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 for all Patent Application as subject to AIA
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.
RESPONSE TO REQUEST FOR CONTINUED EXAMINATION (RCE)
Amended claims 1-2, 4-18, and 20 are pending and remain for further examination.
The New Grounds of Rejection
Applicant’s amendments and arguments with respect to claims 1-2, 4-18, and 20 and request for continued examination (RCE) filed on February 26, 2026 have been fully considered, but they are not deemed to be moot in view of the new grounds of rejections. Applicant’s amendment necessitated the new modified rejections.
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.
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 1-2, 4-18, and 20 are rejected under AIA 35 U.S.C. 103 as being un-patentable over Saito et al (U.S. Patent Application Publication No. 2024/0153326 A1) in view of Choi (U.S. Patent Application Publication 2023/0076017 A1).
As to claim 1, Saito et al disclose an access control management system, configured to control opening of a gate, or entry and exit of an entrance (figure 3, pars. 0071-0072, reference disclose an entry control system), the access control management system comprising: an image capture device, disposed at the gate or the entrance, configured to capture a face image of a user to be identified (figure 3, par. 0073, the entry control system including an image-capturing device); a display, disposed at the gate or the entrance (see figure 3); and a processing device, coupled to the image capture device and the display, and configured to verify an identity of the user, and control the opening of the gate or the entry and exit of the entrance according to a verification result, wherein the first deep learning model identities of a plurality of users registered in advance (figure 2, pars. 0061-0064, figure 3, pars. 0071-0074, figure 4, pars. 0087-0088, figures 5-6, pars. 0095-0097, authentication/unlocking control units verify the user and control the operations of the gate, and register unit identify previously registered users).
However, Saito et al do not teach that de-identify the face image to obtain de-identified image data, and convert the de-identified image data into a plurality of de-identified features for subsequent output; and a processing device configured to display the de-identified image data on the display without storing the face image, verify an identity of the user to which the de-identified features belong by a trained first deep learning model.
Choi teaches that de-identify the face image to obtain de-identified image data, and convert the de-identified image data into a plurality of de-identified features for subsequent output (figures 2-5, pars. 0058-0084, obtaining de-identified image data and generating object information); and a processing device configured to display the de-identified image data on the display without storing the face image, verify an identity of the user to which the de-identified features belong by a trained first deep learning model (figures 7-8, pars. 0115-0123, displaying/providing de-identified image data and object information without storing original face image to train/control a training unit).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teaching of Choi as stated above with the access control management system of Saito et al for displaying/providing de-identified image data and object information without storing original face image to train/control a training unit because it would have improved security of user profile and network data and also improved throughput, bottlenecks, and system utilization by utilizing proper control/authentication devices more efficiently.
As to claim 2, Choi discloses that the image capture device comprises: a lens; an image sensor, configured to sense light intensity 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 (figure 1, pars. 0037-0046, the image capture device captures the face image), de-identify the face image to obtain the de-identified image data, and convert the de-identified image data into the plurality of de-identified features; and an input/output (I/O) interface, configured to output the de-identified features (figures 2-5, pars. 0058-0084, obtaining de-identified image data and generating object information).
As to claim 4, Saito et al disclose that the processing device further comprises a first communication device configured to communicate with the image capture device or connect to a network; and the image capture device further comprises a second communication device configured to communicate with the first communication device or connect to the network (figure 3).
As to claim 5, Saito et al disclose that an interface device, configured to connect the image capture device and the processing device (figure 3, pars. 0072-0074, figure 4, pars. 0089-0091).
As to claim 6, Choi discloses that the first deep learning model is implemented by an application programming interface (API) attached to a processor of the processing device (figure 2, pars. 0064-0067).
As to claims 7-8, Choi discloses that the image signal processor comprises de-identifying the face image by a second deep learning model supporting privacy protection technology, wherein the second deep learning model comprises a plurality of neurons divided into a plurality of layers, the image signal processor converts the face image into feature values of a plurality of neurons in a first layer among the layers, inputs the converted feature values of each of the neurons to a next layer after adding noise generated by using a privacy parameter, and obtains the de-identified image data after processing the layers (figures 2-5, pars. 0058-0084, obtaining de-identified image data and generating object information).
As to claims 9-10, Choi discloses that the first deep learning model comprises calculating a similarity between the de-identified features and a feature space established using the de-identified features of each of the users registered in advance, to verify the identity of the user to which the de-identified features belong according to the calculated similarity; and the image capture device is further configured to identify a living body in the face image by a living body recognition technology, and de-identify the face image when the living body is identified in the face image, wherein the living body recognition technology comprises blink detection, deep learning features, challenge-response technology, or a three-dimensional camera (figure 1, pars. 0049-0056, figures 7-8, pars. 0090-0111, using de-identification unit to verify the identity of the user and using training unit to generate object information from the de-identified image data).
As to claims 11-16, they are also rejected for the same reasons set forth to rejecting claims 1-2 and 4-10 above, since claims 11-16 do not teach or define any new limitations than above rejected claims 1-2 and 4-10.
As to claims 17-18 and 20, they are also rejected for the same reasons set forth to rejecting claims 1 and 7-8 above, since claims 17-18 and 20 do not teach or define any new limitations than above rejected claims 1 and 7-8.
Response to Arguments
Applicant’s arguments with respect to the claims 1-2, 4-18, and 20 filed on February 26, 2026 have been fully considered but they are deemed to be moot in a new ground(s) of rejection is made in view of new references. The examiner has attempted to answer (response) to the remarks (arguments) in the body of the Office Action (see modified rejection of claims 1-10).
Additional Reference
The examiner as of general interest cites the following reference.
Kokaji et al, U.S. Patent Application Publication No. 2025/0299223 A1.
Content Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bharat Barot whose telephone number is (571)272-3979. The examiner can normally be reached on 7:00AM-3:30PM.
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/BHARAT BAROT/Primary Examiner, Art Unit 2453April 17, 2026
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