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 Applicants
This communication is in response to the Application filed on 9/3/2024.
Claims 2-21 are newly added and pending. Claim 1 is canceled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/10/2024, 12/18/2025 and 2/27/2026 has been considered by the examiner.
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 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3 and 5-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0191104A1 to Yoo et al. (hereafter, “Yoo”) in view of US 10,902,543B2 to Kakkirala et al. (hereafter, “Kakkirala”).
With regard to claim 2 Yoo discloses a system comprising: one or more memories configured to store a generator neural network and a classifier neural network (paragraph [0023], The memory 106 can store data 108 and instructions 110 which are executed by the processor 104 to cause the first computing device 102 to perform operations); and a processor (paragraphs [0023, 0031], one or more processors 104) coupled to the one or more memories (paragraphs [0023, 0031], a memory 106) and configured to read instructions from the one or more memories to cause the instructions to perform operations comprising: determining a latent vector from a multi-variable gaussian distribution (paragraph [0025], the machine-learned message embedding model 116 can receive a difference image describing a difference between an input image and an encoded first output image and generate a message vector (e.g., a latent space vector) representing the difference image’ paragraph [0043], message vector 212 (e.g., a latent space vector) can be generated by a machine-learned message embedding model based at least in part on the difference image 208; paragraphs [0016 and 0082]); generating, using the generator neural network, a watermark from the latent vector (paragraph [0044-0046, 0048], a watermark (e.g., image noise) representing the message vector 212 can be generated and added to the input image 202 to obtain the second output image 216; the machine-learned message embedding model (e.g., the machine-learned watermark generation model 214) can take both the message vector 212 and the input image 202 as inputs to generate the second output image 216 embedded with the message. Paragraphs [0061-0078]); and classifying, using the classifier neural network, [the watermark into a classification label] from classification labels (paragraphs [0049 and 0080]).
However, Yoo does not expressly teach having watermark into a classification label. Kakkirala teaches classifying the watermark into a classification label (col. 3 lines 2-5; 58-63; Fig. 4; col. 10 lines 32-36; lines 55 to col. 11 line 15; obtain, based upon the one or more non-classified watermarked images, one or more classified watermarked images).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Yoo’s reference to have classification label of Kakkirala’s reference. The suggestion/motivation for doing so would have been to implement robust techniques or methodologies that properly classify the input images to be scanned by the network as tampered or non-tampered for detecting even a weakest of tampering in the watermarked images, as suggested by Kakkirala at col. 6 lines 25-33.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Kakkirala with Yoo to obtain the invention as specified in claim 2.
With regard to claim 3 Yoo in combination with Kakkirala discloses wherein to classify the watermark the operations further comprise: receiving the watermark at the classifier neural network, wherein the classifier neural network includes convolutional layers and fully connected layers; generating, using the convolutional layers of the classifier neural network, a feature map from the watermark; and classifying, using the fully connected layers of the classifier neural network, the feature map into the classification label (Kakkirala: col. 7 lines 11-45; col. 8 lines 36-47).
With regard to claim 5 Yoo in combination with Kakkirala discloses [wherein the classification label is a class identifier that corresponds to] [the latent vector]. Yoo discloses latent vector (paragraphs [0025, 0043, 0050, 0060, 0068]). Kakkirala teaches wherein the classification label is a class identifier (col. 3 lines 2-5; 58-63; Fig. 4; col. 10 lines 32-36; lines 55 to col. 11 line 15; obtain, based upon the one or more non-classified watermarked images, one or more classified watermarked images). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Yoo’s reference to have classification label of Kakkirala’s reference that related to latent vector of Yoo. The suggestion/motivation for doing so would have been to implement robust techniques or methodologies that properly classify the input images to be scanned by the network as tampered or non-tampered for detecting even a weakest of tampering in the watermarked images, as suggested by Kakkirala at col. 6 lines 25-33.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kakkirala with Yoo to obtain the invention as specified in claim 5.
With regard to claim 6 Yoo in combination with Kakkirala discloses wherein to generate the watermark the operations further comprise: passing the latent vector through deconvolutional layers of the generator neural network, wherein each of the deconvolutional layers comprises different dimensions (Yoo: paragraphs [0041-0042, 0046, 0048, 0051, 0057, 0059, 0066-0068).
With regard to claim 7 Yoo in combination with Kakkirala discloses wherein the operations further comprise incorporating the watermark into an image (Yoo: paragraphs 0015-0018, 0021-0022, 0025-0028, and so on throughout the reference).
With regard to claim 8 Yoo in combination with Kakkirala discloses wherein the operations further comprise: receiving, at the classifier neural network, a data sample from a dataset, wherein the dataset does not include watermarks; and classifying, using the classifier neural network, the data sample into a second classification label from the classification labels (Yoo as disclosed throughout the reference and illustrated in Fig. 2 for example, the input image (i.e., dataset) is received without the watermark; 402 in Fig. 4, paragraphs [0054-0056]).
With regard to claim 9 Yoo in combination with Kakkirala discloses wherein the operations further comprise: modifying weights of one of the convolutional layers or the fully connected layers of the classifier neural network based on the classification label or the second classification label (Kakkirala: col. 7 lines 22-31; col. 8 lines 23-35).
With regard to claim 10 Yoo in combination with Kakkirala discloses wherein the operations further comprise: modifying weights of one of deconvolution layers of the generator neural network based on the classification label or the second classification label (Kakkirala: col. 7 lines 22-31; col. 8 lines 23-35).
With regard to claims 11 and 19, claims 11 and 19 are rejected same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to claims 11 and 19, and all of the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
With regard to claims 12 and 20, claims 12 and 20 are rejected same as claim 3 and the arguments similar to that presented above for claim 3 are equally applicable to claims 12 and 20, and all of the other limitations similar to claim 3 are not repeated herein, but incorporated by reference.
With regard to claim 13, claim 13 is rejected same as claim 5 and the arguments similar to that presented above for claim 5 are equally applicable to claim 13, and all of the other limitations similar to claim 5 are not repeated herein, but incorporated by reference.
With regard to claim 14, claim 14 is rejected same as claim 6 and the arguments similar to that presented above for claim 6 are equally applicable to claim 14, and all of the other limitations similar to claim 6 are not repeated herein, but incorporated by reference.
With regard to claim 15, claim 15 is rejected same as claim 7 and the arguments similar to that presented above for claim 7 are equally applicable to claim 15, and all of the other limitations similar to claim 7 are not repeated herein, but incorporated by reference.
With regard to claim 16, claim 16 is rejected same as claim 8 and the arguments similar to that presented above for claim 8 are equally applicable to claim 16, and all of the other limitations similar to claim 8 are not repeated herein, but incorporated by reference.
With regard to claims 17 and 21, claims 17 and 21 are rejected same as claim 9 and the arguments similar to that presented above for claim 9 are equally applicable to claims 17 and 21, and all of the other limitations similar to claim 9 are not repeated herein, but incorporated by reference.
With regard to claim 18, claim 18 is rejected same as claim 10 and the arguments similar to that presented above for claim 10 are equally applicable to claim 18, and all of the other limitations similar to claim 10 are not repeated herein, but incorporated by reference.
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The cited art of record fails to teach, disclose or suggest the limitations/features “wherein to determine the latent vector the operations further comprise: determining the latent vector from the multi-variable gaussian distribution in a set of non-overlapping multi-variable gaussian distributions, wherein multi-variable gaussian distributions correspond to watermarks”, recited in claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US2020/0395117 discloses adaptive image processing, image analysis, pattern recognition, and time-to-event prediction in various imaging modalities associated with assisted reproductive technology. The reference image may be processed according to one or more adaptive processing frameworks for de-speckling or noise processing of ultrasound images. The subject image is processed according to various computer vision techniques for object detection, recognition, annotation, segmentation, and classification of reproductive anatomy, such as follicles, ovaries and the uterus. An image processing framework may also analyze secondary data along with subject image data to analyze time-to-event progression of the subject image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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