DETAILED ACTION
Response to Amendment
Claims 1-20 are pending. Claim 20 has been added.
Response to Arguments
Applicant’s arguments filed 11/25/2025 have been fully considered.
The rejections of claims 1-9 under 35 U.S.C. 112 have been withdrawn in view of the amendment.
Regarding the rejection of claim 1 as being unpatentable under 35 U.S.C. 103 over Zhang et al. (CN110011954B) in view of Jiang et al. (US20210058229A1), Applicant argues on page 14 that Zhang has not been shown to teach or suggest that "the information processing system comprises: a client, a first node, and a second node" and the client to “send, to the second node, an encryption feature of a to-be recognized first object and the number information of the client" as recited in amended claim 1.
Applicant’s arguments are not persuasive.
First, Zhang discloses “the information processing system comprises: a client [terminal], a first node [service server], and a second node [similarity calculation module]” by showing in [page 6 lines 49-50] the terminal can send a request to the service server; [page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic;
Furthermore, Zhang explains the service server includes the similarity calculation module 43 by showing in [page 10 lines 31-32] the service server is an apparatus; [page 8 lines 51-52] shows the apparatus (e.g., service server) may include a similarity calculation module 43;
Finally, Zhang discloses the client to “send, to the second node [similarity calculation module 43 inside the service server], an encryption feature of a to-be recognized first object and the number information of the client" by showing in [page 6 lines 35-77] shows the terminal to obtain the facial features of the user to be identified; the terminal can send the encrypted biometric feature to the service server (e.g., to the similarity calculation module 43 inside the service server) based on the identity information of the user to be identified so that the service server (e.g., similarity calculation module 43) determines the encryption comparison source feature of the user to be identified according to the comparison source feature acquisition request; [page 8 lines 51-63] shows the apparatus (e.g., service server) may include a similarity calculation module 43; the similarity calculation module 43 is configured to calculate a similarity between the encryption comparison source feature and the encrypted biometric feature.
Therefore Zhang discloses the language of the claim.
As to any argument not specifically addressed, they are the same as those discussed above.
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.
Claims 1-7, 10-11 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN110011954B) in view of Jiang et al. (US20210058229A1).
Regarding claim 1, Zhang discloses an information processing system, wherein the information processing system comprises ([page 6 line 7] shows a biometric identification method):
a client ([page 6 lines 8-10] shows a terminal acquires biometric characteristics of a user, such as iris characteristics, facial characteristics, fingerprint characteristics, and the like),
a first node [service server] ([page 6 lines 49-50] shows the terminal can send a request to the service server; [page 10 lines 31-32] shows the service server is an apparatus; [page 8 lines 51-52] shows the apparatus (e.g., service server) may include a similarity calculation module), and
a second node [similarity calculation module] ([page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic; [page 6 lines 10-12] shows comparison source characteristics are pre-stored biometric characteristics bound with user identity information);
wherein the client comprises at least one first processor and at least one first memory coupled to the at least one first processor and storing first programming instructions for execution by the at least one first processor to ([page 5 lines 11-15] shows a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor):
send, to the first node [service server], a recognition request [comparison request] that carries number information [identity information] of the client ([page 6 lines 8-50] shows terminal acquires biometric characteristics of a user; the terminal may first search the identity information of a user to be identified; the terminal can send a comparison source feature acquisition request to a service server (in which comparison source characteristics (pre-stored biometric characteristics bound with user identity information) are stored) based on the identity information of the user to be identified); and
send, to the second node [similarity calculation module inside the service server], an encryption feature [encrypted biometric feature] of a to-be-recognized first object and the number information [identity information] of the client, wherein the encryption feature of the first object is obtained based on a public key of the first node and a feature of the first object ([page 6 lines 35-77] shows the terminal to obtain the facial features of the user to be identified; the terminal can send the encrypted biometric feature to the service server (e.g., similarity calculation module 43 inside the service server) based on the identity information of the user to be identified so that the service server (e.g., similarity calculation module 43) determines the encryption comparison source feature of the user to be identified according to the comparison source feature acquisition request; [page 8 lines 51-63] shows the apparatus (e.g., service server) may include a similarity calculation module 43; the similarity calculation module 43 is configured to calculate a similarity between the encryption comparison source feature and the encrypted biometric feature);
wherein the first node comprises at least one second processor and at least one second memory coupled to the at least one second processor and storing second programming instructions for execution by the at least one second processor to ([page 9 lines 60-62] shows a service server, which at least includes a memory, a processor, and a computer program stored in the memory and executable on the processor):
determine a first target file [comparison source features] in the second node in response to the recognition request ([page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored); and
send a recognition indication [encrypted biometric feature based on the identity information] to the second node, wherein the recognition indication carries the number information [identity information] of the client and file information [stored in a designated server] of the first target file [comparison source features] ([page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified);
wherein the second node is configured to:
in response to the recognition indication [encrypted biometric feature based on the identity information], compare the encryption feature of the first object based on the received encryption feature of the first object [encrypted biometric feature], the number information [identity information] of the client, the file information [stored in a designated server] of the first target file [comparison source features] to obtain a comparison result ([page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified); and
send the comparison result [encryption similarity] to the first node ([page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic; the similarity sending module is used for sending the encryption similarity to the service server); and
wherein the second programming instructions are for execution by the at least one second processor to ([page 9 lines 60-62] shows the service server, which at least includes a memory, a processor, and a computer program stored in the memory and executable on the processor):
in response to receiving the comparison result, decrypt the comparison result based on a private key of the first node ([page 4 lines 58-62] shows the similarity sending module is used for sending the encryption similarity and a preset similarity threshold to the service server; the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity);
obtain object information [plaintext similarity] of the first object based on a decrypted comparison result ([page 4 lines 58-62] shows the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity); and
send the object information of the first object to the client ([page 4 lines 58-62] shows the service server decrypts the encryption similarity by using a private key to obtain plaintext similarity, and sending a comparison result of the plaintext similarity and the similarity threshold to a home terminal).
Zhang discloses the similarity sending module 44 [second node] as an independent module ([page 8 lines 13-15]) but fails to teach:
the second node comprises at least one third processor and at least one third memory coupled to the at least one third processor and storing third programming instructions for execution by the at least one third processor to:
compare the encryption feature of the first object based on an evaluation key to obtain a comparison result.
However, Jiang discloses:
the second node [secure service provider server] comprises at least one third processor and at least one third memory coupled to the at least one third processor and storing third programming instructions for execution by the at least one third processor to (para [0045] shows the secure service provider server; para [0038] shows computer readable program instructions may be stored in a computer readable storage medium that can direct a processor of a computer to function):
compare the encryption feature of the first object based on an evaluation key to obtain a comparison result ([Abstract] shows performing computations on sensitive data while guaranteeing privacy; para [0045] shows the secure service provider server has only access to the evaluation key for homomorphic computation. The service provider server computes over encryption and returns the ciphertext results to the query client (QC), which then decrypts them; para [0066] shows the service provider computing device 102 transmits the encrypted result to computing device 101 to decrypt the encrypted result which matches a result of performing a matrix multiplication on unencrypted matrices A and B thereby enabling computations to be performed in a secure manner.)
The similarity calculation module 43 that calculates an encryption similarity between the encryption comparison source feature and the encrypted biometric feature in Zhang ([page 8 lines 62-63]) is mapped to the secure service provider server that computes on encrypted data without decryption and generates an encrypted result which matches that of operations on plaintext in Jiang (para [0045, 0074]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang with the teaching of Jiang in order to perform computations on sensitive data while guaranteeing privacy by performing homomorphic matrix computations (Jiang; para [0001]).
Regarding claim 2, Zhang-Jiang as applied to claim 1 discloses the first programming instructions are for execution by the at least one first processor to (Zhang; [page 5 lines 11-15] shows a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor):
perform encoding based on the feature of the first object to obtain a first feature vector of the first object, wherein the first feature vector is an M-dimensional vector [iris characteristic vector, facial characteristic vector, fingerprint characteristic vector]; obtain a second feature vector of the first object based on the first feature vector of the first object, wherein the second feature vector of the first object is NxM-dimensional vectors [matrix comprising iris characteristic vector, facial characteristic vector, fingerprint characteristic vector], and the second feature vector of the first object comprises N first feature vectors of the first object (Zhang; [page 6 lines 8-10] shows a terminal acquires biometric characteristics of a user (e.g., biometric characteristic vectors such as iris characteristic vector, facial characteristic vector, fingerprint characteristic vector, and the like). Jiang; para [0006] shows data is encoded as vectors; para [0024] shows the data as vectors of a matrix, which is homomorphically encrypted using an encryption key to generate a ciphertext); and
encrypt the second feature vector of the first object based on the public key of the first node to obtain the encryption feature of the first object, wherein M and N are positive integers (Jiang; para [0006] shows data is encoded as vectors; para [0024] shows the data as vectors of a matrix, which is homomorphically encrypted using an encryption key to generate a ciphertext; para [0077] shows using the public key pk, the encryption algorithm encrypts a message into a ciphertext).
Regarding claim 3, Zhang-Jiang as applied to claim 1 discloses the second node comprises a scheduling node [comparison module] and a plurality of target nodes (Zhang; [page 4 lines 49] shows a comparison module. Jiang; para [0035] shows the network may comprise servers); and
wherein the second programming instructions are for execution by the at least one second processor to ([page 5 lines 16-20] shows a service server, including a memory, a processor, and a computer program stored in the memory and executable on the processor):
in a file information table [comparison source features] based on the number information [identity information] of the client in the recognition request [comparison request], a first target node [local feature library in designated server] that corresponds to the client and that is in the second node [similarity calculation module]; and in the file information table, the file information of the first target file in the first target node, wherein the file information table comprises a correspondence between the number information of the client and the plurality of target nodes comprised in the second node and a correspondence between the plurality of target nodes and files (Zhang; [page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic; [page 6 lines 8-50] shows the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified, where a correspondence between the identity information and an encryption comparison source feature is recorded in the encryption comparison source feature library; .the terminal can send a comparison request to a service server based on the identity information of the user to be identified).
Regarding claim 4, Zhang-Jiang as applied to claim 3 discloses the third programming instructions are for execution by the at least one third processor to (Zhang; [page 8 lines 51-52] shows the similarity calculation module 43. Jiang; [0038] shows computer readable program instructions may be stored in a computer readable storage medium that can direct a processor of a computer to function):
in response to the recognition indication [encrypted biometric feature based on the identity information], indicate the first target node to load the evaluation key of the first node into memory (Zhang; [page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; [page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored. Jiang; para [0045] shows the secure service provider server has only access to the evaluation key for homomorphic computation.)
Regarding claim 5, Zhang-Jiang as applied to claim 3 discloses the third programming instructions are for execution by the at least one third processor to (Zhang; [page 8 lines 51-52] shows the similarity calculation module 43. Jiang; [0038] shows computer readable program instructions may be stored in a computer readable storage medium that can direct a processor of a computer to function):
in response to the recognition indication [encrypted biometric feature based on the identity information], send the recognition indication and the encryption feature of the first object to the first target node in the second node based on the file information [designated server] of the first target file [encryption comparison source characteristic] when the number information [identity information] received from the client is consistent with the number information of the client carried in the recognition indication (Zhang; [page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server (in which comparison source characteristics (pre-stored biometric characteristics bound with user identity information) are stored) based on the identity information of the user to be identified) based on the identity information of the user to be identified; [page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic.)
Regarding claim 6, Zhang-Jiang as applied to claim 4 discloses the third programming instructions are for execution by the at least one third processor to (Zhang; [page 8 lines 51-52] shows the similarity calculation module 43. Jiang; para [0045] shows the secure service provider server; para [0038] shows computer readable program instructions may be stored in a computer readable storage medium that can direct a processor of a computer to function)
perform, in a ciphertext state based on the evaluation key of the first node, an inner product operation on the received encryption feature of the first object and each registered feature in the first target file to obtain the comparison result (Jiang; [Abstract] shows the service provider performs a homomorphic matrix multiplication on the first and second ciphertexts without decrypting the first and second ciphertexts. An encrypted result from the performed homomorphic matrix multiplication on the first and second ciphertexts is generated.)
Regarding claim 7, Zhang-Jiang as applied to claim 3 discloses the second programming instructions are for execution by the at least one second processor to (Zhang; [page 5 lines 16-20] shows a service server, including a memory, a processor, and a computer program stored in the memory and executable on the processor):
determine a target feature based on the decrypted comparison result, wherein the target feature is a registered feature with a maximum similarity to the feature of the first object (Zhang; [Abstract] shows encrypting the biological identification characteristic by using a public key to obtain an encrypted biological identification characteristic; calculating encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic; sending the encryption similarity and the similarity threshold to a service server so that the service server decrypts the encryption similarity by using a private key to obtain plaintext similarity; [page 6 lines 8-50] shows a server in which comparison source characteristics (pre-stored biometric characteristics bound with user identity information) are stored based on the identity information of the user to be identified);
obtain object information [local library] of the target feature from the file information table based on location information of the target feature in the first target file (Zhang; [page 6 lines 8-50] shows the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified),
wherein the file information table comprises object information of each registered feature in a file in the second node and location information of each registered feature in the file in the second node; and determine the object information of the target feature as the object information of the first object (Zhang; [page 6 lines 8-50] shows a terminal acquires biometric characteristics of a user (e.g., biometric characteristic vectors), such as iris characteristics, facial characteristics, fingerprint characteristics, and the like; the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified.)
Regarding claim 10, Zhang discloses an information processing method ([page 6 line 7] shows a biometric identification method),
wherein the method is performed by a client in an information processing system ([page 6 lines 8-10] shows a terminal acquires biometric characteristics of a user (e.g., biometric characteristic vectors), such as iris characteristics, facial characteristics, fingerprint characteristics, and the like),
the information processing system further comprises a first node [service server] ([page 6 lines 49-50] shows the terminal can send a comparison request to the service server; [page 10 lines 31-32] shows the service server is an apparatus; [page 8 lines 51-52] shows the apparatus may include a similarity calculation module 4), and
a second node [similarity calculation module] ([page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic; [page 6 lines 10-12] shows comparison source characteristics are pre-stored biometric characteristics bound with user identity information), and
the method comprises:
sending, to the first node [service server], a recognition request [comparison request] that carries number information [identity information] of the client ([page 6 lines 8-50] shows the terminal acquires biometric characteristics of a user; the terminal may first search the identity information of a user to be identified; the terminal can send a comparison source feature acquisition request to a service server (in which comparison source characteristics (pre-stored biometric characteristics bound with user identity information) are stored) based on the identity information of the user to be identified);
sending, to the second node [similarity calculation module 43 inside the service server], an encryption feature [encrypted biometric feature] of a to-be-recognized first object and the number information [identity information] of the client, wherein the encryption feature of the first object is obtained based on a public key of the first node and a feature of the first object ([page 6 lines 35-77] shows the terminal performs feature extraction on the facial image to obtain the facial features of the user to be identified; the terminal can send the encrypted biometric feature to the service server (e.g., similarity calculation module) based on the identity information of the user to be identified; the terminal may encrypt the obtained biometric feature of the user by using the public key, the encrypted data is referred to as an encrypted biometric feature; [page 8 lines 51-52, 62-63] shows the apparatus (e.g., service server) may include a similarity calculation module 43; the similarity calculation module 43 is configured to calculate a similarity between the encryption comparison source feature and the encrypted biometric feature); and
receiving object information [plaintext similarity] of the first object from the first node, wherein the object information of the first object is obtained based on the encryption feature of the first object, an evaluation key of the first node, and a private key of the first node ([page 4 lines 58-62] shows the service server decrypts the encryption similarity by using a private key to obtain plaintext similarity, and sending a comparison result of the plaintext similarity and the similarity threshold to a home terminal).
Zhang fails to teach the object information of the first object is obtained based on an evaluation key of the first node.
However, Jiang discloses the object information of the first object is obtained based on an evaluation key of the first node ([Abstract] shows performing computations on sensitive data while guaranteeing privacy; para [0045] shows the secure service provider server has only access to the evaluation key for homomorphic computation. The service provider server computes over encryption and returns the ciphertext results to the query client (QC), which then decrypts them.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang with the teaching of Jiang in order to perform computations on sensitive data while guaranteeing privacy by performing homomorphic matrix computations (Jiang; para [0001]).
Regarding claim 11, Zhang-Jiang as applied to claim 10 discloses before the sending an encryption feature of a to-be-recognized first object and the number information of the client to the second node, the method further comprises (Zhang; [page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; [page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic):
performing encoding based on the feature of the first object to obtain a first feature vector of the first object, wherein the first feature vector is an M-dimensional vector [iris characteristic vector, facial characteristic vector, fingerprint characteristic vector]; obtaining a second feature vector of the first object based on the first feature vector of the first object, wherein the second feature vector of the first object is NxM-dimensional vectors [matrix comprising iris characteristic vector, facial characteristic vector, fingerprint characteristic vector], and the second feature vector of the first object comprises N first feature vectors of the first object (Zhang; [page 6 lines 8-10] shows a terminal acquires biometric characteristics of a user (e.g., biometric characteristic vectors such as iris characteristic vector, facial characteristic vector, fingerprint characteristic vector, and the like). Jiang; para [0006] shows data is encoded as vectors; para [0024] shows the data as vectors of a matrix, which is homomorphically encrypted using an encryption key to generate a ciphertext); and
encrypting the second feature vector of the first object based on the public key of the first node, to obtain the encryption feature of the first object, wherein M and N are positive integers (Jiang; para [0006] shows data is encoded as vectors; para [0024] shows the data as vectors of a matrix, which is homomorphically encrypted using an encryption key to generate a ciphertext; para [0077] shows using the public key pk, the encryption algorithm encrypts a message into a ciphertext).
Regarding claim 15, Zhang discloses a first node [service server] of a system, wherein the first node comprises ([page 6 lines 49-50] shows the terminal can send a comparison request to the service server):
at least one processor; and at least one memory, being configured to store at least one segment of program code, and coupled to the at least one processor and storing programming instructions for execution by the at least one processor to ([page 8 lines 51-52, 62-63] shows the apparatus (e.g., service server) may include a similarity calculation module 43):
determine a first target file [comparison source features] in a second node [similarity calculation module 43 inside the service server] of a system in response to a recognition request [comparison request] that is received from a client that carries number information [identity information] of the client ([page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic; [page 6 lines 8-50] shows the terminal acquires biometric characteristics of a user; the terminal may first search the identity information of a user to be identified; the terminal can send a comparison request to a service server (e.g., to the similarity calculation module inside the service server) based on the identity information of the user to be identified),
send a recognition indication [encrypted biometric feature based on the identity information] to the second node [similarity calculation module 43 inside the service server], wherein the recognition indication carries the number information [identity information] of the client and file information [stored in a designated server] of the first target file [comparison source features] ([page 6 lines 49-50] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; [page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic; the similarity sending module is used for sending the encryption similarity to the service server),
decrypt a comparison result [encryption similarity] based on a private key of the first node when receiving the comparison result from the second node ([page 4 lines 57-61] shows the similarity sending module is used for sending the encryption similarity to the service server; the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity);
obtain object information [plaintext similarity] of a first object based on the decrypted comparison result ([page 4 lines 58-62] shows the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity); and
send the object information of the first object to the client ([page 4 lines 58-62] shows the service server decrypts the encryption similarity by using a private key to obtain plaintext similarity, and sending a comparison result of the plaintext similarity and the similarity threshold to a home terminal),
wherein the comparison result [encryption similarity] is obtained from the second node which performs, in response to the recognition indication [encrypted biometric feature based on the identity information], comparison on an encryption feature of the first object based on the encryption feature [encrypted biometric feature] of the first object, the number information [identity information] of the client, the file information [stored in a designated server] of the first target file [encryption comparison source feature] ([page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; [page 6 lines 35-77] shows the terminal performs feature extraction on the facial image to obtain the facial features of the user to be identified; the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; the terminal may encrypt the obtained biometric feature of the user by using the public key, the encrypted data is referred to as an encrypted biometric feature; [page 8 lines 62-63] shows a similarity calculation module 43 configured to calculate a similarity between the encryption comparison source feature and the encrypted biometric feature).
Zhang fails to teach the comparison on an encryption feature of the first object based on an evaluation key of the first node.
However, Jiang discloses the comparison on an encryption feature of the first object based on an evaluation key of the first node ([Abstract] shows performing computations on sensitive data while guaranteeing privacy; para [0045] shows the secure service provider server has only access to the evaluation key for homomorphic computation. The service provider server computes over encryption and returns the ciphertext results to the query client (QC), which then decrypts them; para [0066] shows the service provider computing device 102 transmits the encrypted result to computing device 101 to decrypt the encrypted result which matches a result of performing a matrix multiplication on unencrypted matrices A and B thereby enabling computations to be performed in a secure manner.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang with the teaching of Jiang in order to perform computations on sensitive data while guaranteeing privacy by performing homomorphic matrix computations (Jiang; para [0001]).
Regarding claim 16, Zhang-Jiang as applied to claim 15 discloses the second node comprises a scheduling node [comparison module] and a plurality of target nodes (Jiang; para [0035] shows the network may comprise servers. Zhang; [page 4 lines 49] shows a comparison module), and
the determining a first target file [comparison source features] in a second node [similarity calculation module] in response to a recognition request that is received from a client that carries number information [identity information] of the client comprises ([page 3 lines 73-77] shows the comparison source features of the user are only allowed to be stored in a designated server; [page 6 lines 35-77] shows the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; [page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic; the similarity sending module is used for sending the encryption similarity to the service server):
determining, in a file information table [designated server] based on the number information of the client in the recognition request, a first target node [local feature library in designated server] that corresponds to the client and that is in the second node [similarity calculation module]; and determining, in the file information table, the file information of the first target file in the first target node, wherein the file information table comprises a correspondence between the number information of the client and the plurality of target nodes comprised in the second node, and a correspondence between the plurality of target nodes and files (Zhang; [page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; the terminal generally sends the collected biometric features to a server in which the comparison source features are stored; [page 4 lines 57-59] shows the similarity calculation module is used for calculating the similarity between the comparison source characteristic and the biometric identification characteristic; [page 6 lines 8-50] shows the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified, where a correspondence between the identity information and an encryption comparison source feature is recorded in the encryption comparison source feature library; .the terminal can send a comparison request to a service server based on the identity information of the user to be identified).
Regarding claim 17, Zhang-Jiang as applied to claim 16 discloses the obtaining object information [plaintext similarity] of a first object based on the decrypted comparison result comprises (Zhang; [page 4 lines 58-62] shows the similarity sending module is used for sending the encryption similarity and a preset similarity threshold to the service server; the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity):
determining, by the first node, a target feature based on the decrypted comparison result, wherein the target feature is a registered feature with a maximum similarity to the feature of the first object (Zhang; [page 4 lines 58-62] shows the similarity sending module is used for sending the encryption similarity and a preset similarity threshold to the service server; the service server decrypts the encryption similarity by using a private key to obtain a plaintext similarity);
obtaining object information [plaintext similarity] of the target feature from the file information table based on location information of the target feature in the first target file (Zhang; [page 6 lines 8-50] shows the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified)
wherein the file information table comprises object information of each registered feature in a file in the second node and location information of each registered feature in the file in the second node; and determining the object information of the target feature as the object information of the first object (Zhang; [page 6 lines 8-50] shows a terminal acquires biometric characteristics of a user (e.g., biometric characteristic vectors), such as iris characteristics, facial characteristics, fingerprint characteristics, and the like; the terminal may first search a locally stored encryption comparison source feature library according to identity information of a user to be identified.)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Jiang et al., further in view of CN111726369B.
Regarding claim 12, Zhang-Jiang as applied to claim 10 discloses the method further comprises:
sending an encryption feature [encrypted biometric feature] of the second object [facial features] and the number information [identity information] of the client to the second node [similarity calculation module], wherein the encryption feature of the second object is obtained based on the public key of the first node, a feature of the second object, and file information [stored in a designated server] of a second target file [comparison source features] in the second node (Zhang; [page 3 lines 73-77] shows because the biometric features belong to private sensitive data, the comparison source features of the user are only allowed to be stored in a designated server; [page 6 lines 35-77] shows the terminal performs feature extraction on the facial image to obtain the facial features of the user to be identified; the terminal can send the encrypted biometric feature to the service server based on the identity information of the user to be identified; the terminal may encrypt the obtained biometric feature of the user by using the public key, the encrypted data is referred to as an encrypted biometric feature; [page 4 lines 57-61] shows the similarity calculation module is used for calculating the encryption similarity between the encryption comparison source characteristic and the encryption biometric identification characteristic; the similarity sending module is used for sending the encryption similarity to the service server)
Zhang-Jiang fails to teach:
sending a registration request to the first node, wherein the registration request carries the number information of the client and object information of a to-be-registered second object; and
the file information of the second target file is determined by the first node in response to the registration request; and
receiving registration success information from the first node.
However CN111726369B, in an analogous art (page 10 shows homomorphic encryption), discloses:
sending a registration request to the first node, wherein the registration request carries the number information of the client and object information of a to-be-registered second object (page 15 shows the user registration information of each user at least comprises: the identity information, the account number, the encrypted password and the encrypted second biological characteristic information of the user. During registration, the electronic device first obtains a registration request, then obtains an account name and a password (n, p) input by a user. Then, the electronic device acquires the biometric information input by the user. The electronic device sends the user registration information to an application server for registration); and
the file information of the second target file is determined by the first node in response to the registration request; and receiving registration success information from the first node (page 15 shows storing the user registration information to a database after the registration is finished so as to be used in identity authentication.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang-Jiang with the teaching of CN111726369B in order to match the user information to be authenticated and user registration information stored in the database, so that identity authentication is completed when a user logs in the mobile terminal application (CN111726369B; page 3).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Jiang et al., further in view of Morris et al. (US20210118579A1)
Regarding claim 14, Zhang-Jiang as applied to claim 10 fails to teach the method further comprises:
sending a deregistration request to the first node, wherein the deregistration request carries the number information of the client and object information of a to-be-deregistered third object; and
receiving deregistration success information from the first node.
However Morris, in an analogous art (para [0121] shows patient biomarker readings can be accomplished using homomorphic encryption), discloses:
sending a deregistration request to the first node, wherein the deregistration request carries the number information of the client and object information of a to-be-deregistered third object; and receiving deregistration success information from the first node (para [0079] shows the patient can also de-register or revoke a previously-registered identity. Moreover, the provider can no longer decrypt future biomarker readings based on or linked with this revoked patient identity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang-Jiang with the teaching of Morris in order to revoke a previously-registered identity (Morris; para [0079]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Jiang et al., further in view of Luo et al. (US20230063590A1)
Regarding claim 20, Zhang-Jiang as applied to claim 1 fails to teach the client, the first node, and the second node are three independent devices.
However, Luo discloses the client, the first node, and the second node are three independent devices ([Abstract] shows a to-be-verified face image inputted into a verification device; para [0038] shows a smartphone and a plurality of physical servers.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhang-Jiang with the teaching of Luo in order to benefit from cloud computing service (Luo; para [0038]).
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CN111726369B discloses on page 15 the user registration information of each user at least comprises: the identity information, the account number, the encrypted password and the encrypted second biological characteristic information of the user. CN111726369B fails to teach claim 8.
Eldefrawy et al. (US20190312731A1) discloses in [Abstract] during an initial enrollment process, the reference biometric template (RBT) reader obtains a biometric from a user, transforms the biometric into an RBT, and provides different shares of the RBT to the authenticator and the auxiliary system; para [0056] shows storage nodes 110 may use techniques such as homomorphic encryption. Eldefrawy fails to teach claim 8.
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Morris et al. (US20210118579A1) discloses in [Abstract] a patient identification/biometrics module; para [0079] shows the patient can also de-register or revoke a previously-registered identity; para [0121] shows patient biomarker readings can be accomplished using homomorphic encryption. Morris fails to teach claim 8.
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CN111726369B discloses on page 15 the user registration information of each user at least comprises: the identity information, the account number, the encrypted password and the encrypted second biological characteristic information of the user. During registration, the electronic device first obtains a registration request, then obtains an account name and a password (n, p) input by a user. Then, the electronic device acquires the biometric information input by the user. The electronic device sends the user registration information to an application server for registration. CN111726369B fails to teach “a vacant location number of the second target file… the first feature vector of the second object is located at a location that corresponds to the vacant location number and that is in the second feature vector of the second object, and a location other than the location that corresponds to the vacant location number and that is in the second feature vector of the second object is 0” as in claim 13.
Eldefrawy et al. (US20190312731A1) discloses in [Abstract] during an initial enrollment process, the reference biometric template (RBT) reader obtains a biometric from a user, transforms the biometric into an RBT, and provides different shares of the RBT to the authenticator and the auxiliary system; para [0056] shows storage nodes 110 may use techniques such as homomorphic encryption. Eldefrawy fails to teach “a vacant location number of the second target file… the first feature vector of the second object is located at a location that corresponds to the vacant location number and that is in the second feature vector of the second object, and a location other than the location that corresponds to the vacant location number and that is in the second feature vector of the second object is 0” as in claim 13.
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CN111726369B discloses on page 15 the user registration information of each user at least comprises: the identity information, the account number, the encrypted password and the encrypted second biological characteristic information of the user. During registration, the electronic device first obtains a registration request, then obtains an account name and a password (n, p) input by a user. Then, the electronic device acquires the biometric information input by the user. The electronic device sends the user registration information to an application server for registration. CN111726369B fails to teach claim 18.
Eldefrawy et al. (US20190312731A1) discloses in [Abstract] during an initial enrollment process, the reference biometric template (RBT) reader obtains a biometric from a user, transforms the biometric into an RBT, and provides different shares of the RBT to the authenticator and the auxiliary system; para [0056] shows storage nodes 110 may use techniques such as homomorphic encryption. Eldefrawy fails to teach claim 18.
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Morris et al. (US20210118579A1) discloses in [Abstract] a patient identification/biometrics module; para [0079] shows the patient can also de-register or revoke a previously-registered identity; para [0121] shows patient biomarker readings can be accomplished using homomorphic encryption. Morris fails to teach claim 19.
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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/TAN DOAN/Primary Examiner, Art Unit 2445