DETAILED ACTION
1.
This is in reply to an application filed on 06/10/2025. Claims 1-20 are pending examination.
2.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
3.
Claims 2-7, 9, 12-17 and 19 are not rejected under 103.
4.
Claim Objection
Claims 11 and 19 are objected to, because these claims have typographical errors. The examiner suggests the following correction:
Claim 11:
Replacement of “the source image” with “the original face image”.
Claim 19:
Replacement of “wherein the robust correspondence module is configured to feeding, by the robust correspondence module, the original face image” with “wherein the robust correspondence module is configured to feeding the original face image”.
5.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101. Claims 1-20 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of copending Application No. 19460994 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
6.
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 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)(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.
Claims 1, 8, 10-11, 18 and 20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Khodadadeh et al. US 2024/0143835 (hereinafter Khodadadeh).
Regarding claim 1 Khodadadeh teaches a system to protect user facial privacy against unknown face recognition levels, comprising:
an input source to input an original face image; a training circuit configured to train a generator model to output an image that resembles the original face image (Khodadadeh teaches utilizing a digital image and a trained neural network to generate anonymization digital image [0042], [0053] and fig. 2-3);
an optimizer configured to generate a protected face image based on the trained model that fools a black-box face recognition model, while imitating a makeup style (Khodadadeh teaches generating the anonymization digital image based on a plurality of face attributes, wherein the generated image may look as a real digital image, which may fool the discriminator neural network [0043-0044], wherein the face attributes may include eye makeup and lips makeup [0059]); and
a display device to display the protected face image online (Khodadadeh teaches the server may transmit data to the client device to cause the client device to display or present an anonymized digital image based on the client device interaction [0033]).
Regarding claim 8 Khodadadeh teaches the system of claim 1, wherein the training circuit includes a robust correspondence module adversarially transfer makeup from a reference image to the original face image Khodadadeh teaches generating the anonymization digital image based on a plurality of face attributes, wherein the generated image may look as a real digital image, which may fool the discriminator neural network [0043-0044], wherein the face attributes may include eye makeup and lips makeup [0059], and wherein generating accurate anonymized digital images for faces in non-frontal poses, the image anonymization system modifies a training dataset for more robust training of the face anonymization neural network [0093]),
wherein the optimizer includes a randomly initialized conditional decoder with Adaptive Makeup Conditioning (AMC) layers, and optimize parameters of the decoder at test-time to generate the protected face image (Khodadadeh teaches features include latent features (e.g., features within the various layers of a neural network and that may change as they are passed from layer to layer) and/or unobservable deep features [0045], wherein the face attributes may include eye makeup and lips makeup [0059]. A mapper is an encoder that maps a random noise vector into a latent space for co-modulating with the masked image vector and/or the face anonymization guide, wherein the image anonymization system utilizes the style vector to guide the synthesizer (i.e decoder) to generate the anonymized digital image as a realistic digital image with another identity that has attributes similar to the sample digital image [0021], [0082]. The image anonymization system learns parameters for the synthesizer such that custom-character is a realistic digital image that is an anonymized version of the sample digital image [0089]).
Regarding claim 10 Khodadadeh teaches the system of claim 8, wherein the decoder is fine-tuned using structured, makeup, and adversarial losses to effectively protect facial privacy (Khodadadeh teaches a generative adversarial neural network (“GAN”) refers to a neural network that is tuned or trained via an adversarial process to generate an output digital image from an input such as a noise vector [0041-0042]. The image anonymization system determines a perceptual loss in addition to an adversarial loss associated with one or more components of the face anonymization neural network [0083-0086]. Generating the anonymization digital image based on a plurality of face attributes, wherein the generated image may look as a real digital image, which may fool the discriminator neural network [0043-0044], wherein the face attributes may include eye makeup and lips makeup [0059].
In response to Claim 11: Rejected for the same reason as claim 1
In response to Claim 18: Rejected for the same reason as claim 8
In response to Claim 20: Rejected for the same reason as claim 10
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYOUB ALATA whose telephone number is (313)446-6541. The examiner can normally be reached on Monday - Friday 7:30 - 5:00 Est.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jung (Jay) Kim can be reached on (571)272-3804. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/AYOUB ALATA/Primary Examiner, Art Unit 2494