DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/03/2026 has been entered.
3. In the applicant’s submission, claims 1 and 11 were amended. Accordingly, claims 1-20 are pending and being examined. Claims 1 and 11 are independent form.
Claim Rejections - 35 USC § 112
4. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
5. Claims 1-20 are rejected under 35 U.S.C. 112(a), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
5-1. Regarding claim 1, the claim recites: “transform the de-identified image data into a plurality of de-identified features, and output the plurality of de-identified features without outputting the face image” in lines 4-6. However, support for this newly added feature is not found throughout applicant’s originally filed specification. Rather, as shown in fig.6A and disclosed in para.66, wherein a real face image 132a of the user is displayed/outputted on the display 130. In other words, the specification does not describe outputting the plurality of de-identified features without outputting the face image as recited by claim 1. Thus, the claim is rejected under 35 U.S.C. 112(a) because the newly added feature/matter is not properly described in the specification as originally filed. See MPEP 608.04.
5-2. Regarding independent claim 11, the claim faces the same issue set forth in the rejection of independent claim 1, and thus, is rejected as failing to comply with the written description requirement under 35 U.S.C. 112(a).
5-3. The dependent claims 2-10, and 12-20 have be viewed individually. These additional elements do not limit the claimed inventions as complying with the written description requirement, therefore, are rejected as failing to comply with the written description requirement under 35 U.S.C. 112(a).
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. Claims 1-5, 8, 10-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (US 2023/0076017, hereinafter “Choi”) in view of Gross et al (“Model-Based Face De-Identification”, 2006, hereinafter “Gross”).
Regarding claim 1, Choi discloses a face recognition system (the object detection system in a neural network training method; see figs.1-8 and abstract), comprising:
an image capturing device comprising a lens, an image sensor, an image signal processor, and an input-output interface (see the user terminal 10 of the system in fig.1), configured to capture a face image of a user to be recognized, see face image 20 and de-identification face image 22 in fig.2; see para.59: “The de-identification unit 110 may de-identify the face image captured by and transmitted from the user terminal 10.”), transform the de-identified image data into a plurality of de-identified features, and output the plurality of de-identified features (see object/face information 30 in fig.2 and para.65: “The training unit 120 may train the neural network which extracts the object information 30 by using the de-identified image received from the de-identification unit 110.” It should be noticed that “the object information 30” is “de-identified features” extracted/transformed from the de-identification face image 22 and outputted from the training NN 120); and
a processing device comprising a processor, configured to receive the plurality of de-identified features output by the image capturing device (see para.40: “the service operation server 100 [of the system] may perform the user authentication by using a neural network [120 shown in fig.2] and perform a faster and more accurate user verification procedure”),
As explained above, the mere differences are, [1] Choi does not explicitly disclose “locally de-identify[ing] the face image”; and [2] Choi does not explicitly disclose “a processing device comprising a processor, configured to [...], verify an identity of the user to which the de-identified features belong by a trained first machine learning model, wherein the first machine learning model is trained by using de-identified features and identities of a plurality of users registered in advance” as recited by the claim. However, in the same field of endeavor, that is, in the field for protecting the privacy of subjects in machine-learning based face recognition, Gross, see Abstract, suggests: [1] images captured by image capturing devices need to “protect the privacy of subjects visible” in the images before being shared. For this reason, Gross discloses an “automated method to de-identify the images”. Gross, see Figure 1, “Definition 3.2 (Image Sets)” and “Definition 3.3 (Face Recognition)”, teaches: [2] a face recognition process which may compare a de-identified probe image output from the trained “De-Identification” network with a known gallery face set to find “the index [i.e., the identity] of the subject most like to correspond to the subject seen in the probe image”. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Gross into the teachings of Choi and directly/locally utilize the combined techniques to image capturing devices. Suggestion or motivation for doing so would have been to “protect the privacy of subjects visible in the scene” before being shared as taught by Gross, see Abstract. Therefore, the claim is unpatentable over Choi in view of Gross.
Regarding claim 2, the combination of Choi and Gross discloses the face recognition system according to claim 1, wherein the image capturing device comprises: a lens; an image sensor , configured to sense an intensity of light passing through the lens to generate an image of a photographed object; an image signal processor, configured to capture the face image in the image (Choi, see the face image 20 captured by the user terminal smart phone 10 in fig.1; see para.37-38), de-identify the face image to obtain the de-identified image data, and transform the de-identified image data into the plurality of de-identified features (Choi, see the de-identification face image 22 in fig.2); and an input-output interface, configured to output the plurality of de-identified features (Choi, see the input face image 20 and the output de-identification face image 22 in fig.2).
Regarding claim 3, 12, the combination of Choi and Gross discloses, wherein the image capturing device uses a second machine learning model that supports privacy protection technology to de-identify on the face image (Choi, see the de-identification unit 110 in fig.2, wherein the de-identification unit 110 is a neural network including encoder 112 and a decoder 114; wherein he converted image 22 may be used as input data of a neural network model and may thus be information making it impossible to recognize the identity with the naked eyes and represented in a more efficient format for the neural network model to extract the object information 30; see para.59--63).
Regarding claim 4, 13, the combination of Choi and Gross discloses, wherein the second machine learning model comprises a plurality of neurons divided into a plurality of layers, transforms the face image into feature values of the plurality of neurons of a first layer in the plurality of layers, adds the transformed feature values of each neuron to noise generated by using privacy parameters for inputting to a next layer, and obtains the de-identified image data after a multi-layer processing (Choi, see the encoder network 112 shown by fig.5 and decoder network 114 shown by fig.5; see para.108: “the third image 22 changed to the q-th dimensions may be decoded from the second image 21 and may have different data values from the original first image 20”. It should be noticed that the third image (i.e., the de-identified face image) 22 changed to the q-th dimensions is a noised (and compressed) face image which cannot be identified by naked eyes).
Regarding claim 5, 14, the combination of Choi and Gross discloses, wherein the privacy protection technology comprises differential privacy, homomorphic encryption, shuffle, or pixelate, etc (Choi, see the third image (i.e., the de-identified face image) 22 shown by fig.5).
Regarding claim 8, 20, the combination of Choi and Gross discloses, wherein the processing device further processes the face image by image masking or face changing and outputs the processed face image by an input-output interface of the image capturing device (Choi, changing the face image 20 to the face image 22 as shown in fig.2).
Regarding claims 10, 11, each of which is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
9. Claims 6, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Choi in view of Gross and further in view of Newton et al (“Preserving Privacy by De-Identifying Face Images”, 2005, hereinafter “Newton”).
Regarding claims 6, 15, the combination of Choi and Gross discloses the claimed inventions except for calculating a similarity between the de-identified features and a feature space established by using the de-identified features of each user registered in advance. However, in the same field of endeavor, Newton teaches a loss matric (which is directly related to a similarity matric, as stated by claim 16) defined by the differences between a de-identified face image and its corresponding normalized and registered face image for face recognition after de-identifying face images to protect the privacy of a user by minimizing the loss (i.e., by maximizing the similarity). See Eq.(2) and Sec. 2, wherein the normalized face images (
Γ
1
,
Γ
2
) and the de-identified face images (
Γ
a
,
Γ
b
) are shown by fig.2. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Newton into the teachings of the combination of Choi and Gross and employ a loss matric defined by the differences between a de-identified face image and its corresponding normalized and registered face image for face recognition after de-identifying face images to find “minimally distance clusters” which represent “person-specific faces” and provide “privacy protection” taught by Newton. Suggestion or motivation for doing so would have been to find “minimally distance clusters” which represent “person-specific faces” and provide “privacy protection” as taught by Newton, see the last 3 paragraphs, on right col., page. 235. Therefore, the claim is unpatentable over Choi in view of Gross and further in view of Newton.
Regarding claim 16, the combination of Choi, Gross and Newton discloses wherein the feature space is obtained by an embedded space or a loss function (Newton, see Eq(2)), which comprises optimizing a margin of a geodesic distance by normalizing a corresponding relationship between angles and radians in a hypersphere (Newton, see Sec. 2, para.2: “In face recognition, faces are detected in the raw video image to provide face stills (Definition 2.1) and a “registration” process performed to provide a face image (Definition 2.2). A face still is normalized, rotated, and cropped, as needed, to provide a face image. The goal is to adjust for pose and make the location of eyes and their interpupil distance align in roughly the same position in each image, thereby making face images somewhat comparable to one another.”).
10. Claims 7, 9, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Choi in view of Gross and further in view of Yennapureddy et al (US 20240022601, hereinafter “Yennapureddy”).
Regarding claims 7, 17, 18, although the combination of Choi and Gross does not explicitly disclose the recited features, these features are well known and widely used in the field of face authentication. As evidence, Yennapureddy teaches a method that uses depth sensors to capture “image data and depth data” of a nefarious actor wearing 3D mask for determining whether the spoofing attacks occur (i.e., detecting the liveness of a user). See para.5, and para.3. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Yennapureddy into the teachings of the combination of Choi and Gross and employ a depth sensor to capture the depth data of a user to determine whether a spoofing attack is occurring taught by Yennapureddy. Suggestion or motivation for doing so would have been to “determine whether a spoofing attack is occurring” as taught by Yennapureddy, see the Title and Abstract Therefore, the claim is unpatentable over Choi in view of Gross and further in view of Yennapureddy.
Regarding claim 9, 19, the combination of Choi, Gross and Yennapureddy discloses, wherein the first machine learning model is implemented by an application programming interface (API) attached to a processor of the processing device (Yennapureddy, see para.235: “In embodiments, one or more of the client devices may use the web-based service software or applications (e.g., by accessing the service software and/or applications via one or more application programming interfaces (APIs)).”).
Response to Arguments
11. Applicant’s arguments, with respects to claims 1 and 11, filed on 04/03/2026, have been fully considered but they are not persuasive.
On page 7 of applicant’s response, the applicant submits:
“In other words, in Choi, the user terminal 10 directly transmits the face image 20 to the server 100 without de-identification, and the server 100 then de-identifies the face image 20 to become the de-identification face image 22...
...
As a result, the applicant argues that the amended technical features set forth in claims 1 and 11 are not taught or suggested by Choi...”.
The examiner respectfully disagrees with the argument. As explained in the rejections of the claims, although Choi does not explicitly disclose “locally de-identify[ing] the face image”, the system shown by fig.1 in Choi is a single service system including a camara 10 and a server 100. In other words, from one single service system point of view, the components in the system in fig.1 can be recognized connected locally to fulfil the task of the system. Therefore, Choi implicitly discloses the argued feature. In addition, in the same field of endeavor, Gross, see Abstract, states “Advances in camera and computing equipment hardware in recent years have made it increasingly simple to capture and store extensive amounts of video data. This, among other things, creates ample opportunities for the sharing of video sequences. In order to protect the privacy of subjects visible in the scene, automated methods to de-identify the images, particularly the face region, are necessary.” In other words, Gross has recognized, images captured by a camera need to remove the face regions from the images before being shared in public. For this, Gross provides an “automatic method to de-identify the images” captured by a camera. In other words, Gross suggests locally de-identification by imaging device self. Therefore, the combination of Choi and Gross discloses or suggests each and every feature recited by claim 1. The applicant’s argument is not persuasive.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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