CTFR 18/533,722 CTFR 88485 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 in response to the Amendment filed on 12/31/2025. Claims 1-20 are under examination. Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US 2025/0104288 A1), Strubbe et al. (US 20200382459 A1) and Tan et al. (US 2018/0068429 A1) . Regarding claim 1 , Agarwal et al. discloses A computer-implemented method that enables data protection using steganography and machine learning [par. 0002, steganographic image that includes the secret, par. 0048, convolutional neural network (“CNN”) that is pretrained using unsupervised learning] , comprising: obtaining, using at least one hardware processor, a manipulated steganography image, [par. 0040, image transformations and perturbations applied to the steganographic image 118] ; and decoding, using the at least one hardware processor, the recovered steganography image using a second trained machine leaning model, wherein the decoding extracts a secret message embedded in the steganography image [abs, “The steganographic image includes the secret and is visually indiscernible from the digital image. Further, the processing device is configured to recover the secret from the steganographic image, such as by training and leveraging a secret decoder to extract the secret”, par. 0077, “The steganographic model 202 is further operable to extract the secret, e.g., an extracted secret 220, from the steganographic image 118 using the secret decoder 208 (block 1014). As described above with respect to the training process, the secret decoder 208 is generally a “lightweight” decoder such as a modified Resnet50 model described by Kaiming He, et al. Deep Residual Learning for Image Recognition ”, par. 0088, “the secret 122 includes a bit string, e.g., a sequence of binary digits. In various examples, the bit string is representative of one or more characters such as a “hidden message” to be concealed within a steganographic image 118”] . Agarwal et al. does not explicitly disclose the manipulated steganography image is a steganography image transformed using a manipulation key. However, Strubbe et al. teaches the manipulated steganography image is a steganography image transformed using a manipulation key [par. 0007, “a steganography processing unit (for example a plug-in) receives an extracted stego-image file and transforms the extracted stego-image file using a centrally managed key with a desired degree of effect on the image to obtain a transformed stego-image file”] . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to incorporate the teaching of Strubbe et al. into the teaching of Agarwal et al. with the motivation such that the obfuscation of the image would prevent a perpetrator from extracting the embedded data as taught by Strubbe et al. [Strubbe et al.: par. 0024] . They not explicitly disclose inputting, using the at least one hardware processor, the manipulated steganography image to a first trained machine learning model to recover the steganography image, wherein the first trained machine learning model outputs a recovered steganography image including a secret message. However, Tan et al. in the field relates to method for image steganalysis based on deep learning teaches inputting, using the at least one hardware processor, the manipulated steganography image to a first trained machine learning model to recover the steganography image, wherein the first trained machine learning model outputs a recovered steganography image including a secret message [pars. 0015-0033, “filtering steganographic class images and true class images in a training set with a high-pass filter to obtain steganographic class residual images and true class residual images… training a deep network model using said steganographic class residual images and true class residual images to obtain a deep network based steganalysis model… filtering the image to be detected with the high-pass filter to obtain a residual image… it can create an automatic blind detection model through deep learning and can identify steganography images accurately”] . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to incorporate the teaching of Tan et al. into the teaching of Agarwal et al. and Strubbe et al. with the motivation to create an automatic blind steganalysis model through feature learning and can identify steganographic images accurately as taught by Tan et al. [Tan et al.: abs.] . Regarding claim 2 , the rejection of claim 1 is incorporated. Agarwal et al. further discloses the manipulation key comprises specific blurring pixels in an area of the steganography image [par. 0061, “the noise model 308 applies a variety of perturbations to the input image 402, such as Gaussian noise, shot noise, impulse noise, defocus blur, fog, brightness, contrast, pixelate, speckle noise, gaussian blur, spatter, saturation, jpeg compression, and frost”] . Regarding claim 3 , the rejection of claim 1 is incorporated. Agarwal et al. further discloses the manipulation key is a pattern of image effects applied to the steganography image [par. 0062, “the perturbed steganographic image 310 is blurry and has different coloring effects”, par. 0081, undergone several transformations, such as one or more lighting effects] . Regarding claim 4 , the rejection of claim 1 is incorporated. Agarwal et al. further discloses the first trained machine learning model and the second trained machine learning model execute via a trained engine, wherein the trained engine comprises at least one manipulation key [par. 0062, “the steganography module 116 includes a training module 204 that is operable to train the steganographic model 202”, par. 0058, “the training module 204 further includes a noise model 308 that is operable to apply various perturbations…”] . Tan et al. also teaches the first trained machine learning model a execute via a trained engine [par. 0008, “detecting image steganography is based on deep learning. In the present invention, the labeled images, including true class images and steganographic class images, in the training set are first filtered with a high-pass filter to obtain residual images, and then these obtained residual images are used to train a deep neural network, and finally a highly universal image steganalysis model is obtained” . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to incorporate the teaching of Tan et al. into the teaching of Agarwal et al. and Strubbe et al. with the motivation to create an automatic blind steganalysis model through feature learning and can identify steganographic images accurately as taught by Tan et al. [Tan et al.: abs.] . Regarding claim 5 , the rejection of claim 1 is incorporated. Agarwal et al. further discloses the steganography image is generated by encoding the secret message onto an original image [par. 0002, “The processing device further leverages a pretrained decoder of the autoencoder to generate a steganographic image that includes the secret and is visually indiscernible from the digital image based on the embedding of the secret and the embedding of the digital image”] . Regarding claim 6 , the rejection of claim 1 is incorporated. Agarwal et al. further discloses the manipulation key applies multiple effects to the steganography image [par. 0058, “the training module 204 further includes a noise model 308 that is operable to apply various perturbations…”, par. 0062, “the perturbed steganographic image 310 is blurry and has different coloring effects”, par. 0081, undergone several transformations, such as one or more lighting effects] . Regarding claim 8 , it recites limitations like claim 1. The reason for the rejection of claim 1 is incorporated herein. Regarding claim 9 , it recites limitations like claim 2. The reason for the rejection of claim 2 is incorporated herein. Regarding claim 10 , it recites limitations like claim 3. The reason for the rejection of claim 3 is incorporated herein. Regarding claim 11 , it recites limitations like claim 4. The reason for the rejection of claim 4 is incorporated herein. Regarding claim 12 , it recites limitations like claim 5. The reason for the rejection of claim 5 is incorporated herein. Regarding claim 13 , it recites limitations like claim 6. The reason for the rejection of claim 6 is incorporated herein. Regarding claim 15 , it recites limitations like claim 1. The reason for the rejection of claim 1 is incorporated herein. Regarding claim 16 , it recites limitations like claim 2. The reason for the rejection of claim 2 is incorporated herein. Regarding claim 17 , it recites limitations like claim 3. The reason for the rejection of claim 3 is incorporated herein. Regarding claim 18 , it recites limitations like claim 4. The reason for the rejection of claim 4 is incorporated herein. Regarding claim 19 , it recites limitations like claim 5. The reason for the rejection of claim 5 is incorporated herein. Regarding claim 20 , it recites limitations like claim 6. The reason for the rejection of claim 6 is incorporated herein . 07-22-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal et al. (US 2025/0104288 A1), Strubbe et al. (US 20200382459 A1) and Tan et al. (US 2018/0068429 A1) as applied to claim s 1-6, 8-13 and 15-20 above, and further in view of Jacobson (US 2024/0235847 A1) . Regarding claim 7 , the rejection of claim 1 is incorporated. Agarwal et al. discloses the steganography image. They do not explicitly disclose the steganography image is a seismic image. However, Jacobson teaches the steganography image is a seismic image [par. 0197, “the digital representation may be hidden within one or more images utilizing steganography or may be presented in a specific portion or portions of the visual spectrum”, par. 0221, record scene information include seismic data] . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to incorporate the teaching of Jacobson into the teaching of Agarwal et al., Strubbe et al. and Tan et al. with the motivation employing scene embedded digital watermarks for authenticating media data as taught by Jacobson [Jacobson: abs.] . Regarding claim 14 , it recites limitations like claim 7. The reason for the rejection of claim 7 is incorporated herein . Response to Arguments Applicant’s arguments, filed on 12/31/2025, with respect to rejection under 35 USC § 103 have been considered but are moot in view of the new ground(s) of rejection. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure : US 12424004 B2 Artificial Intelligence Based Steganographic Systems And Methods For Analyzing Pixel Data Of A Product To Detect Product Counterfeiting US 20250232411 A1 CASCADED MULTI-RESOLUTION MACHINE LEARNING FOR IMAGE PROCESSING WITH IMPROVED COMPUTATIONAL EFFICIENCY US 12206759 B1 System And Method Of Digital Steganography US 20240056477 A1 METHODS AND SYSTEMS FOR DETECTING MALICIOUS MESSAGES US 11823045 B2 Encoding And Decoding Apparatus US 20220405875 A1 SYSTEMS AND METHODS FOR INTELLIGENT STEGANOGRAPHIC PROTECTION US 20170126646 A1 IMAGE PROCESSING METHOD AND CLIENT DEVICE, IMAGE AUTHENTICATION METHOD AND SERVER DEVICE US 8391543 B1 Method And Apparatus For Preventing Data Leakage Facilitated By Steganography US 20200382459 A1 Steganography Mail Block Server Implementation 07-40 AIA 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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON CHIANG whose telephone number is (571)270-3393. The examiner can normally be reached on 9 AM TO 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JASON CHIANG/Primary Examiner, Art Unit 2431 Application/Control Number: 18/533,722 Page 2 Art Unit: 2431 Application/Control Number: 18/533,722 Page 3 Art Unit: 2431 Application/Control Number: 18/533,722 Page 4 Art Unit: 2431 Application/Control Number: 18/533,722 Page 5 Art Unit: 2431 Application/Control Number: 18/533,722 Page 6 Art Unit: 2431 Application/Control Number: 18/533,722 Page 7 Art Unit: 2431 Application/Control Number: 18/533,722 Page 8 Art Unit: 2431 Application/Control Number: 18/533,722 Page 9 Art Unit: 2431 Application/Control Number: 18/533,722 Page 10 Art Unit: 2431 Application/Control Number: 18/533,722 Page 11 Art Unit: 2431
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