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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-4, 6-9 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.
Claim(s) 1-4, 6-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US2021/0215818) in view of Goodfellow et al. (“Generative Adversarial Nets”) and Peng et al. (US2022/0414959).
To claim 1, Kim teach an apparatus for learning image recognition (Fig. 3; paragraph 0005), the apparatus comprising:
a converting device (auto-encoder engine) configured to generate an enhanced image (TSB image) enhanced in a specific element (latent vector) by converting an input image (SAR image) to the enhanced image through a generative artificial intelligence (AI) model (ANN) to enhance a latent vector for at least one element (Fig. 8; paragraph 0013, receiving a real synthetic aperture radar (SAR) image… generating, for the SAR image and using an auto-encoder engine, an auto-encoder-generated TSB image using an ANN. The method includes computing, using a discriminator engine, an image difference between the auto-encoder-generated TSB image and the model-based TSB image; paragraph 0070, a GAN comprises the auto-encoder engine and the discriminator engine; paragraph 0074, the auto-encoder engine comprises an encoder sub-engine, a latent vector, and a decoder sub-engine. The encoder sub-engine receives the new SAR image… The latent vector receives data from the encoder sub-engine and transmits processed data to the decoder sub-engine. The decoder sub-engine outputs the generated TSB image);
a training device configured to train an image recognition model (discriminator engine) based on AI, by utilizing the enhanced image (paragraphs 0030-0032, model is continuously refined thru training);
a determining device configured to compare a recognition rate, which is provided by the image recognition model, for the at least one element in the enhanced image with a target recognition rate preset for the at least one element (obvious in paragraphs 0031-0033, 0050, the learning phase may end early and use the produced model satisfying the end-goal accuracy threshold); and
an analyzing device (auto-encoder engine) configured to analyze, when the at least one element has a recognition rate lower than the target recognition rate is present in the enhanced image, the latent vector of the at least one element having the recognition rate lower than the target recognition rate and provide the latent vector of the at least one element, which is analyzed, to the converting device such that the latent vector of the at least one element is reflected in the generative AI model (obvious in paragraphs 0031-0033, 0050, For example, if the training phase is designed to run n epochs and produce a model with at least 95% accuracy, and such a model is produced before the 11.sup.th epoch, the learning phase may end early and use the produced model satisfying the end-goal accuracy threshold. Similarly, if a given model is inaccurate enough to satisfy a random chance threshold (e.g., the model is only 55% accurate in determining true/false outputs for given inputs), the learning phase for that model may be terminated early, although other models in the learning phase may continue training. Similarly, when a given model continues to provide similar accuracy or vacillate in its results across multiple epochs—having reached a performance plateau—the learning phase for the given model may terminate before the epoch number/computing budget is reached).
But, Kim do not expressly disclose extract and analyze the latent vector of the at least one element having the recognition rate lower than the target recognition rate among the plurality of elements and provide the latent vector of the at least one element, which is analyzed to have the recognition rate lower than the target recognition rate.
However, a scenario with a latent vector of an element having recognition rate lower than the target recognition rate would have been obvious, wherein for AI to improve recognition rate taking failed latent vector into model for adjusted training would also have been obvious.
In furthering said obviousness, Goodfellow teach when the at least one element has a recognition rate lower than the target recognition rate is present in the enhanced image, the latent vector of the at least one element having the recognition rate lower than the target recognition rate (e.g., ½) and provide the latent vector of the at least one element, which is analyzed, to the converting device such that the latent vector of the at least one element is reflected in the generative AI model (pages 3-4, Fig. 1, after several steps of training, if G and D have enough capacity, they will reach a point at which both cannot improve… The discriminator is unable to differentiate between the two distributions, i.e., D(x)=½), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Goodfellow into the apparatus of Kim, in order to further implementation of training convergence.
Peng teach training virtual image generating model (abstract), wherein latent vector is obtained and compared to obtain accuracy rate, wherein training model would be adjusted to continue training when the accuracy rate is less than a preset accuracy rate threshold (paragraphs 0046, 0050, 0066).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Peng into the Goodfellow the apparatus of Kim and Goodfellow, in order to further implementation of learning process.
To claim 6, Kim, Goodfellow and Peng teach a method for learning image recognition (as explained in response to claim 1 above).
To claims 2 and 7, Kim, Goodfellow and Peng teach claims 1 and 6.
Kim, Goodfellow and Peng teach wherein the converting device further generates a newly enhanced image by reflecting the latent vector of the at least one element, which is analyzed by the analyzing device, in the generative AI model and converting the input image into the newly enhanced image enhanced in the latent vector of the at least one element which is analyzed (obvious in teachings of Kim and Goodfellow, since latent vector transmits processed data to decoder sub-engine, auto-encoder engine comprises latent vector and uses ANN, which leads to latent vector being reflected in ANN).
To claims 3 and 8, Kim, Goodfellow and Peng teach claims 2 and 7.
Kim, Goodfellow and Peng teach wherein the determining device further compares the recognition rate, which is provided by the image recognition model, for the at least one element in the newly enhanced image with the target recognition rate, and wherein the analyzing device further analyzes, when the at least one element has a recognition rate lower than the target recognition rate is present in the newly enhanced image, the latent vector of the at least one element having the recognition rate lower than the target recognition rate, and provides the latent vector of the at least one element, which is analyzed, to the converting device (Goodfellow, page 4, Fig. 1, After several steps of training, if G and D have enough capacity, they will reach a point at which both cannot improve because pg=pdata. The discriminator is unable to differentiate between the two distributions, i.e. D(x)=1/2).
.
To claims 4 and 9, Kim, Goodfellow and Peng teach claims 3 and 8.
Kim, Goodfellow and Peng teach wherein the analyzing device further iteratively provides the latent vector of the at least one element, which is analyzed, to the converting device until the recognition rate for the at least one element is equal to or greater than the target recognition rate for the at least one element (Kim, paragraphs 0030-0032).
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 ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 October 11, 2026
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