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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-5 and 17-20 in the reply filed on 2/19/2026 is acknowledged. Claims depending on an allowable independent claim shall be rejoined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 2 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “compression comprises a non-learning based compression and a learning based compression.” This is not disclosed in the specification in a manner that demonstrates possession. Figs. 2-3 depict a non-learning based compression (the standard video encoded and decoder), and Fig. 4 depicts that machine-learning based auto-encoder. There is no description of how they are combined into a non-learning and learning based compression. Therefore Applicant has not demonstrated possession.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “the window-based attention module is applied to a super-resolution.” This is not disclosed in the specification in a manner that demonstrates possession. Fig. 4 depicts and auto-encoder, and Fig. 5 depicts a transformer. There is no description or explanation of how these parts are used to achieve super-resolution. Modifying/applying the transformer (window-based attention module) to achieve super-resolution is non-trivial, therefore the structure and implementation must be shown. Therefore Applicant has not demonstrated possession.
Claim 22 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “the window-based attention module is applied to an in-loop filtering.” This is not disclosed in the specification in a manner that demonstrates possession. Fig. 4 depicts and auto-encoder, and Fig. 5 depicts a transformer. There is no description or explanation of how these parts are used to achieve in-loop filtering. Modifying/applying the transformer (window-based attention module) to achieve in-loop filtering is non-trivial, therefore the structure and implementation must be shown. Therefore Applicant has not demonstrated possession.
Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “the window-based attention module is applied to at least one of a pre-processing or a post-processing.” This is not disclosed in the specification in a manner that demonstrates possession. Fig. 4 depicts and auto-encoder, and Fig. 5 depicts a transformer. There is no description or explanation of how these parts are used to achieve pre- or post-processing. Modifying/applying the transformer (window-based attention module) to achieve pre- or post-processing is non-trivial, therefore the structure and implementation must be shown. Therefore Applicant has not demonstrated possession.
Claims 25, 29 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “a convolution-based super resolution network.” This is not disclosed in the specification in a manner that demonstrates possession. Fig. 4 depicts and auto-encoder, and Fig. 5 depicts a transformer. There is no description or explanation a convolution-based super super-resolution network. Implementing a convolution-based super-resolution network is non-trivial, therefore the structure and implementation must be shown. Therefore Applicant has not demonstrated possession.
Claim 27 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “an encoder in the convolution-based compression network is replaced by the window-based attention module.” This is not clear. The “window based attention module” is the same as “the transformer.” Spec. at [0068]. The “encoder” is the “swin transformer encoding block.” Spec. Figs. 4-5. What does it mean for the swin transformer encoding block to be replaced by the transformer? The encoder appears to be replaced by itself.
Claim 28 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention: The claim recites, “an encoder in the convolution-based compression network is replaced by the window-based attention module.” This is not clear. The “window based attention module” is the same as “the transformer.” Spec. at [0068]. The “decoder” is the “swin transformer decoding block.” Spec. Figs. 4-5. What does it mean for the swin transformer decoding block to be replaced by the transformer? The decoder appears to be replaced by itself.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “a convolution layer in the convolutional network is replaced by layer of the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final model is claimed, or the act of replacing the original/base part is claimed.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “convolution layer of the convolution-based compression network is replaced by the layer of the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 25 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “convolution layer of the convolution-based super resolution network is replaced by the layer of the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 26 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “a portion of modules in the convolutional network is replaced by the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 27 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “an encoder in the convolution-based compression network is replaced by the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 28 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “a decoder in the convolution-based compression network is replaced by the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 29 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “a residual block in the super resolution network is replaced by the window-based attention module.” The original/base “network” is not disclosed, therefore the part that is “replaced” is not clear. The claim does not make clear whether the final network is claimed, or the act of replacing is claimed.
Claim 29 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites, “a residual block in the super resolution network.” This is unclear. “[R]residual block in the super resolution network” has not been described in the specification.
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.
Claim(s) 1-5, 17-19, 24, 26-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lu (NPL “Transformer-based Image Compression,” arXiv 2021).
Regarding Claim 1, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses a method of video processing (Transformer-based image compression, title), comprising:
during a conversion (compression, Abstract, arithmetic encoding (AE), Fig. 2, arithmetic decoding (AD), Fig. 2, reconstruct pixel blocks, Section 3 page 4) between a video unit (input x, Section 3 page 4; input image, Section 3.2 page 5) of a video (video, Abstract) and a bitstream (bits, Fig. 2) of the video (video, Abstract), applying a signal process (transforms the input x into the latent features x^, Section 3.1 page 4, using 3 NTUs, Section 3.2 page 5) to the video unit (input x, Section 3 page 4; input image, Section 3.2 page 5) based at least in part on a window-based attention module (Swin transformer block, Section 1.2 page 2; having window attention and shifted window attention, Section 3.2 page 6), wherein the signal process comprises a restoration (hyper decoder hs reverse the processing steps of ga and ha, Section 3.2) of the video unit (input image, Section 3.2);
and performing the conversion (arithmetic encoding/decoding, Fig. 2, Section 3.2 page 5) based on the processed video unit (quantize y into y-hat and entropy code y-hat, Section 3.1 page 4).
Regarding Claim 2, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein the window-based attention module is applied to a compression of the video unit (swin transformer is in the model, Fig. 2), and the compression comprises a non-learning based compression (quantization, Fig. 2) and a learning based compression (swin transformer is in the model, Fig. 2; transforming input x into features y is part of image compression, Section 3.1 page 4).
Regarding Claim 3, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein applying the signal process comprises:
applying the signal process (using 3 NTUs, Section 3.2 page 5) to the video unit (on the input image x, Fig. 2, Section 3.1, 3.2) based on a combination of the window-based attention module and a convolutional network (each NTU has a Swin transformer block and a convolutional layer, Section 1.2 page 2).
Regarding Claim 4, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 3, wherein a convolution layer in the convolutional network (existing LICs that mainly use CNNs for image coding, Section 3.1) is replaced by a layer of the window-based attention module (using the VAE architecture, use the NTU as the basic module, the NTU having a Swin transform block and a convolutional layer, Section 2.1 page 2).
Regarding Claim 5, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 4, wherein the convolutional network comprises a convolution-based compression network (existing LICs that mainly use CNNs for image coding, Section 3.1), and a convolution layer of the convolution-based compression network is replaced by the layer of the window-based attention module (each NTU has a Swin transformer block and a convolutional layer, Section 1.2 page 2the VAE architecture being a number of convolutional layers stacked, Section 1.1 page 1; using the VAE architecture, use the NTU as the basic module, the NTU having a Swin transform block and a convolutional layer, Section 2.1 page 2).
Regarding Claim 17, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein the conversion includes encoding the video unit into the bitstream, or wherein the conversion includes decoding the video unit from the bitstream (arithmetic encoding/decoding, Fig. 2, Section 3.2 page 5).
Regarding Claim 18, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses a method of video processing (Transformer-based image compression, title) discloses an apparatus for video processing comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor (TIC is implemented on top of an open-source CompressAI PyTorch Library, Section 4 page 7) …. The remainder of Claim 18 is rejected on the grounds provided in Claim 1.
Regarding Claim 19, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses a method of video processing (Transformer-based image compression, title) discloses a non-transitory computer-readable storage medium storing instructions that cause a processor (TIC is implemented on top of an open-source CompressAI PyTorch Library, Section 4 page 7) …. The remainder of Claim 19 is rejected on the grounds provided in Claim 1.
Regarding Claim 24, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein the window-based attention module is applied in a compression framework (transforms the input x into the latent features y, Section 3.1 page 4, using 3 NTUs, Section 3.2 page 5, followed by quantization, Fig. 2).
Regarding Claim 26, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 3, wherein a portion of modules in the convolutional network (existing LICs that mainly use CNNs for image coding, Section 3.1) is replaced by the window-based attention module (each NTU has a Swin transformer block and a convolutional layer, Section 1.2 page 2).
Regarding Claim 27, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 26, wherein the convolutional network comprises a convolution-based compression network (Transformer-based Image Compression, Abstract, Fig. 2, using convolutions, Fig. 2), and an encoder in the convolution-based compression network (context modeling, Fig. 3 caption – part of entropy coding) is replaced by the window-based attention module (a causal attention model is used for entropy coding, Fig. 3).
Regarding Claim 28, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 26, wherein the convolutional network comprises a convolution-based compression network (Transformer-based Image Compression, Abstract, Fig. 2, using convolutions, Fig. 2), and a decoder in the convolution-based compression network (context modeling, Fig. 3 caption – part of entropy coding) is replaced by the window-based attention module (a causal attention model is used for entropy coding, Fig. 3).
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 (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 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(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (NPL “Transformer-based Image Compression,” arXiv 2021) in view of Ge (US PG Publication 2023/0396810).
Regarding Claim 20, Ge (US PG Publication 2023/0396810) teaches a method for storing a bitstream (encoded bitstream 21 may be transmitted to the video decoder 30, or may be stored in a memory [0170),
storing the bitstream in anon-transitory computer-readable recording medium (encoded bitstream 21 may be transmitted to the video decoder 30, or may be stored in a memory [0170).
The remainder of Claim 20 is rejected on the grounds provided in Claim 1.
One of ordinary skill in the art before the application was filed would have been motivated to store the bitstream of Lu because storing video bitstreams allows them to be saved, shared, or watched, making them accessible to consumers.
Claim(s) 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (NPL “Transformer-based Image Compression,” arXiv 2021) in view of Niu (NPL: “Single Image Super-Resolution via a Holistic Attention Network,” arXiv 2020).
Regarding Claim 21, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein the window-based attention module is applied (swin transformer is in the model, Fig. 2).
Lu does not disclose, but Niu (NPL: “Single Image Super-Resolution via a Holistic Attention Network,” arXiv 2020) teaches attention module is applied to a super-resolution of the video unit (attention modules collaboratively improve the SR results, Section I page 2 last bullet).
One of ordinary skill in the art would have been motivated to use the model of Lu to perform super resolution because super resolution upsizes the video frame, which renders the video more suitable for larger TV screens, providing better service to the consumer.
Regarding Claim 23, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1, wherein the window-based attention module is applied (swin transformer is in the model, Fig. 2).
Lu does not disclose, but Niu (NPL: “Single Image Super-Resolution via a Holistic Attention Network,” arXiv 2020) teaches attention module is to at least one of a pre-processing or a post-processing of the video unit (attention modules collaboratively improve the SR results, Section I page 2 last bullet).
One of ordinary skill in the art would have been motivated to use the model of Lu to perform super resolution because super resolution upsizes the video frame, which renders the video more suitable for larger TV screens, providing better service to the consumer.
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over Lu (NPL “Transformer-based Image Compression,” arXiv 2021) in view of Wang (NPL: “Attention-Based Dual-Scale CNN In-Loop Filter for Versatile Video Coding,” IEEE 2019).
Regarding Claim 22, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 1.
Lu does not disclose, but Wang (NPL: “Attention-Based Dual-Scale CNN In-Loop Filter for Versatile Video Coding,” IEEE 2019) teaches wherein the window-based attention module is applied to an in-loop filtering in the compression of the video unit (Attention-Based Dual-Scale CNN In-Loop Filter for Versatile Video Coding, title).
One of ordinary skill in the art would have been motivated to use the model of Lu to perform loop filtering because loop filtering is part of a video coder and improves image quality, providing better service to the consumer.
Claim(s) 25 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (NPL “Transformer-based Image Compression,” arXiv 2021) in view of Dong (NPL: “Accelerating the Super-Resolution Convolutional Neural Network” arXiv 2016).
Regarding Claim 25, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 4, a convolution layer of the convolution-based [] network (existing LICs that mainly use CNNs for image coding, Section 3.1) is replaced by the layer of the window-based attention module (using the VAE architecture, use the NTU as the basic module, the NTU having a Swin transform block and a convolutional layer, Section 2.1 page 2).
Lu does not disclose, but Dong (NPL: “Accelerating the Super-Resolution Convolutional Neural Network” arXiv 2016) teaches wherein the convolutional network comprises a convolution-based super resolution network (Super-Resolution Convolutional Neural Network, title).
One of ordinary skill in the art would have been motivated to use the model of Lu to perform super resolution because super resolution upsizes the video frame, which renders the video more suitable for larger TV screens, providing better service to the consumer.
Regarding Claim 29, Lu (NPL “Transformer-based Image Compression,” arXiv 2021) discloses the method of claim 26, a residual block in the [] network is replaced by the window-based attention module (swin transformer is in the model, Fig. 2).
Lu does not disclose, but Dong (NPL: “Accelerating the Super-Resolution Convolutional Neural Network” arXiv 2016) teaches wherein the convolutional network comprises a super resolution network (Super-Resolution Convolutional Neural Network, Title).
One of ordinary skill in the art would have been motivated to use the model of Lu to perform super resolution because super resolution upsizes the video frame, which renders the video more suitable for larger TV screens, providing better service to the consumer.
Response to Arguments
Applicant’s arguments filed 6/12/2026 have been considered but are not persuasive. Applicant argues that the transform process of Lu does not involve the restoration of the video unit, but Lu discloses that hyper decoder hs reverse the processing steps of ga and ha, Section 3.2, which is a restoration process. Ge is not relied upon to teach this feature, therefore applicant’s arguments regarding Ge are unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220239944 A1 – auto-encoder with self-attention
US 12120348 B2 - auto encoder with a shifted window self-attention block (Swin)
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.
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/SHADAN E HAGHANI/ Examiner, Art Unit 2485
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