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 .
Response to Amendment
The amendment filed 02/27/2026 has been entered. Claims 1, 2, 7 and 8 have been amended. Claims 13-20 have been added. Claims 1-20 are pending for examination.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks, filed on 02/27/2026, with respect to the provisional non-statutory double patenting rejection are moot because the reference application, Application no. 18/117,552, was abandoned on 04/21/2026. Accordingly, the double patenting rejection has been withdrawn.
Applicant’s arguments, with respect to the rejection of independent claims 1 and 7 under 35 U.S.C. §102 and the rejection of claims 2 and 8 under 35 U.S.C. §103 have been fully considered. Applicant argues that the cited references fail to teach the amended limitations. However, the Examiner relies on Karlinsky in the current rejection to teach the amended limitations. Accordingly, applicant’s arguments are moot because they are not directed to the reference relied upon in the current rejection.
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.
Claims 1, 6, 7, 12-14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0209383 (Hu et al.) in view of US 10832096 (Karlinsky et al.).
Regarding Claim 1, Hu teaches a machine learning system, comprising: a server configured to hold a common model ([Fig. 1, ¶ 0022], federated learning system 100 may include a central server 110…global model 111 (e.g., a ML model or a statistical model) may be hosted on the central server 110); and a plurality of clients each configured to hold concealment target data and an individual model ([Fig. 1, ¶ 0022] each local computing system (e.g., 120, 130, and 140) may access the its local data samples (e.g., 122, 132, and 142) and train the corresponding local model (e.g., 121, 131, and 141) locally based on the accessed local data samples. [¶ 0023], federated learning may be used to train ML models or statistic models while protecting data security and data privacy. Since Hu teaches protecting data security and data privacy, therefore, the data disclosed by Hu can reasonably be interpreted as the target data subject to concealment),
wherein the server is configured to transmit the common model to the plurality of clients ([¶ 0022], During the federated learning process, the central server 110 may identify a ML model or a statistical model [i.e., common model] that needs to be trained and may transmit that model in current state to a number of local computing systems),
wherein each of the plurality of clients is configured to: generate a learning result obtained by updating the common model based on the concealment target data and the individual model held by the each of the plurality of clients itself ([¶ 0022], After receiving the model from the central server 110, each local computing system (e.g., 120, 130, and 140) may access the its local data samples (e.g., 122, 132, and 142) and train the corresponding local model (e.g., 121, 131, and 141) locally based on the accessed local data samples. As a result, different local models (e.g., 121, 131, and 141) may have different parameter values after being trained on the corresponding local computing systems using the local data samples of that local computing system. [¶ 0036], Each federated node having a local model may train that model based on the local data samples on that federated node); and
transmit the generated learning result to the server ([¶ 0022], the local computing systems (e.g., 120, 130, and 140) may transmit the respective local models (e.g., 121, 131, and 141) with the trained parameters (or transmit these trained parameters) to the central server 110), and wherein the server is configured to update the common model held by the server itself based on the learning result received from the each of the plurality of clients ([¶ 0022], The central server 110 may aggregate the training results from these local computing systems and integrate the trained parameters to the global model 111. As a result, the global model 111 may be trained without the central server 110 accessing the local data samples (e.g., 122, 132, and 142) on these local computing systems (e.g., 120, 130, and 140). [¶ 0036], After being trained locally at respective federated nodes, the local models and/or the model parameters may be transmitted from these federated nodes back to the central server. After being trained, the local models and corresponding parameters may be transmitted back to the centralized server. The central server may aggregate all these distributed local models that have been trained on respective federated nodes and generate the final training model. The final model may be updated based on an aggregation of a number of local models that are trained on respective federated nodes).
Hu does not explicitly specify the structure of the individual model, however, Karlinsky teaches wherein the individual model is a representative vector of feature vectors generated from the concealment target data (Karlinsky teaches representative vector generated from feature vectors/embedding. For example, [C.2:L.54-56], Each class is represented by a mixture model with multiple modes, and the centers of these modes are considered as the representative vectors for the class. [C.3:L.3-7], The input to subnet can include the feature vectors pooled from the regions of interest (ROIs). …the class posteriors for a given ROI are computed by comparing its embedding vector to the set of representatives for each category. [C.5:L.35-37], the mixture model manager can jointly learn or determine a feature extractor model, a common embedding space for a resulting feature space).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Karlinsky's representative-vector metric-learning techniques into Hu’s federated learning system because such incorporation would have improved representation learning by encoding concealment target data into compact feature-vector embeddings suitable for efficiency distributed model training and aggregation.
Regarding Claim 6, Hu teaches the machine learning system according to claim 1, wherein the individual model held by the each of the plurality of clients is a fixed model exclusively held by the each of the plurality of clients, and is generated based on the common model and personal data held by the each of the plurality of clients ([¶ 0021], The global model may be hosted on a central server and may be distributed to one or more federated nodes (i.e., local sites or clients). After the model is transmitted or distributed to these local sites, these models may be referred to as local models (hosted on respective local sites). These local models may be trained on respective local sites based on local data samples of these local sites).
Regarding Claims 7 and 12, the claim limitations are identical and/or equivalent in scope to claims 1 and 6, therefore, Claims 7 and 12 are rejected under the same rationale as claims 1 and 6.
Regarding Claim 13, Hu does not explicitly teach, however, Karlinsky teaches the machine learning system according to claim 1, wherein the feature vectors are generated by inputting the concealment target data to the common mode ([C.5:L.35-37], the mixture model manager can jointly learn or determine a feature extractor model, a common embedding space for a resulting feature space. [C.3:L.3-7], The input to subnet can include the feature vectors pooled from the regions of interest (ROIs). [C.7:L.33-41] the input of the subnet can be a single pooled feature vector computed by a backbone for the given image or ROI…The output of the embedding module is an embedded vector).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Karlinsky's representative-vector metric-learning techniques into Hu’s federated learning system in order to improve discriminative representation learning and model accuracy by generating compact embedding representations and optimizing distance between representative vectors using margin-based loss function.
Regarding Claim 14, Hu teaches the machine learning system according to claim 1, wherein the concealment target data comprises personal data ([Fig. 1, ¶ 0022] each local computing system (e.g., 120, 130, and 140) may access the its local data samples (e.g., 122, 132, and 142) and train the corresponding local model (e.g., 121, 131, and 141) locally based on the accessed local data samples. [¶ 0023], federated learning may be used to train ML models or statistic models while protecting data security and data privacy. Since Hu teaches protecting data security and data privacy, therefore, the data disclosed by Hu can reasonably be interpreted as the target data subject to concealment).
Regarding Claim 16, Hu teaches the machine learning system according to claim 1, wherein… the common model and the concealment target data held by the each of the plurality of clients (([Fig. 1, ¶ 0022] each local computing system (e.g., 120, 130, and 140) may access the its local data samples (e.g., 122, 132, and 142) and train the corresponding local model (e.g., 121, 131, and 141) locally based on the accessed local data samples. [¶ 0023], federated learning may be used to train ML models or statistic models while protecting data security and data privacy. Since Hu teaches protecting data security and data privacy, therefore, the data disclosed by Hu can reasonably be interpreted as the target data subject to concealment).
However, Hu does not explicitly teach, but Karlinsky teach wherein the representative vector is generated based on the common model and the…target data (Karlinsky teaches representative vector generated from feature vectors/embedding. For example, [C.2:L.54-56], Each class is represented by a mixture model with multiple modes, and the centers of these modes are considered as the representative vectors for the class. [C.3:L.3-7], The input to subnet can include the feature vectors pooled from the regions of interest (ROIs). …the class posteriors for a given ROI are computed by comparing its embedding vector to the set of representatives for each category. [C.5:L.35-37], the mixture model manager can jointly learn or determine a feature extractor model, a common embedding space for a resulting feature space).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Karlinsky's representative-vector metric-learning techniques into Hu’s federated learning system because such incorporation would have improved representation learning by encoding concealment target data into compact feature-vector embeddings suitable for efficiency distributed model training and aggregation.
Claims 2, 8, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Karlinsky, and further in view of US 2022/0164712 (Behera et al.) and US 2021/0374617 (Chu et al.).
Regarding Claim 2, Hu in view of Karlinsky do not explicitly teach, however, Behera teaches the machine learning system according to claim 1, wherein the each of the plurality of clients is configured to: generate a concealed individual model by applying concealment transformation to the individual model held by the each of the plurality of clients itself ; and transmit, to the server, the generated concealed individual model… ([¶¶ 0034-0035] Each participant node may include a local machine learning model, …Participant nodes may communicate their respective local machine learning models with aggregator node using a secure messaging protocol, such as message level dynamic cryptography. For example, participant nodes may encrypt their respective local machine learning models and may send the encrypted models [i.e., concealed individual model] to aggregator node), wherein the server is configured to: update each of the concealed individual models ([¶ 0036] Aggregator node may aggregate the received decrypted local machine learning models, and may perform federated learning on the models, and may generate aggregated model); and transmit each of the updated concealed individual models to each of the plurality of clients…([¶ 0036], Aggregator node may aggregate the received decrypted local machine learning models, and may perform federated learning on the models, and may generate aggregated model. It may then send aggregated model to participant nodes. Aggregated model may be encrypted), and wherein the each of the plurality of clients is configured to: restore an updated individual model from the updated concealed individual model received from the server; and update the individual model held by the each of the plurality of clients itself ([¶ 0036], Participant nodes may update their respective models as is necessary and/or required. The federated learning process may continue until an objective is reached, such as the different local models converge to a desired point).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Behera's teaching of encrypt machine learning models before transmission to the combined teachings of Hu and Karlinsky, because such incorporation would have addressed known security risks in distributed learning system, ensuring that models shared between server and clients cannot be intercepted or altered by unauthorized entities.
Hu in view of Karlinsky and Behera do not explicitly teach, however, Chu teaches transmit, to the server, …model in association with identification information for identifying the each of the plurality of clients… transmit each of the updated models to each of the plurality of clients indicated by the identification information corresponding to the individual model ([¶ 0077], each set of updated local model parameters is transmitted to the respective client. For example, using some identifying metadata (e.g., a tag or identifier originally associated with each set of local model parameters received from the clients) associated with a given set of updated local model parameters, may identify the client).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chu's teaching of transmit updated model to the respective client based on the identifier associated with each set of local model of each client to the combined teachings of Hu, Karlinsky and Behera, because such incorporation would have ensured that a specific update is correctly attributed to the correct client that generated it and the server could reliably match an updated model to its source client.
While, Behera teaches update each of the concealed individual model, however, Hu in view of Behera and Chu do not explicitly teach, but Karlinsky teaches optimizing distances between the concealed individual models such that distances between representative vectors are equal to or longer than a predetermined margin ([C.6:L.25-28] The embedding loss can be based on distances between the embedded vectors and modes of mixture distributions for the categories. The embedding loss can penalize any of the training points that are closer to a mode of an incorrect class than the distance to a closest correct class mode plus a margin parameter. [C.8:L.43-47] The other loss or embedding loss is intended to ensure there is at least a margin between the distance of E to the closest representative of the correct class, and the distance of E to the closest representative of a wrong class. [C.7:L.56-58] the sub-net architecture 300 computes…the distances from E to every representative).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Karlinsky's distance-based embedding optimization techniques to the combined teachings of Hu- Behera-Chu, because optimizing distance between representative vectors improves distinction between different data classes and reduces overlap between learned client-side representations in the federated learning environment.
Regarding Claim 8, the claim limitations are identical and/or equivalent in scope to claim 2, therefore, Claim 8 is rejected under the same rationale as claim 2.
Regarding Claim 19, Hu in view of Karlinsky and Chu do not explicitly teach, however, Behera teaches wherein the concealed individual model is generated by applying the concealment transformation to the representative vector … ([¶¶ 0034-0035] Each participant node may include a local machine learning model, …Participant nodes may communicate their respective local machine learning models with aggregator node using a secure messaging protocol, such as message level dynamic cryptography. For example, participant nodes may encrypt their respective local machine learning models and may send the encrypted models [i.e., concealed individual model] to aggregator node).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Behera's teaching of encrypt machine learning models before transmission to the combined teachings of Hu and Karlinsky, because such incorporation would have addressed known security risks in distributed learning system, ensuring that models shared between server and clients cannot be intercepted or altered by unauthorized entities.
Regarding Claim 20, Hu in view of Behera and Chu do not explicitly teach, however, Karlinsky teaches wherein the predetermined margin in a concealed domain corresponds to a margin between the representative vectors due to concealment transformation preserving distances ([C.6:L.18-28] As the neural network is trained by the mixture model manager, a weighted average or sum of a cross entropy loss and an embedded loss can be calculated. The embedding loss can be based on distances between the embedded vectors and modes of mixture distributions for the categories. The embedding loss can penalize any of the training points that are closer to a mode of an incorrect class than the distance to a closest correct class mode plus a margin parameter).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Karlinsky's distance-based embedding optimization techniques to the combined teachings of Hu- Behera-Chu, because optimizing distance between representative vectors improves distinction between different data classes and reduces overlap between learned client-side representations in the federated learning environment.
Claims 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu-Karlinsky-Behera-Chu, and further in view of US 2009/0122979 (Lee et al.).
Regarding Claim 3, Hu in view of Karlinsky, Behera and Chu do not explicitly teach, however, Lee teaches the machine learning system according to claim 2, wherein the concealment transformation includes projective transformation having an orthonormal matrix as a parameter ([¶ 0003], Lee discloses a data protection technique that converts original data into a secure form so that even if data registered to a system or database is revealed, information relating to original data cannot be revealed from the revealed data. [¶¶ 0155, 0157] When the probe feature vector is input for comparison and authentication, the low-dimensional coordinates are generated from the probe feature vector. (p.sub.1, p.sub.2) from among the generated low-dimensional coordinates is transformed by the transformation function T.sub.1 and (p.sub.3, p.sub.4) is transformed by the transformation function T.sub.2. … for the efficiency of the system, affine transformation [i.e., projective transformation] formed of an orthogonal matrix A.sub.i and a random vector b.sub.i may be used for the transformation function).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lee's teaching of affine transformation formed of an orthogonal matrix used for the transformation function to the combined teachings of Hu-Karlinsky-Behera-Chu, because such incorporation would have provided a mathematically well-defined way to conceal data while retaining computational utility.
Regarding Claim 9, the claim limitations are identical and/or equivalent in scope to claim 3, therefore, Claim 9 is rejected under the same rationale as claim 3.
Claims 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu- Karlinsky-Behera-Chu, and further in view of WO 2018037689 (Yonaha).
Regarding Claim 4, Hu in view of Karlinsky, Behera and Chu do not explicitly teach, however, Yonaha teaches the machine learning system according to claim 2, further comprising a parameter server configured to hold a parameter, wherein the parameter server is configured to transmit the parameter to the plurality of clients, and wherein the each of the plurality of clients is configured to execute the concealment transformation through use of the parameter received from the parameter server ([Page7, para. 8] user terminal performs projective transformation1 [i.e., concealment transformation] on the enlarged image based on the projective transformation formula having the transformation parameters received from the image processing server, and performs projective transformation. Since Yonaha teaches received parameter from a server to perform projective transformation, therefore it would be understood that the server hold parameter and transmit the parameter to the client terminal to perform the projective transformation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Yonaha's teaching of receive transformation parameters from a server for projective transformation to the combined teachings of Hu- Karlinsky-Behera-Chu, because such incorporation would have protected client data while retaining centralized control over privacy, training stability, and system-wide consistency.
Regarding Claim 10, the claim limitations are identical and/or equivalent in scope to claim 4, therefore, Claim 10 is rejected under the same rationale as claim 4.
Claims 5, 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hu in view Karlinsky, and further in view of US 2018/0308107 (Deng).
Regarding Claim 5, Hu in view of Karlinsky does not explicitly teach, however, Deng teaches the machine learning system according to claim 1, wherein the concealment target data is personal data, and wherein a first client included in the plurality of clients is configured to: hold: the common model; a template based on a feature vector for registration obtained by inputting personal data for registration to the common model; and personal data for authentication; input the personal data for authentication to the common model, to thereby acquire a feature vector for authentication; and execute personal authentication based on the template and the acquired feature vector for authentication ([¶ 0042], through a registration procedure, a user is guided to submit basic identity data information and set a password, and the user is prompted to make a specified action through the camera so as to acquire facial video image data of the registered user. The information mentioned above is sent to a user authority management module, and a facial recognition model and basic data information of each user are established and saved correspondingly. [¶ 0044], basic data information about the user, and corresponding facial recognition model information, questionnaire setting and management authority or questionnaire answering authority are saved and managed; through the data information submitted and account type information selected upon user registration, the questionnaire setting or question answering authority corresponding to the user are configured, and authority determination and allocation are performed after the user logs in; and a facial model corresponding to the user is established by means of the facial video acquired upon user registration, for verifying user consistency. ([¶ 0052], establishing a set of facial feature vectors for each registered user as the user's facial feature model and correspondingly storing same to the user authority management module, for later model comparison when verifying the user consistency; and when user verification is performed, extracting a key frame of the user's facial video during verification, and comparing the feature vectors of the 72 points on the face to the facial feature model of the corresponding user, so as to determine user consistency. [¶¶ 0059-0062] verification action information is acquired, wherein the verification action information comprises a verification feature vector of the human face, …an action model base is established according to the verification action information and an operation instruction corresponding thereto. By training through machine learning and by analyzing a large amount of facial action change…the extracted verification feature vectors of the human face are stored in correspondence to various action instruction template bases, so as to establish a recognition model for user authentication. In the training process, the recognition result needs to be compared constantly to correct the vector set for each instruction; and this step mainly consists in identifying whether a real human is answering questions through a verification action. …action recognition information about a user is acquired, the action recognition information comprising a current feature vector of a human face. the action recognition information about the user is compared to a verification feature vector in the action recognition model base, and the verification is passed if a comparison result indicates consistency).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Deng's teaching of training a machine learning for analyzing a large amount of facial action change by extracting verification feature vectors of the human face that are stored in correspondence action instruction template, so as to establish a recognition model for user authentication to the combined teachings of Hu and Karlinsky, because such incorporation would have enabled dynamic security level and improving recognition accuracy by allowing biometric processing to be tailored to the context.
Regarding Claim 11, the claim limitations are identical and/or equivalent in scope to claim 5, therefore, Claim 11 is rejected under the same rationale as claim 5.
Regarding Claim 15, Hu in view Karlinsky do not explicitly teach, however, Deng teaches the machine learning system according to claim 1, wherein the each of the plurality of clients is configured to generate a template based on the representative vector for use in personal authentication ([¶¶ 0013-0017], acquiring verification action information, wherein the verification action information comprises a verification feature vector of the human face. …establishing an action model base according to the verification action information and an operation instruction corresponding thereto…constructing a facial recognition model base according to the acquired facial recognition information about the user. …determining whether the similarity between the action recognition information and the verification action information in the action recognition model base is greater than a pre-set value, the verification being passed if yes. [¶ 0060], the extracted verification feature vectors of the human face are stored in correspondence to various action instruction template bases, so as to establish a recognition model for user authentication).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Deng's teaching of training a machine learning for analyzing a large amount of facial action change by extracting verification feature vectors of the human face that are stored in correspondence action instruction template, so as to establish a recognition model for user authentication to the combined teachings of Hu and Karlinsky, because such incorporation would have enabled dynamic security level and improving recognition accuracy by allowing biometric processing to be tailored to the context.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Hu-Karlinsky-Behera-Chu, and further in view of US 2023/0052433 (Bank et al.).
Regarding Claim 17, Hu in view of Karlinsky-Behera-Chu do not explicitly teach, however, Bank teaches wherein the concealment transformation preserves Euclidean distances between vectors such that a distance between any two individual models before the concealment transformation equals a distance between corresponding concealed individual models after the concealment transformation ([¶ 0118], According to a Johnson-Lindenstrauss lemma, random mappings may preserve the Euclidean distances of data points in the original high-dimensional space... to enable this feature, the elements of R may be independent and identically distributed with zero mean. [¶ 0108], Random Projection may be offered using random projection module to reduce the dimension of data points …while maintaining reasonable accuracy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bank's teaching of distance-preserving random projection as a concealment transformation maintain inter-vector distance while enabling efficient downstream nearest-neighbor computation into the combined teachings of Hu-Karlinsky-Behera-Chu, because such incorporation would have provided predictable benefit of preserving model relationships while reducing dimensionality.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hu-Karlinsky-Behera-Chu, and further in view of US 2022/0414208 (Teranishi).
Regarding Claim 18, Hu in view of Karlinsky-Behera-Chu do not explicitly teach, however, Teranishi teaches the machine learning system according to claim 2, wherein the server is configured to update each of the concealed individual models without restoring the concealed individual models to the individual models ([¶ 0085], a model update parameter to be transmitted from the terminal apparatus to the server is encrypted and then transmitted and a model update parameter is computed without being decrypted [i.e., without restoring] in the server).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Teranishi's teaching of updating model parameter without being decrypted into the combined teachings of Hu-Karlinsky-Behera-Chu, because such incorporation would have ensured security of the underlying model parameter or gradients.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MOHAMMAD YOUSUF A. MIAN/Examiner, Art Unit 2457
/ARIO ETIENNE/Supervisory Patent Examiner, Art Unit 2457
1 According to the claim 3, concealment transformation includes projective transformation.
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