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 .
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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
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.
Claims 1-16 are 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.
Claims 1 and 9 recite the limitation “a target radar image.” The limitation renders the claims indefinite because it is not clear whether this image corresponds to the earlier-recited “a low-resolution radar image of a target”, “a high-resolution radar image of the target”, or a new/different image of the same or different target.
For the purpose of further examination, the claims have been interpreted as performing SR using the LR and HR images corresponding to the same target.
Claims 2-8 and 10-16 depend from claims 1 and 9, respectively, and therefore inherit this deficiency.
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) 1-7 and 9-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cho et al. (“Guided Generative Adversarial Network for Super Resolution of Imaging Radar,” 2020 17th European Radar Conference (EuRAD), 10-15 January 2021), hereinafter referred to as Cho.
Regarding claim 1, Cho teaches a device for radar image super-resolution (Cho Abstract: “We propose a super-resolution algorithm for radar data”), the device comprising:
a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction stored in the memory (Cho pg. 1 right column: “MIMO system … uses the convolutional neural network (CNN)-based adversarial learning framework … data preprocessing” – MIMO systems using CNN to process image data require the use of a computer comprising a memory and processor),
wherein the processor generates a low-resolution radar image of a target, generates a high-resolution radar image of the target, trains a super-resolution model for performing super-resolution on radar images on the basis of the low-resolution radar image and the high-resolution radar image, and performs super-resolution on a target radar image using the super-resolution model (Cho pg. 2 left column: “For the SR of radar data, an HR super-resolved data, SHR must be estimated from a low-resolution (LR) input data, SLR”; Cho pg. 2 right column: “Our ultimate goal is to estimate the generating function G that produces the corresponding SHR from SLR”; further see Cho eq. (5): LSR calculated based on SLR and SHR).
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Regarding claim 2, Cho teaches the device of claim 1, wherein the processor generates the low-resolution radar image by imaging low-resolution radar data generated through a first radio frequency (RF) simulation (Cho Abstract: “enabling reliable super resolution of radar data … can be generalized and applied to all dimensions of radar data”; Cho Figs. 2-3 & pg. 2 right column: “the radar-specific data-augmentation technique … a radar coordinate-transfer module … the radar SR network based on adversarial learning”).
Regarding claim 3, Cho teaches the device of claim 2, wherein the first RF simulation includes operations of:
generating a target model corresponding to the target and a radar model corresponding to a radar apparatus (Cho pg. 1 left column: “a multiple-input multiple-output (MIMO) antenna array structure is adopted to realize HR azimuth and elevation angle”; Cho pg. 3 right column: “From NA virtual antennas for estimating the azimuth angle”);
when a radar signal with a first frequency is transmitted through the radar model, predicting the radar signal that is reflected by the target model and received by the radar model (Cho pg. 3-4: “From NA virtual antennas for estimating the azimuth angle, 0.8NA and 0.2NA antennas can be selected to construct the HR and LR RA maps, respectively … the RSR-GAN was trained based on the weighted sum of MSE loss to secure content similarity and adversarial loss to find SR results within the actual HR data manifold”); and
generating the low-resolution radar data on the basis of the predicted radar signal (Cho pg. 3-4 discussed above, 0.2NA).
Regarding claim 4, Cho teaches the device of claim 3, wherein the processor generates the high-resolution radar image by imaging high-resolution radar data generated through a second RF simulation (Cho pg. 3-4 discussed above, 0.8NA).
Regarding claim 5, Cho teaches the device of claim 4, wherein the second RF simulation includes operations of:
generating a target model corresponding to the target and a radar model corresponding to the radar apparatus (Cho pg. 1 left column & pg. 3 right column discussed above);
when a radar signal with a second frequency, which is higher than the first frequency, is transmitted through the radar model, predicting the radar signal that is reflected by the target model and received by the radar model (Cho pg. 3-4 discussed above, 0.8NA); and
generating the high-resolution radar data on the basis of the predicted radar signal (Cho pg. 3-4 discussed above, 0.8NA).
Regarding claim 6, Cho teaches the device of claim 1, wherein the processor trains the super-resolution model using at least one of a generative adversarial network (GAN), a transformer, a convolutional neural network (CNN), and an autoencoder (Note that only one of the alternative limitations is required by the claim language. Cho pg. 1 right column: “The proposed method uses the convolutional neural network (CNN)-based adversarial learning framework to perform SR on images of the surrounding environment obtained through radar”; Cho pg. 3 left column: “C. Radar SR using Generative Adversarial Network”).
Regarding claim 7, Cho teaches the device of claim 6, wherein, when the super-resolution model is trained using the GAN, the processor generates a fake high-resolution radar image by inputting the low-resolution radar image to a generator of the GAN, calculates a discriminator loss and a generator loss by inputting the fake high-resolution radar image and the high-resolution radar image to a discriminator of the GAN, and updates the generator and the discriminator to minimize the discriminator loss and the generator loss (Cho Fig. 5 & pg. 3 right column: “The problem of GAN-based SR is that the generator tries to produce a realistic result that can fool the discriminator into not producing a reliable result. To resolve to this problem, we propose to input supplementary information, such as results of the MUSIC algorithm, into the discriminator as a guide, as shown in Fig. 5, so that it is not misled if the generator does not produce an SR result with reliable angle estimation … Training object LSR is defined by the weighted combination of content loss LC and adversarial loss Ladv … adds a gradient penalty term to the discriminator loss and removes weight clipping”).
Regarding claim 9, Cho further teaches a method comprising the processes described in claim 1 (Cho Abstract: “algorithm”). Therefore, claim 9 is rejected using the same rationale as applied to claim 1 discussed above.
Claim 10 is rejected using the same rationale as applied to claim 2 discussed above.
Claim 11 is rejected using the same rationale as applied to claim 3 discussed above.
Claim 12 is rejected using the same rationale as applied to claim 4 discussed above.
Claim 13 is rejected using the same rationale as applied to claim 5 discussed above.
Claim 14 is rejected using the same rationale as applied to claim 6 discussed above.
Claim 15 is rejected using the same rationale as applied to claim 7 discussed above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho et al. (“Guided Generative Adversarial Network for Super Resolution of Imaging Radar,” 2020 17th European Radar Conference (EuRAD), 10-15 January 2021), in view of Yang et al. (US 2023/0177643 A1), hereinafter referred to as Cho and Yang, respectively.
Regarding claim 8, Cho teaches the device of claim 6, wherein, when the super-resolution model is trained using GAN and CNN, but does not appear to explicitly teach using a transformer.
Pertaining to the same field of endeavor, Yang teaches training the super-resolution model using the transformer, the processor extracts a feature from the low-resolution radar image through the transformer, generates a fake high-resolution radar image on the basis of the feature, compares the fake high-resolution radar image with the high-resolution radar image, and updates the transformer on the basis of a comparison result (Yang ¶¶0021: “the computing device 100 may receive a reference image 172 and an input image 171 to be transformed to a target resolution through the input device 150. In general, the input image 171 has a resolution lower than the target resolution, and may be referred to as a low-resolution image … The computing device 100 may transform the input image 171 into an output image 180 having the target resolution based on the reference image 172. The output image 180 reflects texture features of the reference image 172”; Yang ¶¶0041: “the neural network may be a shallow convolutional neural network, and the parameters of the neural network may also be continuously updated during the training process of the texture transformer 220”; Yang ¶¶0063: “The subject matter described herein has mainly been described above in the context of super-resolution tasks”; Yang Figs. 2-4).
Cho and Yang are considered to be analogous art because they are directed to image super resolution. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the guided GAN for super resolution of imaging radar (as taught by Cho) to use and update a transformer (as taught by Yang) because the combination efficiently and accurately transfers the textures of the reference image and obtain a clearer and more realistic image processing result (Yang Abstract & ¶¶0002).
Claim 16 is rejected using the same rationale as applied to claim 8 discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
