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 .
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-6 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amonkar et al. (IDS: US 20230252173 A1) in view of Hong et al. (“Partial Encryption of Digital Contents Using Face Detection Algorithm”).
Regarding claims 1 and 19, Amonkar et al. disclose an apparatus for image processing, the apparatus comprising: a memory; and a processor coupled to the memory (processor, server, [0083]) and configured to and method for image processing, the method comprising: obtain encryption mode information associated with an image, wherein the encryption mode information comprises at least one of a region of interest (ROI) of the image or a task associated with the image (Systems and methods are provided for detecting an object region in an image and encrypting/decrypting a detected object region, The data analytics system further comprise a privacy processing component that is configured to selectively encrypt the detected object using an encryption key following the advanced encryption standard with cipher block chaining mode (AES-CBC), abstract, In some embodiments, the object detection component may detect an object region by using a multi-head self-supervised learning neural network-based classifier techniques and distinguish the object from the target object to be searched and designed the detected object region to be encrypted, [0010], The privacy processing component comprises an AES key generator, an AES encryption block, and a Rivest-Shamir-Adleman (RSA) key-pair module. The AES key generator is configured to generate a random AES key. The AES encryption block takes an image with the detected object and the AES key and then produces an encrypted image output. The RSA key-pair module is configured to generate a public key that is used to encrypt the AES key. In another embodiment, the encrypted data is transmitted and stored in a large scale distributed database server so that even in the incidents like thievery or hacking individual privacy would not be compromised. Still in another embodiment, the encrypted AES key is transmitted and stored in a key store database with limited access, [0018], In some embodiment, when the object detection component designates a plurality of detected object regions to be encrypted, the privacy processing component may encrypt the image of the respective areas of the plurality of designated object regions to be encrypted individually using a plurality of encryption keys, [0020], designating the human object region with pre-defined features to be encrypted, generating, via an advanced encryption standard (AES) key generator, an AES key, [0030]-[0031], The AES key generator 401 is configured to generate a random AES key. The AES encryption block 402 takes an image with the detected object and the AES key, [0110]); encrypt at least a portion of image based on the encryption mode information to produce an encrypted image (produces an encrypted image output, [0018], The image of each designated object region to be encrypted is encrypted using a separate encryption key, thereby increasing the strength of security. Still in another embodiment, the privacy processing component may encrypt the image of the respective areas of the plurality of designated object regions to be encrypted all at once using one encryption key, [0020], produces 256-bits of encrypted image output, [0110]); and output the encrypted image for transmission (outputting an encrypted image and storing the encrypted image in a large scale distributed database server, [0034], [0048], The encrypted data is transmitted and stored in a large scale distributed database server so that even in the incidents like thievery or hacking individual privacy would not be compromised, [0110]).
While it would be obvious an object region is generally an image’s region of interest, another reference is added to make this more explicit.
Hong et al. teach obtain encryption mode information associated with an image, wherein the encryption mode information comprises at least one of a region of interest (ROI) of the image or a task associated with the image and encrypt at least a portion of image based on the encryption mode information to produce an encrypted image (In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, abstract, “In this paper, we proposed the partial encryption method that uses the face region as the feature so that we encrypt only part of the video frame. The face region is the most important region for the human in the digital video. Therefore our proposed method can reduce the encryption time and can increase the protection strength by adding the location of the face region as the second key” “The reason that we use the face region to encrypt the digital contents is that the face region is the most important part for the digital video contents;
actors show their feelings and atmospheres of the contents via their faces” “To encrypt the video contents, the proposed system uses the face region, which is one of the digital video contents’ features. For the more exact face detection, we use the MLP (Multi-Layer Perceptrons) to make a texture classifier, which discriminates between face pixels and non-face ones[6, 7], and the Gaussian skin-color model, discriminates between skin regions and non-skin regions[8]”, p634, In this paper, because we encrypt the contents using the block encryption algorithms such as the DES (Data Encryption Standard) [9] and the AES (Advanced Encryption
Standard) [10], we make a rectangle of the face region and use the x and y location of the top-left and the bottom-right position of the detected face region, The encryption process consists of three steps as follows(Fig. 4(a)): 1. the face regions detection. 2. the detected region encryption in the content with the key. 3. the location information hiding of the encrypted region p637, “When an image is inputted this system, we detect the feature region from the
input image (Fig. 5(a, b)). The feature region is the important part such as the actor’s
face, text, objects, etc. And we use the face region as the feature region in the proposed
system. For the face detection, we use the MLP that this system learn for detecting face regions, and apply the skin-color model to the image. Through the AND operation between the results of the MLP and that of the skin-color model, we can detect face regions more exactly. After the face detection, we encrypt the feature region using the location of detected face regions(Fig. 5(c))”, p638).
Amonkar et al. and Hong et al. are in the same art of encryption (Amonkar et al., abstract; Hong et al., abstract). The combination of Hong et al. with Amonkar et al. will enable using a region of interest. It would have been obvious at the time of filing to combine the ROI of Hong et al. with the invention of Amonkar et al. as this was known at the time of filing, the combination would have predictable results, and as Hong et al. state “Therefore our proposed
method can reduce the encryption time and can increase the protection strength by
adding the location of the face region as the second key” (p634) providing a computational time benefit to combining inventions.
Regarding claims 2 and 20, Amonkar et al. and Hong et al. disclose the apparatus and method of claims 1 and 19. Amonkar et al. and Hong et al. further indicate the processor is configured to process the image based on an encryption mode associated with the encryption mode information (Amonkar et al., when the object detection component designates a plurality of detected object regions to be encrypted, the privacy processing component may encrypt the image of the respective areas of the plurality of designated object regions to be encrypted individually using a plurality of encryption keys, [0020], designating the human object region with pre-defined features to be encrypted, generating, via an advanced encryption standard (AES) key generator, an AES key, [0030]-[0031], “The data analytics system is configured to detect an object region with specific features to be encrypted and encrypt the object region with an AES key”, [0111]; Hong et al., In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, abstract, because we encrypt the contents using the block encryption algorithms such as the DES (Data Encryption Standard) [9] and the AES (Advanced Encryption Standard) [10], we make a rectangle of the face region and use the x and y location of the top-left and the bottom-right position of the detected face region, The encryption process consists of three steps as follows(Fig. 4(a)): 1. the face regions detection. 2. the detected region encryption in the content with the key. 3. the location information hiding of the encrypted region, p637).
Regarding claim 3, Amonkar et al. and Hong et al. disclose the apparatus of claim 2. Amonkar et al. and Hong et al. further indicate to process the image based on the encryption mode, the processor is configured to determine at least the portion of the image based on at least one of the ROI or the processor performing the task associated with the image (Amonkar et al., object detection component for detecting an object region, abstract, [0009], [0111]; Hong et al., In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, abstract, “In this paper, we proposed the partial encryption method that uses the face region as the feature so that we encrypt only part of the video frame. The face region is the most important region for the human in the digital video. Therefore our proposed method can reduce the encryption time and can increase the protection strength by adding the location of the face region as the second key” “The reason that we use the face region to encrypt the digital contents is that the face region is the most important part for the digital video contents;actors show their feelings and atmospheres of the contents via their faces” “To encrypt the video contents, the proposed system uses the face region, which is one of the digital video contents’ features. For the more exact face detection, we use the MLP (Multi-Layer Perceptrons) to make a texture classifier, which discriminates between face pixels and non-face ones[6, 7], and the Gaussian skin-color model, discriminates between skin regions and non-skin regions[8]”, p634, In this paper, because we encrypt the contents using the block encryption algorithms such as the DES (Data Encryption Standard) [9] and the AES (Advanced Encryption Standard) [10], we make a rectangle of the face region and use the x and y location of the top-left and the bottom-right position of the detected face region, The encryption process consists of three steps as follows(Fig. 4(a)): 1. the face regions detection. 2. the detected region encryption in the content with the key. 3. the location information hiding of the encrypted region p637, “When an image is inputted this system, we detect the feature region from the input image (Fig. 5(a, b)). The feature region is the important part such as the actor’s
face, text, objects, etc. And we use the face region as the feature region in the proposed
system. For the face detection, we use the MLP that this system learn for detecting face regions, and apply the skin-color model to the image. Through the AND operation between the results of the MLP and that of the skin-color model, we can detect face regions more exactly. After the face detection, we encrypt the feature region using the location of detected face regions(Fig. 5(c))”, p638).
Regarding claim 4, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. and Hong et al. further indicate the ROI corresponds to at least one of the task associated with the image, an attention region, or a threat (In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, abstract, “In this paper, we proposed the partial encryption method that uses the face region as the feature so that we encrypt only part of the video frame. The face region is the most important region for the human in the digital video. Therefore our proposed method can reduce the encryption time and can increase the protection strength by adding the location of the face region as the second key” “To encrypt the video contents, the proposed system uses the face region, which is one of the digital video contents’ features. For the more exact face detection, we use the MLP (Multi-Layer Perceptrons) to make a texture classifier, which discriminates between face pixels and non-face ones[6, 7], and the Gaussian skin-color model, discriminates between skin regions and non-skin regions[8]”, p634) [semantic/important/face region interpreted as attention region].
Regarding claim 5, Amonkar et al. and Hong et al. disclose the apparatus of claim 2. Amonkar et al. and Hong et al. further indicate the task associated with the image comprises at least one of semantic segmentation, object detection, depth estimation, optical flow, map generation, or attack algorithm performance (Amonkar et al., object detection component for detecting an object region, abstract, [0009], [0111]; Hong et al., In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video
contents, abstract, In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, p634, we make a rectangle of the face region and use the x and y location of the top-left and the bottom-right position of the detected face region, p637).
Regarding claim 6, Amonkar et al. and Hong et al. disclose the apparatus of claim 5. Amonkar et al. further indicate to perform the map generation task, the processor is configured to generate of at least one of a class activation map, a saliency map, an attention map using an attention network, or a depth estimation map (frames are manually annotated with more than 2.6 million bounding boxes or points of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles, [0080], feature map of each channel includes activations that record the semantic information for the respective regions in the image, [0101]).
Regarding claim 15, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Amonkar et al. further indicate the processor is configured to obtain the image from at least one of a camera sensor, a radar sensor, or a light detection and ranging (LIDAR) sensor ([0004], [0080], [0085]).
Regarding claim 16, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Hong et al. further indicate the image comprises a set of pixels, and wherein at least the portion of the image comprises a subset of pixels from the set of pixels (“To encrypt the video contents, the proposed system uses the face region, which is one of the digital video contents’ features. For the more exact face detection, we use the MLP (Multi-Layer Perceptrons) to make a texture classifier, which discriminates between face pixels and non-face ones[6, 7], and the Gaussian skin-color model, discriminates between skin regions and non-skin regions[8]”, p634, A received image is scanned by the MLP, which receives a given pixel and its neighbors within a small window; in this paper, we use the 11×11 window2. The outputs of the MLP are combined into a face probability image, where each pixel’s value is in the range [0, 1] and represents the probability that the corresponding input pixel is a part of face; one node is for face regions and the other is for non-face regions. If a pixel has a larger value than the given threshold value, we deem it to be a face pixel(Fig. 2), p635).
Regarding claim 17, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Amonkar et al. and Hong et al. further indicate the encryption mode information further comprises an identification of an encryption mode (Amonkar et al., selectively encrypt the detected object using an encryption key following the advanced encryption standard with cipher block chaining mode (AES-CBC), abstract, The present disclosure relates generally to image processing, and more specifically to systems and methods of securely protecting or storing personal information in image or video data based on object recognition followed by image encryption, [0002], selectively extracts an object image with pre-defined features. A image encryption is performed to prevent leakage of individual personal information, [0008], According to one aspect of the present invention, the present system comprises an object detection component for detecting an object region in each of the image frames. In another aspect, the object detection component may designate a detected object region with pre-defined features to be encrypted, [0009]; Hong et al.,
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, Fig. 1, In this paper, we proposed the partial encryption method that uses the face region as the feature so that we encrypt only part of the video frame. The face region is the most important region for the human in the digital video. Therefore our proposed method can reduce the encryption time and can increase the protection strength by adding the location of the face region as the second key, p634, [only encrypts face region so identifies mode as encryption or non-encryption mode based on detection of a face].
Regarding claim 18, Amonkar et al. and Hong et al. disclose the apparatus of claim 17. Hong et al. further indicate the encryption mode is at least one of a task-based mode, an attention-based mode, or a threat-based mode (In this paper, we proposed the partial encryption method using the face region as the feature because the face has the semantic information and is the most important part in the digital content, especially the video contents, abstract, “In this paper, we proposed the partial encryption method that uses the face region as the feature so that we encrypt only part of the video frame. The face region is the most important region for the human in the digital video. Therefore our proposed method can reduce the encryption time and can increase the protection strength by adding the location of the face region as the second key” “To encrypt the video contents, the proposed system uses the face region, which is one of the digital video contents’ features. For the more exact face detection, we use the MLP (Multi-Layer Perceptrons) to make a texture classifier, which discriminates between face pixels and non-face ones[6, 7], and the Gaussian skin-color model, discriminates between skin regions and non-skin regions[8]”, p634) [semantic/important/face discrimination interpreted as attention based].
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amonkar et al. (IDS: US 20230252173 A1) and Hong et al. (“Partial Encryption of Digital Contents Using Face Detection Algorithm”) as applied to claim 5 above, further in view of Maung et al. (“Adversarial Test on Learnable Image Encryption”).
Regarding claim 7, Amonkar et al. and Hong et al. disclose the apparatus of claim 5. Amonkar et al. and Hong et al. do not disclose to perform the attack algorithm task, the processor is configured to perform at least one of a fast gradient sign method (FGSM) or a projected gradient descent (PGD) algorithm.
Maung et al. teach to perform the attack algorithm task, the processor is configured to perform at least one of a fast gradient sign method (FGSM) or a projected gradient descent (PGD) algorithm (Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However,
existing privacy preserving approaches have never considered the threat of adversarial attacks. In this paper, we ran an adversarial test on learnable image encryption in five different scenarios, abstract, There are many different ways of crafting adversarial examples. The popular and computationally efficient one is known as Fast Gradient Sign Method (FGSM) [9]. In this work, we consider a stronger adversary (i.e., multi-step FGSM) known as projected gradient descent (PGD),
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, p668).
Amonkar et al. and Hong et al. and Maung et al. are in the same art of encryption (Amonkar et al., abstract; Hong et al., abstract; Maung et al., abstract). The combination of Maung et al. with Amonkar et al. and Hong et al. will enable using PGD. It would have been obvious at the time of filing to combine the PGD of Maung et al. with the invention of Amonkar et al. and Hong et al. as this was known at the time of filing, the combination would have predictable results, and as Maung et al. indicate “The results show different behaviors of the network in the variable key scenarios and suggest learnable image encryption provides certain level of adversarial robustness” (abstract) demonstrating an improvement to system security against adversarial attacks when inventions are combined.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amonkar et al. (IDS: US 20230252173 A1) and Hong et al. (“Partial Encryption of Digital Contents Using Face Detection Algorithm”) as applied to claim 1 above, further in view of Hall et al. (US 20210090202 A1).
Regarding claim 8, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Amonkar et al. teach using AES-CBC ([0018]) but do not disclose to encrypt at least the portion of the image, the processor is configured to apply a message authentication code (MAC) to at least the portion of the image.
Hall et al. teach to encrypt at least the portion of the image, the processor is configured to apply a message authentication code (MAC) to at least the portion of the image (“FIGS. 5 and 6 are diagrams showing illustrative encryption and decryption processes that may be implemented by host subsystem 120 and image sensor 112 to securely transmit and receive the region-of-interest and (pixel) sparsity parameters. As shown in FIG. 5, host subsystem 120 (e.g., processing circuitry in host subsystem 120) may generate region-of-interest (ROI) configuration data 502 (e.g., parameters defining a region-of interest, sparsity of pixels whose data is used for authentication, a frame counter or frame identifier indicative of when or with which frames this set of configuration data should be used, etc.). Host subsystem 120 (e.g., an encryption engine, processing circuitry, etc.) may perform an encryption operation 506 to encrypt ROI configuration data 502 based on the shared control channel key 504. As an example, encryption circuitry on host subsystem 120 may use an authenticated encryption cryptographic algorithm such as AES-CCM (i.e., Advanced Encryption Standard-Counter with Cipher Block Chaining-Message Authentication Code). The use of an authenticated encryption cryptographic algorithm may enable image sensor 112 to confirm that the encrypted data was generated by host subsystem 120. The encryption operation 506 may thereby generate encrypted ROI parameters and authentication data for the encrypted parameters 508 (e.g., a MAC value for the encrypted parameters)”, [0065]).
Amonkar et al. and Hong et al. and Hall et al. are in the same art of encryption (Amonkar et al., abstract; Hong et al., abstract; Hall et al., [0065]). The combination of Hall et al. with Amonkar et al. and Hong et al. will enable using a MAC. It would have been obvious at the time of filing to combine the MAC of Hall et al. with the invention of Amonkar et al. and Hong et al. as this was known at the time of filing, the combination would have predictable results, and as Hall et al. indicate “The host subsystem may securely provide region-of-interest parameters to the image sensor to update the sparse region-of-interest in an adaptive manner to account for factors such as computational load of the host subsystem and authentication coverage for the entire pixel array” (abstract) “To mitigate these issues, a system (e.g., system 100) may provide a mechanism that allows a host system such as host subsystem 120 to control the per-frame MAC computational load to match the capabilities of processing circuitry on the host subsystem without reducing the authentication efficacy… The system may also include associated circuitry in the imaging system and the host subsystem for securely configuring/ reconfiguring the ROI (e.g., depending on the capabilities of the host subsystem). Illustrative details for these features are described herein” ([0033]) “To verify the authenticity of the image frame from path 238 received at host subsystem 120, host subsystem 120 (FIG. 1) may also include corresponding authentication data generation circuitry (e.g., a MAC engine for generating the MAC value for the incoming data frame to be compared with the MAC value generated by and received from image sensor 112). Data authentication subsystem 240 (FIG. 3) may selectively generate the MAC value based only on a subset of pixel data for a given frame, thereby reducing the computational load compared to scenarios where pixel data from the entire frame is used. As such, computational load placed on the authentication data generation circuitry in host subsystem 120 to generate the corresponding MAC value based on the same subset of pixel data is similarly reduced.” ([0057]) demonstrating a computational load and security benefit to combining inventions.
Claim(s) 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amonkar et al. (IDS: US 20230252173 A1) and Hong et al. (“Partial Encryption of Digital Contents Using Face Detection Algorithm”) as applied to claim 1 above, further in view of Chang et al. (US 20240012892 A1).
Regarding claim 9, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Amonkar et al. and Hong et al. do not disclose the processor is configured to output the encrypted image for transmission to an additional processor of the apparatus.
Chang et al. teach the processor is configured to output the encrypted image for transmission to an additional processor of the apparatus (According to a further embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)), or an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor 121. For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121, or to be specific to a specified function. The auxiliary processor 123 may be implemented as separate from, or as part of the main processor 121, [0029], For example, if the received biometric data of the user is generally exposed data such as the user's face, the electronic device 220 may transmit the received biometric data of the user to the server 260 through the communication module 240. According to another embodiment, the first data processing module 252 may generate the first data by encrypting (e.g., homomorphic encryption) the received biometric data of the user. The first data processing module 252 may generate first data by generating and encrypting feature points from a user's face image captured by a camera, [0058], encrypting feature points from the user's face image, [0069]).
Amonkar et al. and Hong et al. and Chang et al. are in the same art of encryption (Amonkar et al., abstract; Hong et al., abstract; Chang et al., [0065]). The combination of Chang et al. with Amonkar et al. and Hong et al. will enable using an additional processor. It would have been obvious at the time of filing to combine the additional processor of Chang et al. with the invention of Amonkar et al. and Hong et al. as this was known at the time of filing, the combination would have predictable results, and as Chang et al. indicate “For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121, or to be specific to a specified function” ([0029]) providing a power saving benefit to combining inventions.
Regarding claim 10, Amonkar et al. and Hong et al. and Chang et al. disclose the apparatus of claim 9. Chang et al. further teach the processor is an image signal processor (ISP) of the apparatus, and wherein the additional processor is an application processor (AP) of the apparatus (According to a further embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)), or an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor 121. For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121, or to be specific to a specified function. The auxiliary processor 123 may be implemented as separate from, or as part of the main processor 121, [0029]).
Regarding claim 11, Amonkar et al. and Hong et al. and Chang et al. disclose the apparatus of claim 9. Amonkar et al. and Chang et al. further indicate the processor is configured to output the encrypted image for transmission to the additional processor via a secure connection (Amonkar et al., the privacy processing component may selectively encrypt the detected object using an encryption key following the advanced encryption standard with cipher block chaining mode (AES-CBC). The privacy processing component comprises an AES key generator, an AES encryption block, and a Rivest-Shamir-Adleman (RSA) key-pair module. The AES key generator is configured to generate a random AES key. The AES encryption block takes an image with the detected object and the AES key and then produces an encrypted image output. The RSA key-pair module is configured to generate a public key that is used to encrypt the AES key. In another embodiment, the encrypted data is transmitted and stored in a large scale distributed database server so that even in the incidents like thievery or hacking individual privacy would not be compromised. Still in another embodiment, the encrypted AES key is transmitted and stored in a key store database with limited access, [0018]; Chang et al., At least some of the above-described components may be coupled mutually and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)), [0048], According to various embodiments, the processor 275 may transmit a response message by generating a key or token as a result of comparison between the first data and the third data and including the same in the response message. When the result of comparison between the first data and the third data is determined to be valid, the processor 275 may include the key or token in the response message and transmit the response message. When the result of comparison between the first data and the third data is determined to be invalid, the processor 275 may transmit an error message as a response message., [0066], According to a further embodiment, the authentication request message may include a session-specific value and/or a token, [0071]
Regarding claim 12, Amonkar et al. and Hong et al. and Chang et al. disclose the apparatus of claim 11. Chang et al. further indicate the secure connection is a mobile industry processor interface (MIPI) (At least some of the above-described components may be coupled mutually and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)), [0048]).
Regarding claim 13, Amonkar et al. and Hong et al. and Chang et al. disclose the apparatus of claim 9. Amonkar et al. and Chang et al. further indicate the processor is configured to obtain the encryption mode information from the additional processor (Amonkar et al., FIG. 1 depicts an exemplary system for detecting the object region in an image and for encrypting/decrypting the detected object region. The system 100 comprises three main components: a data analytics system 110, a standard dashboard 130, a database server 140. The database server 140 may further comprises a distributed database server 141 and a key store database server 142. Database server 140 is configured to store real-time images. Distributed database server 141 is configured to store encrypted images. Key store database server 142 is configured to store the encrypted AES key. Data analytics system 110 is executed by a computer processor configured to apply deep learning algorithms to detect the object region captured by the image. Standard dashboard 130 is configured to communicate with the data analytics system 110 and the database server 140 to allow a user to encrypt an image or decrypt the encrypted image data using the secure keys, [0083]; Chang et al., the memory 235 may store various data used by at least one component of the electronic device 220. For example, biometric data of the user received from the sensor module 230, the authentication result received from the server 260, and biometric data of the user processed by the processor 250 may be stored in the memory 235. In addition, an algorithm required for the processor 250 to process the received biometric data of the user may be stored in the memory 235, [0054], According to various embodiments, the first data processing module 252 may generate first data by processing the biometric data of the user 210 received from the sensor module 230. According to a further embodiment, the first data processing module 252 may generate first data by applying an algorithm to the received biometric data of the user. The algorithm for generating the first data may be the same algorithm as the algorithm applied to the biometric data of the user in the server 260. The algorithm for generating the first data may be received from the server 260. According to another embodiment, the first data processing module 252 may bypass the received biometric data of the user. For example, if the received biometric data of the user is generally exposed data such as the user's face, the electronic device 220 may transmit the received biometric data of the user to the server 260 through the communication module 240. According to another embodiment, the first data processing module 252 may generate the first data by encrypting (e.g., homomorphic encryption) the received biometric data of the user. The first data processing module 252 may generate first data by generating and encrypting feature points from a user's face image captured by a camera., [0058], The communication module 540 may be controlled by a processor (e.g., a communication processor) different from a processor (e.g., an application processor) that processes the biometric data of the user received from the sensor module 530., [0084], According to various embodiments, the processor 550 may include a data processing module 551, a comparison module 556, and a combining module 558 according to the order and/or method of processing the biometric data of the user 510 received from the sensor module 530. The data processing module 551 may include a first data processing module 552 and a second data processing module 553, [0085]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amonkar et al. (IDS: US 20230252173 A1) and Hong et al. (“Partial Encryption of Digital Contents Using Face Detection Algorithm”) as applied to claim 1 above, further in view of Ha et al. (IDS: US 20200201349 A1).
Regarding claim 14, Amonkar et al. and Hong et al. disclose the apparatus of claim 1. Amonkar et al. and Hong et al. do not disclose the apparatus is a vehicle or a computing system of the vehicle.
Ha et al. teach the apparatus is a vehicle or a computing system of the vehicle (Therefore, it is an aspect of the present disclosure to provide an autonomous driving control apparatus capable of encrypting and storing image data or transmitting encrypted image data to a server when a driving condition is an accident risk condition, a vehicle having the same, and a method for controlling the vehicle, [0006], It is another aspect of the present disclosure to provide an autonomous driving control apparatus capable of encrypting only image data corresponding to a direction in which an accident risk condition occurs, a vehicle having the same, and a method for controlling the vehicle, [0008], The controller may identify a region of interest (ROI) from the image data, identify the sizes of macroblocks divided into different sizes based on the brightness and chrominance of the image data, and encrypt the brightness data of the macroblocks smaller than a predetermined size among the macroblocks within the identified ROI, [0011], The first controller 150 may determine whether the current driving condition is an accident risk condition based on the detection information of the obstacle detected by the obstacle detector 130 in the autonomous driving mode. When it is determined that the current driving condition is the accident risk condition, the first controller 150 may encrypt and store the brightness data of the image data obtained by the first and second image obtainers 110 and 120, [0106]).
Amonkar et al. and Hong et al. and Ha et al. are in the same art of encryption (Amonkar et al., abstract; Hong et al., abstract; Ha et al., [0065]). The combination of Ha et al. with Amonkar et al. and Hong et al. will enable implementing as a computing system of a vehicle. It would have been obvious at the time of filing to combine the vehicle of Ha et al. with the invention of Amonkar et al. and Hong et al. as this was known at the time of filing, the combination would have predictable results, and as Ha et al. indicate “The vehicle may further include the vehicle terminal for user convenience” ([0216]) “As described above, the present disclosure can efficiently store the image data in the embedded environment by selectively encrypting and storing only a part of the image data by taking advantage of the characteristics of the image data in the compressed NAL form according to the memory limitation in the embedded environment.” ([0289]) providing a computational benefit to combining inventions, and expanding commercial applicability with use in a vehicle.
Conclusion
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/MICHELLE M ENTEZARI HAUSMANN/Primary Examiner, Art Unit 2671
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