DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to Applicant’s communication filed on 12/10/2025. Claims 1-9,12,15,18-26 have been examined. Claims 10,11, 13,14,16,17 are cancelled.
Response to Arguments
With regards to 112 2nd rejection, Applicant amendment overcomes the rejection. Therefore, the rejection is withdrawn.
With regards to 101 rejection (Software per se) , Applicant amendment overcomes the rejection. Therefore, the rejection is withdrawn.
With regards to 101 rejection (Abstract), Applicant argues that claim does not recite mathematical concept. The examiner respectfully disagrees.
The claim recites (1 generating a registered vector… 2) generates a registered concealed vector through calculating a product of the registered vector and a registration key matrix by using regular matrix….3) transmit the registered concealed vector..4) generating a matching feature vector.. 5) generate a matching concealed vector through calculating a product of the matching feature vector and a matching key matrix. 6)transmit the matching concealed vector .. 7) match the registered information with the matching information through calculating an inner product of the registered concealed vector and the matching concealed vector.
The examiner checked the published specification and the specification recites in ¶0052-¶0053
“ A registered feature vector generated from registered information is denoted as x, and a matching feature vector generated from matching information is denoted as y. Then, a registered concealed vector t is a product of the registered feature vector x and the registration key matrix A, therefore, t=AT x holds, and a matching concealed vector s is a product of the matching feature vector and the matching key matrix B, therefore, s=Bx holds[..] When matching, an inner product of the registered concealed vector t and the matching concealed vector s is calculated. Considering that the matching key matrix B is the inverse of the registration key matrix A, the inner product of the matching concealed vector and the registered concealed vector matches an inner product of the registered feature vector x and the matching feature vector y, as follows.
(
A
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x
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B
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t
B
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B
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.
Therefore, The limitations of (1) generating, (2) generating, (4) generating , (5) generating, (7) matching, as drafted are series of steps that recite mathematical calculation (e.g. computer programmer can make mathematical calculation to generate concealed vector through calculating product of feature vector and registration key matrix and calculating matching concealed vector through calculating product of the matching feature vector and matching key matrix) . Therefore these steps fall within “the mathematical concepts grouping” of abstract ideas.
Applicant also relied on his argument is that the claimed subject matter is integrated in to a practical application at least in view of “a matching apparatus”, “first transformation apparatus” and “ second transformation apparatus” – See Remarks _ Pages 12 & 13.
The examiner respectfully disagrees.
The terms “ first transformation apparatus ”, “second transformation apparatus” ,“matching apparatus” recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Applicant Argument #1
Applicant argues that Hirano does not explicitly disclose or suggest “generate a matching concealed vector through calculating a product of the matching feature vector and a matching key matrix by using an inverse matrix of the registration key matrix as the matching key matrix.
Examiner response to Applicant’s argument #1
Applicant relied on his argument is that Hirano fails to disclose or suggest “ generate a matching concealed vector through calculating a product of the matching feature vector and a matching key matrix by using an inverse matrix of the registration key matrix as the matching key matrix” – See Remarks – Page 15.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Examiner respectfully disagrees. Takahashi teaches generating matching feature vector through calculating a product of the matching feature vector and the matching key matrix by using a matrix of the registration key matrix as the matching key matrix. Takahashi teaches For a matrix K [i] that is each i-th element of the transform filter array K, its transposed matrix M [i] is calculated, The d-dimensional vector v [i] is transformed with the transposed matrix M [i] to calculate a vector V [i] = v [i] M [i], and each i-th element is replaced with the vector V [i]. Create a conversion feature quantity array V for authentication (Page 15).
Takahashi further teaches that the secret cross-correlation calculation unit 107 converts the basis conversion feature amount array Y on the memory using the conversion filter array K on the memory, and creates a conversion feature amount array V on the memory as the conversion result (Page 9).
Therefore, Takahashi teaches using the transposed matrix of the Key as the matching key matrix to generate the matching concealed vector.
However, Takahashi does not explicitly teach the transposed matrix is a an inverse matrix.
Hirano discloses calculating the encrypted vector based on the first response R received by the first response receiving unit and T pieces of the inverse elements K calculated by the inverse number calculations. (See ¶ 0334- ¶ 00336).
Hirano further teaches the inverse matrix calculation unit 471 calculates the inverse matrix x- 1 of the regular matrix X in the finite field F q, based on the order q which is the part of the public key stored by the public key storage unit 403 and the regular matrix X which is the secret key stored by the secret key storage unit 413( See ¶ 0379).
Therefore, It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the transposed matrix taught by Takahashi to include the inverse matrix taught by Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims 1-9 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) The term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Such claim limitations are
“a first transformation apparatus configured to have ” as recited in claim 1
“ a second transformation apparatus configured to have …." as recited in claim 1
“the first transformation apparatus is further configured to generate ” as recited in claim 1
“the first transformation apparatus is further configured to transmit ” as recited in claim 1
“the second transformation apparatus is further configured to generate ” as recited in claim 1
“the second transformation apparatus is further configured to transmit ” as recited in claim 1
“ the matching apparatus is further configured to match” as recited in claim 1
“ the matching apparatus is further configured to update ” as recited in claim 2
“the first transformation apparatus is further configured to reduce ” as recited in claim 3
“the second transformation apparatus is further configured to update ” as recited in claim 3
“storage device that stores ” as recited in claim 9
A review of the published specification shows that the following appears to be the corresponding structure described in the published specification for the 35 U.S.C 112 (f) or pre-AIA 35 U.S.C 112 sixth paragraph limitation.
“a first transformation apparatus configured to have ” See - ¶0040, ¶0041, ¶ 0049, ¶ 0070 – ¶0075.
“ a second transformation apparatus configured to have …." - ¶0040, ¶0041, ¶ 0049, ¶ 0070 – ¶0075.
“the first transformation apparatus is further configured to generate ” - ¶ 0049, ¶ 0063-0064, ¶ 0070 – ¶0075.
“the first transformation apparatus is further configured to transmit ” - ¶ 0045-¶ 0053, ¶ 0070 – ¶0075.
“the second transformation apparatus is further configured to generate ” ¶ 0045-¶ 0053, ¶ 0070 – ¶0075.
“the second transformation apparatus is further configured to transmit ” ¶ 0045-¶ 0053, ¶ 0070 – ¶0075.
“ the matching apparatus is further configured to match ” ¶ 0045-¶ 0053, ¶ 0070 – ¶0075.
“ the matching apparatus is further configured to update ” ¶ 0059 -¶ 0061, ¶0082-¶ 0087, ¶ 0070 – ¶0075
“the first transformation apparatus is further configured to reduce ” ¶ 0059 -¶ 0061, ¶0082-¶ 0087, ¶ 0070 – ¶0075
“the second transformation apparatus is further configured to update ” ¶ 0059 -¶ 0061, ¶0082-¶ 0087, ¶ 0070 – ¶0075
“storage device that stores ” ¶ 0065, ¶ 0154,¶ 0070 – ¶0075
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1, 12,15 recite “ (1) match registered information with matching information (2) have the registered information input 3) have a matching information input (4)generates a registered feature vector …, (5) generates a registered concealed vector through calculating a product… (6)transmit the concealed vector …; (7) generate a matching feature from the matching information (8) generates a matching concealed vector through calculating a product ; (9) transmit the matching concealed vector to the matching ... (10) matches the register ed information .. through calculating an inner product..
The limitation of (1) matching in claims 1,15 as drafted is a step that recite mental process (e.g. computer programmer compare registered information with a matched information). Therefore this step fall within “ “mental process grouping” of abstract ideas.
The limitations of (4) generating, (5) generating, (7) generating , (8) generating, (10) matching, as drafted are series of steps that recite a mathematical calculation . Therefore these steps fall within “the mathematical concepts grouping” of abstract ideas.
The (2), (3) input limitations in claims 1,15 represent mere data gathering. These limitations do not impose any meaningful limits on the claims. The limitation amounts to necessary data gathering.
The (6), (7) transmitting limitations in claims 1,15 represents mere data gathering. These limitations do not impose any meaningful limits on the claims. The limitation amounts to necessary data gathering.
Therefore, the claims are directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims 1 ,12, 15 recite additional elements–“ first transformation apparatus ”, “second transformation apparatus” and “matching apparatus” and non transitory computer readable medium and matching apparatus as recited in claim 15.
The terms “ first transformation apparatus ”, “second transformation apparatus” ,“matching apparatus”, “non transitory computer readable medium” and recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With regards to claims 2,18,23 the claims recite the limitations “ …update the registered feature vector by multiplying the registered feature vector..”. This limitation recites a concept that falls into “the mathematical concepts grouping” of abstract ideas. “. Therefore, the claims recite an abstract idea.
The term ” matching apparatus” in claim 2 recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With regards to claim 3, the claim recites the limitation “.. reduce a number of registered vectors … update the matching key matrix .”. This limitation recites a concept that falls into “the mathematical concepts grouping” of abstract ideas. Therefore, the claim recites an abstract idea.
The terms “ first transformation apparatus ”, “second transformation apparatus” recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With regards to claims 4,20, 25, the claims recite the limitations “the registered feature vector and the matching feature vector are configured that vectors to each other are orthogonal…”. This limitation recites a concept that falls into “the mathematical concepts grouping” of abstract ideas. Therefore, the claims recite an abstract idea.
With regards to claims 5, 6,21,22,26, the claims recite the limitations “the inner product of the registered feature vector and the matching feature vector is …”. This limitation recites a concept that falls into “the mathematical concepts grouping” of abstract ideas. Therefore, the claim recites an abstract idea.
With regards to claim 7, the claim recites the limitation “the regular matrix is generated …”. This limitation recites a concept that falls into “the mathematical concepts grouping” of abstract ideas. Therefore, the claim recites an abstract idea.
With regards to claim 8, the claim recites the limitations “the first transformation apparatus and second transformation apparatus are united into the device…”.
The limitation of “ the first transformation apparatus and second transformation apparatus are united into the device” recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With regards to claims 9, the claim recites the limitations “matching apparatus comprises a storage device that stores the registered …”.
The limitation “matching apparatus comprises a storage device …” recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer components.
The limitation of storing is just a nominal or tangential addition to the claim , the storing data is also well known. This limitation remains insignificant extra solution activity and not amount to significantly more.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With regards to claims 19,24, the claims recite the limitations “ wherein a number of registered concealed vectors that are generated…updating the matching key matrix ...”. This limitation recite a concept that falls into “the mathematical concepts grouping” of abstract ideas. Therefore, the claims recite an abstract idea.
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 1,2,5,7,9,12,15,18,21,23,26 are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi et al. Publication No. JP 2009-129292 A ( Takahashi hereinafter) in view of Hirano et al. Publication No. US 2013/0318351 A1 ( Hirano hereinafter).
Regarding claim 1,
Takahashi teaches a matching system comprising:
a matching apparatus having a processor configured to match registered information with matching information (Page 2 - The processing device calculates a similarity between the registration feature quantity array and the authentication feature quantity array from the masked cross-correlation function on the storage device, and determines match / mismatch based on the similarity A biometric authentication method – Page 9 - In the server 130, the communication unit 131 receives the authentication vector array V and stores it in the memory, and the secret cross-correlation calculation unit 134 stores the authentication vector array V and the template array T for each i-th element on the memory. The inner product is calculated on Zp, and an array C is created on the memory) .
a first transformation apparatus configured to have the registered information input ( Page 5 – registration conversion unit 106 that converts a quantity to create a registration template – Page 3 - Conversion means for converting the registration feature quantity array using the conversion filter array, and calculating a registration conversion feature quantity array);
a second transformation apparatus configured to have the matching information input ( Page 6 - a secret mutual correlation that performs calculation so as to disclose a cross-correlation function with a mask between feature quantities to the authentication server 130 while keeping the feature quantities secret. It is comprised from the correlation calculation part 107 – Page 15 Create a conversion feature quantity array V for authentication),
wherein the first transformation apparatus is further configured to generate a registered feature vector from the registered information, generate a registered concealed vector through calculating a product of the registered feature vector and the registration key matrix by using a regular matrix selected at random as a registration key matrix (Page 2 - The biometric authentication system according to claim 2, further comprising issuing means for issuing the registration conversion feature quantity array to the authentication server Each element K [i] of the transformation filter array K is a random regular matrix of d × d having a predetermined integer d having a size of 2 or more, The registration feature amount conversion means includes: A basis transformation of the registration feature quantity array to calculate a registration basis transformation array X; For each i-th element X [i] of the registration basis conversion array X, a d-dimensional vector u [i] = () using a predetermined (d-2) -dimensional vector (u3,..., Ud). X[i], 1, u3, ..., ud). An inverse matrix L [i] is calculated for a matrix K [i] that is each i-th element of the transform filter array K, and the d-dimensional vector u [i] is transformed by the inverse matrix L [i]. Then, a vector T [i] = u [i] L [i] is calculated, and a registration conversion feature quantity array T having each i-th element as the vector T [i] is created See Also Pages 5 &6)
transmit the registered concealed vector to the matching apparatus (Page 6 – The registration conversion unit 106 converts the base conversion feature amount array X on the memory using the conversion filter array K on the memory, creates a template array T on the memory as the conversion result, and transmits the template array T to the server 130)
the second transformation apparatus is further configured to generate a matching feature vector from the matching information, generate a matching concealed vector through calculating a product of the matching feature vector and the matching key matrix by using an [..] matrix of the registration key matrix as the matching key matrix (Page 2 The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y, For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, and d-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated - and The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, andd-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated Means for basis transforming the mask function to calculate a basis transform mask array R For a matrix K [i] that is each i-th element of the transform filter array K, its transposed matrix M [i] is calculated The d-dimensional vector v [i] is transformed with the transposed matrix M [i] to calculate a vector V [i] = v [i] M [i], and each i-th element is replaced with the vector V[i]. Create a conversion feature quantity array V for authentication).
transmit the matching concealed vector to the matching apparatus (Page 9 - The secret cross-correlation calculation unit 107 of the client 100 converts the basis conversion feature amount array Y on the memory and the mask filter array R on the memory using the conversion filter array K on the memory, and the conversion result is stored in the memory. An authentication vector array V is created and transmitted to the server 130 (S407).
the matching apparatus is configured to match the registered information and with the matching information through calculating an inner product of the registered concealed vector and the matching concealed vector (Page 2 - The processing device calculates a similarity between the registration feature quantity array and the authentication feature quantity array from the masked cross-correlation function on the storage device, and determines match / mismatch based on the similarity A biometric authentication method – Page 9 - In the server 130, the communication unit 131 receives the authentication vector array V and stores it in the memory, and the secret cross-correlation calculation unit 134 stores the authentication vector array V and the template array T for each i-th element on the memory. The inner product is calculated on Zp, and an array C is created on the memory) .
Takahashi teaches that registered concealed vector uses inverse matrix of the key registration and that the matching concealed vector uses a [..] matrix of registration key (See Page 2). However, Takahashi does not explicitly teach
registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key
Hirano teaches
a registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key (Abstract, ¶ 0129 - The encrypted data generation unit 206 ( encryption unit, comparison ciphertext generation unit) encrypts the feature vector formed by the feature vector forming device 204, based on the random numbers generated by the random number generation unit 205, thereby generating encrypted biometric information ( encrypted feature vector), using the processing device 911 – ¶0185 – ¶0186 – The encrypted data generation unit 206 of the registration apparatus 104 generates an encrypted feature vector C, based on the read public key pk and the random numbers generated by the random number generation unit 205, using the processing device 911. The encrypted feature vector C is the one obtained by encrypting the feature vector b. ¶0230 – ¶0231 - The inverse elements in the multiplication of the integers modulo q can be readily calculated. Accordingly, when the random number x, is known, the inverse element x,-1 can be readily calculated. Consequently, it is easy to calculate the encrypted feature vector C' from the first response. However, when the random number x, is not known, it is virtually impossible to calculate the encrypted feature vector C' from the first response. That is, the random numbers as the plaintexts generated by the random number generation unit 303 in the first challenge generation step S701 constitute a secret key (temporary secret key) known by the authentication apparatus 102 alone – ¶0336 - Using the processing device 911, the scalar multiplication calculation unit 352 calculates the encrypted feature vector C', based on the first response R' received by the first response receiving unit 331 and T pieces of the inverse elements K, calculated by the inverse number calculation unit – See ¶0379).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Regarding claim 2,
Takahashi further teaches
wherein the matching apparatus is configured to update the registered feature vector by multiplying the registered feature vector by another regular matrix selected at random to enable matching using a new matching key matrix (Page 8 - The conversion filter generation unit 103 of the client 100 generates a conversion filter array K having the same size N as the basis conversion feature amount vector X on the memory (S204). As shown in FIG. 7B, each element K [i] (i = 0 to N-1) of the conversion filter array K701 is randomly generated as ad x d regular matrix on Zp and sequentially stored in the memory. dis a predetermined integer of 2 or more. For example, when d = 3, each element of a 3 x 3 matrix is randomly determined on Zp as in the following equation, and is checked to be regular (the determinant is non-zero). What is necessary is just to repeat generating randomly. Similar to the first embodiment, the random number series can be realized by giving an appropriate seed value to the pseudo-random number generator - Page 3 - When a single user repeats authentication, a simultaneous equation regarding each pixel value of Y can be similarly established from the relationship between data transmitted to the server. Specifically, for example, authentication is repeated m times, and feature amount images extracted in each authentication are denoted by yl, y2,. Assuming that the basis conversion images for the reverse sorting of the feature amount images are Yl, Y2, ... , Ym, the data transmitted to the server is Vl = Yl x K, V2 = Y2 x K,. x K, ... Therefore, the server can calculate Vl / V2, Vl / V3, ... , Vl / Vm. By the way, Vl /Vi= Yl / Yi, which can be regarded as a simultaneous equation in which the left side is a known constant and the right side is an unknown variable).
Regarding claim 5,
Takahashi does not explicitly teach
the inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information.
However, Hirano teaches
the inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information (¶0402 - The similarity degreed is the square of the Euclidean distance between the two feature vectors b and b'. Thus, it indicates that the smaller the similarity degree dis, the more the two feature vectors b and b' are similar. To take an example, the determination unit 306 compares the similarity degree d with a predetermined threshold value da, and then determines that the two feature vectors b and b' are similar when the similarity degree d is smaller than the threshold value d0– See Also (¶0383, (¶0969)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Regarding claim 7,
Takahashi teaches regular matrix ( Page 8 – regular matrix) However, Takahashi does not explicitly teach
wherein the regular matrix is generated by excluding non-regular matrices from square matrices in which random numbers of a square of a given natural number n are assigned to each element
Hirano teaches
wherein the regular matrix is generated by excluding non-regular matrices from square matrices in which random numbers of a square of a given natural number n are assigned to each element (¶ 0276 - When the determinant IXI calculated by the determinant calculation unit 424 is not 0, the regular matrix setting unit 425 sets the square matrix X generated by the determinant calculation unit 424 to a regular matrix X, using the processing device 911 – Para 0288 -When the determinant IXI calculated by the matrix calculation unit 424 is not 0, the regular matrix setting unit 425 sets the square matrix X as the regular matrix X, using the processing device 911. The secret key storage unit 413 stores the regular matrix X set by the regular matrix setting unit 425 as the secret key sk, using the storage device 914. Para 0252 – Para 0254 - Assume that "X is an n-dimensional i-row, j-colunm square matrix having elements of n2 values of xi/ ( each of i, j being each integer not less than 1 and less than n) uniform randomly selected from the finite field F q· When the value of q is sufficiently large, the square matrix X will be a regular matrix at a very high probability).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶0014).
Regarding claim 9,
Takahashi further teaches
wherein the matching apparatus comprises a storage device that stores the registered concealed vector received from the first transformation apparatus (Page 5 – The server 130 includes a communication unit 131 that communicates with the client 100, a database 133 that manages templates, a registration unit that registers a template received from the client in the database 133).
Regarding claim 12,
Takahashi teaches a matching method for matching registered information with matching information, by a matching apparatus, a first transformation apparatus and a second transformation apparatus (Page 4), comprising
generating by the first transformation apparatus, a registered feature vector from the registered information, generate by the first transformation apparatus, a registered concealed vector through calculating a product of the registered feature vector and the registration key matrix by using a regular matrix selected at random as a registration key matrix (Page 2 - The biometric authentication system according to claim 2, further comprising issuing means for issuing the registration conversion feature quantity array to the authentication server Each element K [i] of the transformation filter array K is a random regular matrix of d × d having a predetermined integer d having a size of 2 or more, The registration feature amount conversion means includes: A basis transformation of the registration feature quantity array to calculate a registration basis transformation array X; For each i-th element X [i] of the registration basis conversion array X, a d-dimensional vector u [i] = () using a predetermined (d-2) -dimensional vector (u3,..., Ud). X[i], 1, u3, ..., ud). An inverse matrix L [i] is calculated for a matrix K [i] that is each i-th element of the transform filter array K, and the d-dimensional vector u [i] is transformed by the inverse matrix L [i]. Then, a vector T [i] = u [i] L [i] is calculated, and a registration conversion feature quantity array T having each i-th element as the vector T [i] is created See Also Pages 5 &6)
generate by the second transformation apparatus, a matching feature vector from the matching information , generating by the second transformation apparatus, a matching concealed vector through calculating a product of the matching feature vector and a matching key matrix by using an [..] matrix of the registration key matrix as the matching key matrix, and matching by the matching apparatus, the registered information with the matching information through calculating an inner product of the registered concealed vector and the matching concealed vector (Page 2 The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y, For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, and d-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated - and The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, andd-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated Means for basis transforming the mask function to calculate a basis transform mask array R For a matrix K [i] that is each i-th element of the transform filter array K, its transposed matrix M [i] is calculated The d-dimensional vector v [i] is transformed with the transposed matrix M [i] to calculate a vector V [i] = v [i] M [i], and each i-th element is replaced with the vector V[i]. Create a conversion feature quantity array V for authentication).
Takahashi teaches that registered concealed vector uses inverse matrix of the key registration and that the matching concealed vector uses a [..] matrix of registration key (See Page 2). However, Takahashi does not explicitly teach
a registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key
Hirano teaches
a registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key (Abstract, ¶ 0129 - The encrypted data generation unit 206 ( encryption unit, comparison ciphertext generation unit) encrypts the feature vector formed by the feature vector forming device 204, based on the random numbers generated by the random number generation unit 205, thereby generating encrypted biometric information ( encrypted feature vector), using the processing device 911 – ¶0185 – ¶0186 – The encrypted data generation unit 206 of the registration apparatus 104 generates an encrypted feature vector C, based on the read public key pk and the random numbers generated by the random number generation unit 205, using the processing device 911. The encrypted feature vector C is the one obtained by encrypting the feature vector b. ¶0230 – ¶0231 - The inverse elements in the multiplication of the integers modulo q can be readily calculated. Accordingly, when the random number x, is known, the inverse element x,-1 can be readily calculated. Consequently, it is easy to calculate the encrypted feature vector C' from the first response. However, when the random number x, is not known, it is virtually impossible to calculate the encrypted feature vector C' from the first response. That is, the random numbers as the plaintexts generated by the random number generation unit 303 in the first challenge generation step S701 constitute a secret key (temporary secret key) known by the authentication apparatus 102 alone – ¶0336 - Using the processing device 911, the scalar multiplication calculation unit 352 calculates the encrypted feature vector C', based on the first response R' received by the first response receiving unit 331 and T pieces of the inverse elements K, calculated by the inverse number calculation unit -See Also ¶0379).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Regarding claim 15,
Takahashi teaches a non-transitory computer readable medium storing a matching program executed by a matching apparatus that matches registered information and matching information, first transformation apparatus into which the registered information is input, and a second transformation apparatus into which the matching information is input, comprising; (Page 4),
generating a registered feature vector from the registered information by the first transformation apparatus, generating, by the first transformation apparatus, a registered concealed vector through calculating a product of the registered feature vector and the registration key matrix by using a regular matrix selected at random as a registration key matrix (Page 2 - The biometric authentication system according to claim 2, further comprising issuing means for issuing the registration conversion feature quantity array to the authentication server Each element K [i] of the transformation filter array K is a random regular matrix of d × d having a predetermined integer d having a size of 2 or more, The registration feature amount conversion means includes: A basis transformation of the registration feature quantity array to calculate a registration basis transformation array X; For each i-th element X [i] of the registration basis conversion array X, a d-dimensional vector u [i] = () using a predetermined (d-2) -dimensional vector (u3,..., Ud). X[i], 1, u3, ..., ud). An inverse matrix L [i] is calculated for a matrix K [i] that is each i-th element of the transform filter array K, and the d-dimensional vector u [i] is transformed by the inverse matrix L [i]. Then, a vector T [i] = u [i] L [i] is calculated, and a registration conversion feature quantity array T having each i-th element as the vector T [i] is created See Also Pages 5 &6)
transmitting, by the first transformation apparatus, the registered concealed vector to the matching apparatus (Page 6 – The registration conversion unit 106 converts the base conversion feature amount array X on the memory using the conversion filter array K on the memory, creates a template array T on the memory as the conversion result, and transmits the template array T to the server 130)
generating by the second transformation apparatus, a matching feature vector from the matching information, generates, by the second transformation apparatus, a matching concealed vector through calculating a product of the matching feature vector and the matching key matrix by using an [..] matrix of the registration key matrix as the matching key matrix (Page 2 The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y, For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, and d-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated - and The authentication terminal side secret cross-correlation calculation means comprises Sorting the authentication feature quantity array in reverse order, basis conversion to calculate the authentication basis conversion array Y For each i-th element Y [i] of the authentication basis transformation array Y, vectors (v3,..., Vd) orthogonal to the (d-2) dimension vector (u3,..., Ud). ) At random, andd-dimensional vector v [i] = (Y [i], R [i], v3,..., Vd) is generated Means for basis transforming the mask function to calculate a basis transform mask array R For a matrix K [i] that is each i-th element of the transform filter array K, its transposed matrix M [i] is calculated The d-dimensional vector v [i] is transformed with the transposed matrix M [i] to calculate a vector V [i] = v [i] M [i], and each i-th element is replaced with the vector V[i]. Create a conversion feature quantity array V for authentication).
Transmitting, by the second transformation apparatus, the matching concealed vector to the matching apparatus (Page 9 - The secret cross-correlation calculation unit 107 of the client 100 converts the basis conversion feature amount array Y on the memory and the mask filter array R on the memory using the conversion filter array K on the memory, and the conversion result is stored in the memory. An authentication vector array V is created and transmitted to the server 130 (S407).
matching, by the matching apparatus, the registered information and with the matching information through calculating an inner product of the registered concealed vector and the matching concealed vector by the matching apparatus (Page 2 - The processing device calculates a similarity between the registration feature quantity array and the authentication feature quantity array from the masked cross-correlation function on the storage device, and determines match / mismatch based on the similarity A biometric authentication method – Page 9 - In the server 130, the communication unit 131 receives the authentication vector array V and stores it in the memory, and the secret cross-correlation calculation unit 134 stores the authentication vector array V and the template array T for each i-th element on the memory. The inner product is calculated on Zp, and an array C is created on the memory) .
Takahashi teaches that registered concealed vector uses inverse matrix of the key registration and that the matching concealed vector uses a [..] matrix of registration key (See Page 2). However, Takahashi does not explicitly teach
a registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key
Hirano teaches
a registered concealed vector uses matrix of the key registration and that the matching concealed vector uses an inverse matrix of registration key (Abstract, ¶ 0129 - The encrypted data generation unit 206 ( encryption unit, comparison ciphertext generation unit) encrypts the feature vector formed by the feature vector forming device 204, based on the random numbers generated by the random number generation unit 205, thereby generating encrypted biometric information ( encrypted feature vector), using the processing device 911 – ¶0185 – ¶0186 – The encrypted data generation unit 206 of the registration apparatus 104 generates an encrypted feature vector C, based on the read public key pk and the random numbers generated by the random number generation unit 205, using the processing device 911. The encrypted feature vector C is the one obtained by encrypting the feature vector b. ¶0230 – ¶0231 - The inverse elements in the multiplication of the integers modulo q can be readily calculated. Accordingly, when the random number x, is known, the inverse element x,-1 can be readily calculated. Consequently, it is easy to calculate the encrypted feature vector C' from the first response. However, when the random number x, is not known, it is virtually impossible to calculate the encrypted feature vector C' from the first response. That is, the random numbers as the plaintexts generated by the random number generation unit 303 in the first challenge generation step S701 constitute a secret key (temporary secret key) known by the authentication apparatus 102 alone – ¶0336 - Using the processing device 911, the scalar multiplication calculation unit 352 calculates the encrypted feature vector C', based on the first response R' received by the first response receiving unit 331 and T pieces of the inverse elements K, calculated by the inverse number calculation unit -See Also ¶0379).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Regarding claim 18,
Takahashi further teaches
wherein the registered feature vector is updated by multiplying the registered feature vector by another regular matrix selected at random to enable matching using a new matching key matrix. (Page 8 - The conversion filter generation unit 103 of the client 100 generates a conversion filter array K having the same size N as the basis conversion feature amount vector X on the memory (S204). As shown in FIG. 7B, each element K [i] (i = 0 to N-1) of the conversion filter array K701 is randomly generated as ad x d regular matrix on Zp and sequentially stored in the memory. dis a predetermined integer of 2 or more. For example, when d = 3, each element of a 3 x 3 matrix is randomly determined on Zp as in the following equation, and is checked to be regular (the determinant is non-zero). What is necessary is just to repeat generating randomly. Similar to the first embodiment, the random number series can be realized by giving an appropriate seed value to the pseudo-random number generator - Page 3 - When a single user repeats authentication, a simultaneous equation regarding each pixel value of Y can be similarly established from the relationship between data transmitted to the server. Specifically, for example, authentication is repeated m times, and feature amount images extracted in each authentication are denoted by yl, y2,. Assuming that the basis conversion images for the reverse sorting of the feature amount images are Yl, Y2, ... , Ym, the data transmitted to the server is Vl = Yl x K, V2 = Y2 x K,. x K, ... Therefore, the server can calculate Vl / V2, Vl / V3, ... , Vl / Vm. By the way, Vl /Vi= Yl / Yi, which can be regarded as a simultaneous equation in which the left side is a known constant and the right side is an unknown variable).
Regarding claim 21,
Takahashi does not explicitly teach
the inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information.
However, Hirano teaches
The inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information. (¶0402 - The similarity degreed is the square of the Euclidean distance between the two feature vectors b and b'. Thus, it indicates that the smaller the similarity degree dis, the more the two feature vectors b and b' are similar. To take an example, the determination unit 306 compares the similarity degree d with a predetermined threshold value da, and then determines that the two feature vectors b and b' are similar when the similarity degree d is smaller than the threshold value d0– See Also (¶0383, (¶0969)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶0014).
Regarding claim 23,
Takahashi further teaches
wherein the matching apparatus updates the registered feature vector by multiplying the registered feature vector by another regular matrix selected at random to enable matching using a new matching key matrix (Page 8 -
The conversion filter generation unit 103 of the client 100 generates a conversion filter array K having the same size N as the basis conversion feature amount vector X on the memory (S204). As shown in FIG. 7B, each element K [i] (i = 0 to N-1) of the conversion filter array K701 is randomly generated as ad x d regular matrix on Zp and sequentially stored in the memory. dis a predetermined integer of 2 or more. For example, when d = 3, each element of a 3 x 3 matrix is randomly determined on Zp as in the following equation, and is checked to be regular (the determinant is non-zero). What is necessary is just to repeat generating randomly. Similar to the first embodiment, the random number series can be realized by giving an appropriate seed value to the pseudo-random number generator - Page 3 - When a single user repeats authentication, a simultaneous equation regarding each pixel value of Y can be similarly established from the relationship between data transmitted to the server. Specifically, for example, authentication is repeated m times, and feature amount images extracted in each authentication are denoted by yl, y2,. Assuming that the basis conversion images for the reverse sorting of the feature amount images are Yl, Y2, ... , Ym, the data transmitted to the server is Vl = Yl x K, V2 = Y2 x K,. x K, ... Therefore, the server can calculate Vl / V2, Vl / V3, ... , Vl / Vm. By the way, Vl /Vi= Yl / Yi, which can be regarded as a simultaneous equation in which the left side is a known constant and the right side is an unknown variable).
Regarding claim 26,
Takahashi does not explicitly teach
The inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information.
However, Hirano teaches
inner product of the registered feature vector and the matching feature vector is a squared Euclidean distance between the registered information and the matching information (¶0402 - The similarity degreed is the square of the Euclidean distance between the two feature vectors b and b'. Thus, it indicates that the smaller the similarity degree dis, the more the two feature vectors b and b' are similar. To take an example, the determination unit 306 compares the similarity degree d with a predetermined threshold value da, and then determines that the two feature vectors b and b' are similar when the similarity degree d is smaller than the threshold value d0– See Also (¶0383, (¶0969)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Hirano. The motivation for doing so is to allow system to calculate a similarity degree between data that is kept encrypted while preventing leakage of information on original data and information to be used for spoofing (Hirano – ¶ 0014).
Claims 3,19,24 are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Hirano further in view of Onishi et al. Publication JP 2018128736 A ( Onishi hereinafter).
Regarding claim 3,
Takahashi does not explicitly teach
wherein the first transformation apparatus is configured to reduce a number of registered concealed vectors that are generated using the same registration key matrix to less than a predetermined natural number n, the second transformation apparatus is configured to prior to a number of matchings using the same matching key matrix exceeding the predetermined natural number n, update the matching key matrix
However, Onishi teaches
first transformation apparatus is configured to reduce a number of registered concealed vectors that are generated using the same registration key matrix to less than a predetermined natural number n, the second transformation apparatus is configured to prior to a number of matchings using the same matching key matrix exceeding the predetermined natural number n, update the matching key matrix (Page 3 - the count value of the registered template is counted on the condition that the single authentication in which the combination of the input face data authenticated as the face of the same person and the registered template becomes one set is established. Further, in this room entrance management system, the count value of the candidate template is counted on condition that the authentication between the input face data and the candidate template is established. Then, when a predetermined condition such as the number of authentication processes reaches the specified value for each registered person, the registered template with the smallest count value is targeted for deletion, and the candidate template with the largest count value is updated. Update the registered template as the target – Page 4 - when the number of authentication processes reaches the specified value, the face template update unit 115 sets the registration template 121A having the smallest count value 123A as the deletion target and the candidate template 121B having the largest count value 123B as the update target. It also functions as an update processing unit that performs update processing. Further, the face template update unit 115 updates the registration template 121A when the candidate template 121B is empty because the candidate template 121B has been updated as the registration
template 121A – Page 10 - the registration template 121A that is less frequently matched with the input face image can be deleted and a new registration template 121A can be added, so that the configuration of the registration template 121A can be optimized )
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Onishi. The motivation for doing so is to allow system to optimize the configuration of the registration template to respond to changes in various condition ( Page 10 – Onishi).
Regarding claim 19,
Takahashi does not explicitly teach
wherein a number of registered concealed vectors that are generated using the same registration key matrix is less than a predetermined natural number n, updating the matching key matrix prior to a number of matchings using the same matching key matrix exceeding the predetermined natural number n
However, Onishi teaches
wherein a number of registered concealed vectors that are generated using the same registration key matrix is less than a predetermined natural number n, updating the matching key matrix prior to a number of matchings using the same matching key matrix exceeding the predetermined natural number n (Page 3 - the count value of the registered template is counted on the condition that the single authentication in which the combination of the input face data authenticated as the face of the same person and the registered template becomes one set is established. Further, in this room entrance management system, the count value of the candidate template is counted on condition that the authentication between the input face data and the candidate template is established. Then, when a predetermined condition such as the number of authentication processes reaches the specified value for each registered person, the registered template with the smallest count value is targeted for deletion, and the candidate template with the largest count value is updated. Update the registered template as the target – Page 4 - when the number of authentication processes reaches the specified value, the face template update unit 115 sets the registration template 121A having the smallest count value 123A as the deletion target and the candidate template 121B having the largest count value 123B as the update target. It also functions as an update processing unit that performs update processing. Further, the face template update unit 115 updates the registration template 121A when the candidate template 121B is empty because the candidate template 121B has been updated as the registration template 121A – Page 10 - the registration template 121A that is less frequently matched with the input face image can be deleted and a new registration template 121A can be added, so that the configuration of the registration template 121A can be optimized )
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Onishi. The motivation for doing so is to allow system to optimize the configuration of the registration template to respond to changes in various condition ( Page 10 – Onishi).
Regarding claim 24,
Takahashi does not explicitly teach
wherein a number of registered concealed vectors that are generated using the same registration key matrix is less than a predetermined natural number n, the matching key matrix is updated so that a number of times of matching that uses the same matching key matrix does not exceed the predetermined natural number n.
However, Onishi teaches
wherein a number of registered concealed vectors that are generated using the same registration key matrix is less than a predetermined natural number n, the matching key matrix is updated so that a number of times of matching that uses the same matching key matrix does not exceed the predetermined natural number n.(Page 3 - the count value of the registered template is counted on the condition that the single authentication in which the combination of the input face data authenticated as the face of the same person and the registered template becomes one set is established. Further, in this room entrance management system, the count value of the candidate template is counted on condition that the authentication between the input face data and the candidate template is established. Then, when a predetermined condition such as the number of authentication processes reaches the specified value for each registered person, the registered template with the smallest count value is targeted for deletion, and the candidate template with the largest count value is updated. Update the registered template as the target – Page 4 - when the number of authentication processes reaches the specified value, the face template update unit 115 sets the registration template 121A having the smallest count value 123A as the deletion target and the candidate template 121B having the largest count value 123B as the update target. It also functions as an update processing unit that performs update processing. Further, the face template update unit 115 updates the registration template 121A when the candidate template 121B is empty because the candidate template 121B has been updated as the registration template 121A – Page 10 - the registration template 121A that is less frequently matched with the input face image can be deleted and a new registration template 121A can be added, so that the configuration of the registration template 121A can be optimized )
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Onishi. The motivation for doing so is to allow system to optimize the configuration of the registration template to respond to changes in various condition ( Page 10 – Onishi).
Claims 4,20,25 are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Hirano further in view of Kenji et al. JP 3729581 B2 ( Kenji hereinafter)
Regarding claim 4,
Takahashi further teaches
wherein the registered feature vector and the matching feature vector are configured that vectors [..] are incorporated into respective corresponding elements (Page 4- The filter K read out from the above is applied, V [i] = Y [i] x K [i] is calculated for each i-th pixel, and a converted image Vis created and transmitted to the server. The server calculates C [i] = T [i] x V [i] (= X [i] x Y [i]) for each pixel, and inverse basis transform (inverse Fourier transform or inverse number transform) of image C) So that the cross-correlation function of x and y Calculate The similarity between x and y is calculated from this cross correlation function, and a match/ mismatch is determined. Page – 10 - the server performs the cross-correlation function only in the four corner areas where the mask array r is O (see FIG. 8A). [ 1] You can know the value of. The four corner regions correspond to cross-correlation values when the feature amount arrays x and y are overlapped).
However, Takahashi does not explicitly teach that vectors orthogonal to each other
Kenji teaches
vectors orthogonal to each other (¶0011 - the weighted average covariance C .sub.s and model vector covariance C .sub.m are diagonalized using the diagonalization means 1 and 2, and feature extraction is performed using the obtained matrix H, whereby the model pattern is obtained. Can be controlled so that the change of the input pattern from is orthogonal to the space occupied by the model set. As a result, even when the difference between the model and the input pattern is large, the model corresponding to the input is correctly matched by ignoring the feature in the direction orthogonal to the space occupied by the model in the final process of recognition and matching – ¶0022 - selecting a feature space is selecting K orthogonal coordinate axes (vectors) constituting such a K dimensional space, and thus feature extraction is a linear transformation Matrix). For this purpose, the space of the model pattern and the space of the variation vector are orthogonalized by transformation that diagonalizes the matrix C .sub.s and the model vector covariance C .sub.m simultaneously).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Kenji. The motivation for doing so is to allow system to ensure that the information from different biometric traits is independent, or uncorrelated, maximizing the reliability and accuracy of an authentication system.
Regarding claim 20,
Takahashi further teaches
wherein the registered feature vector and the matching feature vector are configured that vectors [..] are incorporated into respective corresponding elements (Page 4- The filter K read out from the above is applied, V [i] = Y [i] x K [i] is calculated for each i-th pixel, and a converted image Vis created and transmitted to the server. The server calculates C [i] = T [i] x V [i] (= X [i] x Y [i]) for each pixel, and inverse basis transform (inverse Fourier transform or inverse number transform) of image C) So that the cross-correlation function of x and y Calculate The similarity between x and y is calculated from this cross correlation function, and a match/ mismatch is determined. Page – 10 - the server performs the cross-correlation function only in the four corner areas where the mask array r is O (see FIG. 8A). [ 1] You can know the value of. The four corner regions correspond to cross-correlation values when the feature amount arrays x and y are overlapped).
However, Takahashi does not explicitly teach that vectors orthogonal to each other
Kenji teaches
vectors orthogonal to each other (¶0011 - the weighted average covariance C .sub.s and model vector covariance C .sub.m are diagonalized using the diagonalization means 1 and 2, and feature extraction is performed using the obtained matrix H, whereby the model pattern is obtained. Can be controlled so that the change of the input pattern from is orthogonal to the space occupied by the model set. As a result, even when the difference between the model and the input pattern is large, the model corresponding to the input is correctly matched by ignoring the feature in the direction orthogonal to the space occupied by the model in the final process of recognition and matching – ¶0022 - selecting a feature space is selecting K orthogonal coordinate axes (vectors) constituting such a K dimensional space, and thus feature extraction is a linear transformation Matrix). For this purpose, the space of the model pattern and the space of the variation vector are orthogonalized by transformation that diagonalizes the matrix C .sub.s and the model vector covariance C .sub.m simultaneously).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Kenji. The motivation for doing so is to allow system to ensure that the information from different biometric traits is independent, or uncorrelated, maximizing the reliability and accuracy of an authentication system.
Regarding claim 25,
Takahashi further teaches
wherein the registered feature vector and the matching feature vector are configured that vectors [..] are incorporated into respective corresponding elements (Page 4- The filter K read out from the above is applied, V [i] = Y [i] x K [i] is calculated for each i-th pixel, and a converted image Vis created and transmitted to the server. The server calculates C [i] = T [i] x V [i] (= X [i] x Y [i]) for each pixel, and inverse basis transform (inverse Fourier transform or inverse number transform) of image C) So that the cross-correlation function of x and y Calculate The similarity between x and y is calculated from this cross correlation function, and a match/ mismatch is determined. Page – 10 - the server performs the cross-correlation function only in the four corner areas where the mask array r is O (see FIG. 8A). [ 1] You can know the value of. The four corner regions correspond to cross-correlation values when the feature amount arrays x and y are overlapped).
However, Takahashi does not explicitly teach that vectors orthogonal to each other
Kenji teaches
vectors orthogonal to each other (¶0011 - the weighted average covariance C .sub.s and model vector covariance C .sub.m are diagonalized using the diagonalization means 1 and 2, and feature extraction is performed using the obtained matrix H, whereby the model pattern is obtained. Can be controlled so that the change of the input pattern from is orthogonal to the space occupied by the model set. As a result, even when the difference between the model and the input pattern is large, the model corresponding to the input is correctly matched by ignoring the feature in the direction orthogonal to the space occupied by the model in the final process of recognition and matching – ¶0022 - selecting a feature space is selecting K orthogonal coordinate axes (vectors) constituting such a K dimensional space, and thus feature extraction is a linear transformation Matrix). For this purpose, the space of the model pattern and the space of the variation vector are orthogonalized by transformation that diagonalizes the matrix C .sub.s and the model vector covariance C .sub.m simultaneously).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Kenji. The motivation for doing so is to allow system to ensure that the information from different biometric traits is independent, or uncorrelated, maximizing the reliability and accuracy of an authentication system.
Claims 6,22 are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Hirano further in view of Volkovs et al. Publication No. US 2020/0159766 A1 ( Volkovs hereinafter)
Regarding claim 6,
Takahashi further teaches
inner product of the registered feature vector and the matching feature vector is a score of the registered information and the matching information [..] (Page 9 - For each i-th element of the conversion feature quantity array T for registration and the conversion feature quantity array V for authentication, the inner product value C [i] = T [i] · V [i] (= X [i] × Y [ i] + R [i]), and calculates a basis conversion correlation array C with each i-th element as the inner product value C [i],. The inner product is calculated on Zp, and an array C is created on the memory (S408). From Equations 10 and 11).
However, Takahashi does not explicitly teach that the score obtained by referring to a score table
Volkovs teaches
score obtained by referring to a score table (Para 0023 - graph generation module 120 evaluates similarity 210 by determining a similarity score between the subject image and other images in the image repository. In one embodiment, the similarity score is an inner product between the vector representation of the subject image and the vector representation of the other image. The inner product may be a dot product or may be another vector similarity function for comparing similarity between vectors. As a working example, a similarity score table 250 shows example similarity scores calculated for image A with respect to other images, including images B, C, D).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Volkovs. The motivation for doing so is to allow system to speed up operations by avoiding redundant computations.
Regarding claim 22,
Takahashi further teaches
an inner product of the registered feature vector and the matching feature vector is a score of the registered information and the matching information [..] (Page 9 - For each i-th element of the conversion feature quantity array T for registration and the conversion feature quantity array V for authentication, the inner product value C [i] = T [i] · V [i] (= X [i] × Y [ i] + R [i]), and calculates a basis conversion correlation array C with each i-th element as the inner product value C [i],. The inner product is calculated on Zp, and an array C is created on the memory (S408). From Equations 10 and 11).
However, Takahashi does not explicitly teach that the score obtained by referring to a score table
Volkovs teaches
score obtained by referring to a score table (Para 0023 - graph generation module 120 evaluates similarity 210 by determining a similarity score between the subject image and other images in the image repository. In one embodiment, the similarity score is an inner product between the vector representation of the subject image and the vector representation of the other image. The inner product may be a dot product or may be another vector similarity function for comparing similarity between vectors. As a working example, a similarity score table 250 shows example similarity scores calculated for image A with respect to other images, including images B, C, D).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Volkovs. The motivation for doing so is to allow system to speed up operations by avoiding redundant computations.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Hirano further in view of Jhanji et al. Publication No. US 2018/0027070 A1 ( Jhanji hereinafter)
Regarding claim 8,
Takahashi further teaches
wherein the first transformation apparatus and the second transformation apparatus ( Page 3 - In a system in which a client and a server are connected via a network, when the server performs biometric authentication for a user on the client side, the server typically holds a template. The client acquires the biometric information of the user at the time of authentication, extracts the feature value and transmits it to the server, and the server checks the feature value against the template to determine whether or not the user is the person)
However Takahashi does not explicitly teach that the first apparatus and second apparatus are united into one device.
Jhanji teaches
the first apparatus and second apparatus are united into one device (¶0082 - The same device can function as client, or server, or both)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Takahashi to include the teachings of Jhanji. The motivation for doing so is to allow system to offer cost savings by eliminating the need for extra hardware and simplified inter-application security since the communication doesn't traverse a network.
Conclusion
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/YOUNES NAJI/Primary Examiner, Art Unit 2445