1, shown below, The final loss 165 is used in a training operation 170 (of the training method 101), the outputs 171, 172 of which are respectively routed back to the neural encoder 120 and the neural decoder 130.).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include multiple neural networks for inferred data, and using the loss as taught by Aytekin to the invention of Torfason. The motivation to do so would be to have multiple neural networks using various techniques, that are “expert” neural networks that may then be used at inference time.
Claim 16 is similarly analyzed as analogous claim 6.
Claims 7, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Torfason in view of “Zhu” (Zhu et al., CN 113159073 A).
7. (Original): Torfason teaches the method of claim 1.
Torfason does not explicitly disclose a training the extractor model with a knowledge distillation technique.
However, Zhu, a similar field of endeavor of compressing and processing offloaded data from edge device, teaches, wherein the extractor model is trained by using a knowledge distillation technique (The teacher network includes a first feature extraction module and a first classifier, and the first feature extraction module includes one or more stages. Connected first feature unit, the student network includes a second feature extraction module and a second classifier, the second feature extraction module includes one or more cascaded second feature units, wherein the first feature; Zhu, ¶[51])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include training the extractor with a knowledge distillation technique as taught by Zhu to the invention of Torfason. The motivation to do so would be to transfer the Dark Knowledge (Dark Knowledge) of the complex teacher network to the StudentNetwork (StudentNetwork); so that the student network can have the reasoning prediction capability of the teacher network, so that the student network not only has fast calculation speed, small memory requirement, but also has high prediction performance.
10. (Original): Torfason teaches the method of claim 1.
Torfason does not explicitly disclose further comprising transmitting the inferred data to the terminal device.
However, Zhu teaches further comprising transmitting the inferred data to the terminal device (the server will transmit the calculation results to the edge device after the calculation is completed; Zhu, [48]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include training the extractor with a knowledge distillation technique as taught by Zhu to the invention of Torfason. The motivation to do so would be to quickly and accurately employ complex deep neural networks.
Claim 17 is similarly analyzed as analogous claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Hu” (Hu, Yueyu, et al. "Learning end-to-end lossy image compression: A benchmark." IEEE Transactions on Pattern Analysis and Machine Intelligence 44.8 (2021): 4194-4211) provides a review of end-to-end learned image compression methods, and introduces a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction.
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.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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