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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17, 21-22 and 24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. The limitations recited in claims 1 and 10 are unclear, for example, the limitation “performing, based on a first private key, an acquired biometric vector to be matched, a preset generating element, a second private key, and first encrypted data, a number of interactions and processes with a server to obtain second encrypted data, the first private key being a private key for the terminal device, the second private key being a private key for the server, the first encrypted data being pre-obtained by the terminal device through encrypting a sample biometric vector using the generating element and the first private key and sent to the server, a computational operator comprising the second private key and a target Euclidean distance being formed in the second encrypted data, the target Euclidean distance comprising a Euclidean distance between the biometric vector to be matched and the sample biometric vector” and “sending the second encrypted data to the server to cause the server to obtain, using the second encrypted data, the generating element, the second private key, and a preset Euclidean distance matching threshold, a matching result for the biometric vector to be matched and the sample biometric vector” is unclear what the claim scope is and what steps being performed. The claims are written in general narrative form causing ambiguity in the determining the claim scope. Dependent claims are also rejected for inheriting the deficiencies of the independent claims set forth above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 24 is rejected under 35 U.S.C. § 101 because it is directed to non-statutory subject matter, specifically a 'computer readable medium' that is not explicitly defined as 'non-transitory,' thereby encompassing both tangible storage devices and transitory signals, which are considered non-patentable subject matter under current case law. To overcome this rejection, Applicant must amend the claim to clearly recite a 'non-transitory computer readable medium' or otherwise limit the claim scope to exclude transitory signals."
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-17, 21-22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Higo et al (2021/0367783) in views of Li et al (2021/0119803).
For claim 1, Higo teaches a biometric matching method applied to a terminal device (Higo teaches of using system vector type biometric information as Higo teaches in par.183), the method comprising: performing, based on a first key, an acquired biometric vector to be matched, a preset generating element, a second private key (Higo teaches of par.111 secret key), and first encrypted data, a number of interactions (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111) and processes with a server to obtain second encrypted data (Higo teaches that the matching apparatus 140 multiplies the generated second random numbers {a.sub.i} (i=1, . . . , n) and the first encrypted data (Enc(pk, (2x.sub.i−1))) (i=1, . . . , n) transmitted from storage apparatus 130, to obtain encrypted data (Enc(pk, a.sub.i(2x.sub.i−1))) (i=1, . . . , n), and the matching apparatus 140 multiplies the encrypted data (Enc(pk, a.sub.i(2x.sub.i−1))) (i=1, . . . , n) and the second encrypted data as Higo teaches in par.139 and 140), the second private key being a private key for the server (Higo teaches that the secret key is for the server and the verification apparatus 150 decrypts the encrypted data (Enc(pk, c.sub.i (2x.sub.i−1))) (i=1, . . . , n) transmitted from the storage apparatus 130 by using a secret key (sk) as Higo teaches in par.144 and 164), the first encrypted data being pre-obtained by the terminal device through encrypting a sample biometric vector using the generating element and the first key and sent to the server (Higo teaches that registration request apparatus 110, which is part of the terminal device 101 as shown in fig.1, transmits at least one of encrypted data (Enc(x.sub.i) (i=1, . . . , n)) of individual elements {x.sub.i} (i=1, . . . , n) of the registration target first binary vector X and the first evaluated values (operation results) of the individual elements (for example, Enc(1−2x.sub.i) (i=1, . . . , n) in FIGS. 4, 9, and 10 or Enc(A.sub.i) in FIG. 14) to a storage apparatus 130 which is part of the server 102 as shown in fig.1 as Higo teaches in par.86 and 169 and 193), a computational operator comprising the second private key and a target Euclidean distance being formed in the second encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183), the target Euclidean distance comprising a Euclidean distance between the biometric vector to be matched and the sample biometric vector (Higo teaches that Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.183) and sending the second encrypted data to the server to cause the server to obtain (Higo teaches that the matching request apparatus, element 120 as shown in fig.1, transmit the obtained ecrytped data to the matching apparatus, element 140 as shown in fig.1, and the matching apparatus sends the encrypted data to the verification apparatus, element 150 as shown in fig.1 as Higo teaches in par.177-180), using the second encrypted data, the generating element, the second private key, and a preset Euclidean distance matching threshold, a matching result for the biometric vector to be matched and the sample biometric vector (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key, the first private key being a private key for the terminal device.
Li teaches, similar system, first private key, the first private key being a private key for the terminal device (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include private key for the terminal device as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 2, Higo, as modified by Li, further teaches that wherein the performing, based on the first key, the acquired biometric vector to be matched, the preset generating element, the second private key, and the first encrypted data, a number of interactions and processes with the server to obtain the second encrypted data (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111) comprises: receiving first intermediate encrypted data sent by the server, the first intermediate encrypted data being obtained by the server through encrypting the first encrypted data using the second private key (Higo teaches that matching request apparatus 120 calculates third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)), which is a sum of values obtained by multiplying the second encrypted data (Enc(pk, S(1−2x.sub.i)), . . . , Enc(pk, S(1−2x.sub.n))) transmitted from the matching apparatus 140 by the individual elements {y.sub.i}(i=1, . . . , n) of the registration target n-dimensional second binary vector Y=[y.sub.1, . . . , y.sub.n] through a scalar operation as Higo teaches in par.90), computational operators comprising products of the first key, the second private key, and elements in the sample biometric vector being formed in the first intermediate encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183); obtaining second intermediate encrypted data according to the first intermediate encrypted data, the biometric vector to be matched, the generating element, and the first key, computational operators comprising products of the first key, the second private key, and elements in the biometric vector to be matched being formed in the second intermediate encrypted data (Higo teaches that the matching request apparatus, element 120 as shown in fig.1, transmit the obtained ecrytped data to the matching apparatus, element 140 as shown in fig.1, and the matching apparatus sends the encrypted data to the verification apparatus, element 150 as shown in fig.1 as Higo teaches in par.177-180); and performing, based on the second intermediate encrypted data, the first encrypted data, the first key, and the second private key, interactions and processes with the server, and removing the first private key from the processed data to obtain the second encrypted data (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 3, Higo, as modified by Li, further teaches wherein the second intermediate encrypted data comprises first intermediate encrypted subdata and second intermediate encrypted subdata, and the obtaining the second intermediate encrypted data according to the first intermediate encrypted data, the biometric vector to be matched, the generating element, and the private key (Higo teaches that matching apparatus 140 calculates the fourth encrypted data (Enc(pk, Σ[i=1 to n] (1−2x.sub.i)y.sub.i)) by removing the random number (S) from the third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)) transmitted from the matching request apparatus 120 as Higo teaches in par.90-93) comprises: obtaining the first intermediate encrypted subdata according to the first intermediate encrypted data and the biometric vector to be matched, computational operators comprising products of the first private key, the second private key, elements in the sample biometric vector, and elements in the biometric vector to be matched being formed in the first intermediate encrypted subdata (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183); and obtaining the second intermediate encrypted subdata according to the first key, the generating element, and the biometric vector to be matched, computational operators comprising products of the first private key and elements in the biometric vector to be matched being formed in the second intermediate encrypted subdata (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 4, Higo, as modified by Li, further teaches wherein the performing, based on the second intermediate encrypted data, the first encrypted data, the key, and the second private key, interactions and processes with the server, and removing the first key from the processed data to obtain the second encrypted data (Higo teaches that matching apparatus 140 calculates the fourth encrypted data (Enc(pk, Σ[i=1 to n] (1−2x.sub.i)y.sub.i)) by removing the random number (S) from the third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)) transmitted from the matching request apparatus 120 as Higo teaches in par.90-93) comprises: sending the second intermediate encrypted data to the server to cause the server to obtain third intermediate encrypted data based on the second intermediate encrypted data, the first encrypted data, and the second private key (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111), a computational operator comprising a product of the first key, the second private key, and the target Euclidean distance being formed in the third intermediate encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183);; receiving the third intermediate encrypted data sent by the server; and removing, using the first private key, the first key from the third intermediate encrypted data to obtain the second encrypted data (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 5, Higo, as modified by Li, further teaches wherein the matching result comprises a successful match under a condition that the second encrypted data belongs to a matched data set; the matching result comprises a failed match under a condition that the second encrypted data does not belong to the matched data set; and wherein a maximum value of elements in the matched data set is obtained based on the second private key and the preset Euclidean distance matching threshold (Higo teaches that verification apparatus 150 decrypts the encrypted data (Enc(pk, d.sub.H (X, Y))) by using a secret key (sk) (Dec(sk, Enc(pk, d.sub.H (X, Y)))). Next, the verification apparatus 150 outputs acceptance or rejection based on the comparison between the Hamming distance (d.sub.H (X, Y)) between the first and second binary vectors obtained as a result of the decryption and a threshold as Higo teaches in par.92).
For claim 6, Higo, as modified by Li, further teaches wherein elements in the biometric vector to be matched, elements in the sample biometric vector, and the preset Euclidean distance matching threshold are converted to integers by equal multiples, and computational operators comprising products of the second private key and each of 0 to a square of the preset Euclidean distance matching threshold after being converted to an integer being formed in the matched data set (Higo teaches in par.174).
For claim 7, Higo, as modified by Li, further teaches wherein the second encrypted data belongs to the matched data set under a condition that values of positions in a pre-established Bloom filter lookup table corresponding to K target hash values are all 1; the second encrypted data does not belong to the matched data set under a condition that at least one of the values of the positions in the Bloom filter lookup table corresponding to the K target hash values is 0; and wherein the K target hash values are calculated based on the second encrypted data according to K hash functions, the values in the Bloom filter lookup table are calculated based on elements in the matched data set according to the K hash functions, and K is a positive integer (Higo teaches that generates hash values (H(c.sub.i{circumflex over ( )}2)) of the squares (c.sub.i{circumflex over ( )}2) of the respective random number c.sub.i. The storage apparatus 130 transmits the encrypted data (Enc(pk, c.sub.i (2x.sub.i−1))) (i=1, . . . , n) obtained by multiplying each random number c.sub.i (i=1, . . . , n) to the encrypted data (Enc(pk, (2x.sub.i−1))) (i=1, . . . , n) through a scalor operation and the hash values (H(c.sub.i{circumflex over ( )}2)) (i=1, . . . , n) to the verification apparatus as Higo teaches in par.133-135).
For claim 8, Higo, as modified by Li, further teaches before the performing, based on the first key, the acquired biometric vector to be matched, the second private key, and the first encrypted data, a number of interactions and processes with the server to obtain the second encrypted data: acquiring the sample biometric vector; encrypting the sample biometric vector using the generating element and the first key to obtain the first encrypted data (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111), computational operators comprising products of the first key and elements in the sample biometric vector being formed in the first encrypted data; and sending the first encrypted data to the server (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 9, Higo, as modified by Li, further teaches wherein the computational operator comprises a modular exponentiation operator or a dot product operator (Higo teaches in par.147).
For claim 10, Higo teaches that A biometric matching method applied to a server, the method comprising: performing, based on a second private key (Higo teaches of par.111 secret key),, first encrypted data, a first key, a preset generating element, and a biometric vector to be matched acquired by a terminal device, a number of interactions and processes with the terminal device to cause the terminal device to obtain second encrypted data (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111), the second private key being a private key for the server (Higo teaches that the secret key is for the server and the verification apparatus 150 decrypts the encrypted data (Enc(pk, c.sub.i (2x.sub.i−1))) (i=1, . . . , n) transmitted from the storage apparatus 130 by using a secret key (sk) as Higo teaches in par.144 and 164), the first encrypted data being pre-obtained by the terminal device through encrypting a sample biometric vector using the generating element and the first key and sent to the server (Higo teaches that registration request apparatus 110, which is part of the terminal device 101 as shown in fig.1, transmits at least one of encrypted data (Enc(x.sub.i) (i=1, . . . , n)) of individual elements {x.sub.i} (i=1, . . . , n) of the registration target first binary vector X and the first evaluated values (operation results) of the individual elements (for example, Enc(1−2x.sub.i) (i=1, . . . , n) in FIGS. 4, 9, and 10 or Enc(A.sub.i) in FIG. 14) to a storage apparatus 130 which is part of the server 102 as shown in fig.1 as Higo teaches in par.86 and 169 and 193), a computational operator comprising the second private key and a target Euclidean distance being formed in the second encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183), the target Euclidean distance comprising a Euclidean distance between the biometric vector to be matched and the sample biometric vector (Higo teaches that Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.183); receiving the second encrypted data sent by the terminal device (Higo teaches that the matching request apparatus, element 120 as shown in fig.1, transmit the obtained ecrytped data to the matching apparatus, element 140 as shown in fig.1, and the matching apparatus sends the encrypted data to the verification apparatus, element 150 as shown in fig.1 as Higo teaches in par.177-180),; and obtaining, using the second encrypted data, the second private key, the generating element, and a preset Euclidean distance matching threshold, a matching result for the biometric vector to be matched and the sample biometric vector (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key, the first private key being a private key for the terminal device.
Li teaches, similar system, first private key, the first private key being a private key for the terminal device (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include private key for the terminal device as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 11, Higo, as modified by Li, further teaches wherein the performing, based on the second private key, the first encrypted data, the first key, the preset generating element, and the biometric vector to be matched acquired by the terminal device, a number of interactions and processes with the terminal device to cause the terminal device to obtain the second encrypted data (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111) comprises: encrypting the first encrypted data using the second private key to obtain first intermediate encrypted data (Higo teaches that matching request apparatus 120 calculates third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)), which is a sum of values obtained by multiplying the second encrypted data (Enc(pk, S(1−2x.sub.i)), . . . , Enc(pk, S(1−2x.sub.n))) transmitted from the matching apparatus 140 by the individual elements {y.sub.i}(i=1, . . . , n) of the registration target n-dimensional second binary vector Y=[y.sub.1, . . . , y.sub.n] through a scalar operation as Higo teaches in par.90), computational operators comprising products of the first key, the second private key, and elements in the sample biometric vector being formed in the first intermediate encrypted data; sending the first intermediate encrypted data to the terminal device to cause the terminal device to obtain second intermediate encrypted data according to the first intermediate encrypted data, the biometric vector to be matched, the generating element, and the first key (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183),, computational operators comprising products of the first key and elements in the biometric vector to be matched being formed in the second intermediate encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183);; and performing, based on the second intermediate encrypted data, the first encrypted data, the first key, and the second private key, interactions and processes with the terminal device to cause the terminal device to remove the first private key from the processed data to obtain the second encrypted data (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 12, Higo, as modified by Li, further teaches wherein the second intermediate encrypted data comprises first intermediate encrypted subdata and second intermediate encrypted subdata, the first intermediate encrypted subdata is obtained by the terminal device according to the first intermediate encrypted data and the biometric vector to be matched, computational operators (Higo teaches that matching apparatus 140 calculates the fourth encrypted data (Enc(pk, Σ[i=1 to n] (1−2x.sub.i)y.sub.i)) by removing the random number (S) from the third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)) transmitted from the matching request apparatus 120 as Higo teaches in par.90-93) comprising products of the first key, the second private key, elements in the sample biometric vector, and elements in the biometric vector to be matched being formed in the first intermediate encrypted subdata, and the second intermediate encrypted subdata is obtained by the terminal device according to the first private key, the generating element, and the biometric vector to be matched, computational operators (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183); comprising products of the first private key and elements in the biometric vector to be matched being formed in the second intermediate encrypted subdata (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key.
Li teaches first private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 13, Higo, as modified by Li, further teaches wherein the performing, based on the second intermediate encrypted data, the first encrypted data, the first key, and the second private key, interactions and processes with the terminal device to cause the terminal device to remove the first key from the processed data to obtain the second encrypted data (Higo teaches that matching apparatus 140 calculates the fourth encrypted data (Enc(pk, Σ[i=1 to n] (1−2x.sub.i)y.sub.i)) by removing the random number (S) from the third encrypted data (Enc(pk, SΣ[i=1 to n] (1−2x.sub.i)y.sub.i)) transmitted from the matching request apparatus 120 as Higo teaches in par.90-93) comprises: receiving the second intermediate encrypted data sent by the terminal device; obtaining third intermediate encrypted data based on the second intermediate encrypted data, the first encrypted data, and the second private key (Higo teaches that transmits encrypted data obtained by performing an operation of each of first values related to a first binary vector encrypted with an encryption key, and the random number, with each of the first values being kept in an encrypted state, or encrypted data obtained by encrypting the generated random number with the encryption key, to a matching request apparatus as Higo teaches in par.29 and 111), a computational operator comprising a product of the key, the second private key, and the target Euclidean distance being formed in the third intermediate encrypted data (Higo teaches that the matching apparatus 140 performs an additive homomorphic operation to obtain the encrypted data of the distance and Hamming distance between binary vectors can be calculated by the same method used to calculate a Euclidean distance between multivalued vectors. Thus, a system in which the above multivalued-vector-type biometric information is matched by using the Euclidean distance d.sub.E.sup.2 (X, Y) seems to be capable of handling binary-vector-type biometric information as Higo teaches in par.178-183); and sending the third intermediate encrypted data to the terminal device to cause the terminal device to remove, using the key, the first key from the third intermediate encrypted data to obtain the second encrypted data (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183).
Higo fails to teach that first private key and private key.
Li teaches first private key and private key (private key of the first terminal device as Li teaches in par.142-143). It would have been obvious to one ordinary skill in the art before effective filling date to modify Higo to include first private key as taught and suggested by Li for the purpose of obtaining the fingerprint feature information in the first ciphertext form and the fingerprint feature information is verified, thereby ensuring the integrity and validity of the stored fingerprint feature information (Li, par.177).
For claim 14, Higo, as modified by Li, further teaches wherein the obtaining, using the second encrypted data, the second private key, the generating element, and the preset Euclidean distance matching threshold, the matching result for the biometric vector to be matched and the sample biometric vector (Higo teaches that verification apparatus 150 decrypts the encrypted data (Enc(pk, d.sub.H (X, Y))) by using a secret key (sk) (Dec(sk, Enc(pk, d.sub.H (X, Y)))). Next, the verification apparatus 150 outputs acceptance or rejection based on the comparison between the Hamming distance (d.sub.H (X, Y)) between the first and second binary vectors obtained as a result of the decryption and a threshold as Higo teaches in par.92) comprises: obtaining a matched data set based on the second private key, the generating element, and the preset Euclidean distance matching threshold, a maximum value of elements in the matched data set being obtained based on the second private key and the preset Euclidean distance matching threshold (Higo teaches that the verification apparatus 150 decrypts the encrypted data of the Hamming distance between the binary vectors X and Y by using a secret key sk, and the verification apparatus 150 determines whether the decrypted value (Hamming distance) is less than or equal to a threshold t. If the decrypted value is less than or equal to the threshold t, the verification apparatus 150 determines acceptance. If the decrypted value is over the threshold t, the verification apparatus 150 determines rejection as Higo teaches in par.117 and 179-183); determining that the matching result comprises a successful match under a condition that the second encrypted data belongs to the matched data set; and determining that the matching result comprises a failed match under a condition that the second encrypted data does not belong to the matched data set (Higo teaches that verification apparatus 150 decrypts the encrypted data (Enc(pk, d.sub.H (X, Y))) by using a secret key (sk) (Dec(sk, Enc(pk, d.sub.H (X, Y)))). Next, the verification apparatus 150 outputs acceptance or rejection based on the comparison between the Hamming distance (d.sub.H (X, Y)) between the first and second binary vectors obtained as a result of the decryption and a threshold as Higo teaches in par.92).
For claim 15, Higo, as modified by Li, further teaches wherein elements in the biometric vector to be matched, elements in the sample biometric vector, and the preset Euclidean distance matching threshold are converted to integers by equal multiples, and the obtaining the matched data set based on the second private key, the generating element, and the preset Euclidean distance matching threshold (Higo teaches that verification apparatus 150 decrypts the encrypted data (Enc(pk, d.sub.H (X, Y))) by using a secret key (sk) (Dec(sk, Enc(pk, d.sub.H (X, Y)))). Next, the verification apparatus 150 outputs acceptance or rejection based on the comparison between the Hamming distance (d.sub.H (X, Y)) between the first and second binary vectors obtained as a result of the decryption and a threshold as Higo teaches in par.92) comprises: calculating products of the second private key and each of 0 to a square of the preset Euclidean distance matching threshold after being converted to an integer; and obtaining the matched data set according to the products of the second private key and each of 0 to the square of the preset Euclidean distance matching threshold after being converted to an integer and the generating element (Higo teaches in par.174).
For claim 16, Higo, as modified by Li, further teaches calculating K target hash values based on the second encrypted data according to K hash functions; determining that the second encrypted data belongs to the matched data set under a condition that values of positions in a pre-established Bloom filter lookup table corresponding to the K target hash values are all 1 (Higo teaches that generates hash values (H(c.sub.i{circumflex over ( )}2)) of the squares (c.sub.i{circumflex over ( )}2) of the respective random number c.sub.i. The storage apparatus 130 transmits the encrypted data (Enc(pk, c.sub.i (2x.sub.i−1))) (i=1, . . . , n) obtained by multiplying each random number c.sub.i (i=1, . . . , n) to the encrypted data (Enc(pk, (2x.sub.i−1))) (i=1, . . . , n) through a scalor operation and the hash values (H(c.sub.i{circumflex over ( )}2)) (i=1, . . . , n) to the verification apparatus as Higo teaches in par.133-136); determining that the second encrypted data does not belong to the matched data set under a condition that at least one of the values of the positions in the Bloom filter lookup table corresponding to the K target hash values is 0; and wherein the K target hash values are calculated based on the second encrypted data according to the K hash functions, the values in the Bloom filter lookup table are calculated based on elements in the matched data set according to the K hash functions, and K is a positive integer (Higo teaches that query generation unit 145 generates a query including the encrypted data Enc(pk, a.sub.i(2x.sub.i−1)) and Enc(pk, b.sub.i r.sub.i (2y.sub.i−1)) (i=1, . . . , n) and the hash values H(a.sub.i b.sub.i r.sub.i) (i=1, . . . , n) (C20) and transmits the query to the verification apparatus 150 via the communication unit 146 (C21). H denotes a one-way hash function as Higo teaches in par.395-400).
For claim 17, Higo, as modified by Li, further teaches performing, using the K hash functions, calculation on each of the elements in the matched data set to obtain K hash values corresponding to the element; and mapping the K hash values corresponding to the element to K positions in a binary array whose values are all 0 and updating values of the K positions corresponding to the element to 1, and determining the updated binary array as the Bloom filter lookup table (Higo teaches that andom number(s) (for example, S in FIGS. 4, 14, etc. or, in the case of 1:N authentication, a.sub.i, r.sub.i in FIG. 22 or a.sub.i in FIG. 26), the matching apparatus 140 generates, as a query to verify a degree of mismatch between the second binary vector and the first binary vector, encrypted data (for example, Enc(d.sub.H (X, Y)) in FIGS. 4, 14, etc.) or encrypted data (for example, Enc(a.sub.i (2x.sub.i−1)) and Enc(b.sub.i r.sub.i (2y.sub.i−1)) in FIG. 22 or Enc(a.sub.i b.sub.i (2x.sub.i−1)(2y.sub.i−1)) in FIG. 26) and auxiliary data (for example, hash values H(a.sub.i b.sub.i r.sub.i) in FIG. 22 or hash values H(a.sub.i b.sub.i) in FIG. 26) in the case of 1:N authentication, and transmits the generated query to a verification apparatus as Higo teaches in apr.84).
For claim 21, terminal device comprising: a processor and a memory storing computer program instructions; and the processor implementing, when executing the computer program instructions, the biometric matching method of claim 1 (claim 21 recite commensurate subject matter as claim 1. Therefore, claim 21 is rejected for the same reason set forth for claim 1 above).
For claim 22, A server comprising: a processor and a memory storing computer program instructions; and the processor implementing, when executing the computer program instructions, the biometric matching method of claim 10. (claim 22 recite commensurate subject matter as claim 10. Therefore, claim 22 is rejected for the same reason set forth for claim 10 above).
For claim 24, A computer-readable storage medium storing computer program instructions thereon, the computer program instructions, when executed by a processor, implementing the biometric matching method of claim 1 (claim 24 recite commensurate subject matter as claim 1. Therefore, claim 24 is rejected for the same reason set forth for claim 1 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYUB A MAYE whose telephone number is (571)270-5037. The examiner can normally be reached Monday-Friday 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SHEWAYE GELAGAY can be reached at 571-272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AYUB A MAYE/Examiner, Art Unit 2436 /SHEWAYE GELAGAY/Supervisory Patent Examiner, Art Unit 2436