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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 7 is objected to because of the following informalities:
Claim 7 should be amended as “the decryption device of index i”.
Appropriate correction is required.
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 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because claim 11 is directed to a “computer program product comprising program code instructions” to perform the method of claim 1. Thus, claim 11 is directed to a software program. Software per se is not patent eligible subject matter.
Allowable Subject Matter
Claims 1-10 and 12-14 are allowed.
The claimed invention is directed to comparing biometric data within an encrypted domain to prevent exposure of the biometric data. The process employes pre-computed look-up tables based on prior knowledge of primary and secondary mask data to reduce computational resources and distributed decryption devices to reduce the risk of collusion. See [0010], [0025], [0093]-[0096], & [0109] of the publication of the current patent application (US 2026/0163882).
The most relevant prior arts are the collective non-patent literature works under BASSIT (* cite in the IDS filed 4/18/2026):
BASSIT* et al., “Multiplication-Free Biometric Recognition for Faster Processing under Encryption”, 2022 IEEE International Join Conference on Biometrics (IJCB).
BASSIT* et al., “Fast and Accurate Likelihood Ratio-Based Biometric Verification Secure Against Malicious Adversaries”, IEEE Transactions on Information Forensics and Security, Vol. 6, 2021.
BASSIT et al., "Improved Multiplication-Free Biometric Recognition Under Encryption," in IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 6, no. 3, pp. 314-325, July 2024.
References [A]-[C] disclosed improved similarity measurements between homomorphic encrypted (HE) (i.e., encrypted domain) biometric data. Similar to the claimed invention, look-up tables and summation processing are used over inefficient HE-based solutions that employ a large number of multiplications. See Abstracts of [A]-C], Section III on pg. 316 of [C]. However, references [A]-[C] present distinct methodologies from the overall method presented in independent claim 1. For example, see Figs. 2 of [A] & [C]; Section V & Fig. 3 of [B].
The following prior arts are relevant to the claimed invention and/or teach various features of independent claim 1:
US 2026/0005861: A pre-generated Bloom filter look-up table facilitates determining whether encrypted data belongs to a matched data set. See [0088]-[0089]
US 2023/0403157: A user’s HE biometric information is compared with a third-party computer’s copy of the HE reference to determine a domain vector similarity or distance. See [0036].
US 2022/0131698: Zero-knowledge techniques are used to validate user biometric templates. Biometric templates are encrypted using HE schemes and a similarity metric is computed with capture encrypted biometrics. The similarity metric is masked using a random value. See Abstract; [0103].
US 2021/0141896: A set of measurable encrypted feature vectors can be derived from biometric data where a deep neural network (DNN) can determine matches on encrypted data. See Abstract.
US 2021/0124815: The result of a measurement HE biometric data is determined. The result is masked with a random value to prevent the result from being revealed. See [0055].
US 2020/0014541: A distance measurable for homomorphic encryption enables computations and comparisons on encrypted data without decrypting encrypted feature vectors of biometric data. See Abstract.
US 9,935,947: A biometric template is split into multiple template shares using a polynomial-based secret sharing scheme, such that at least a threshold number of the resulting template shares must be combined to reconstruct the biometric template. See col. 2, lines 51-68.
J. Peeter and R. Veldhuis, “Fast and Accurate Likelihood Radio Based Biometric Comparison in the Encrypted Domain,” arXiv: 1705.09936v1, 28 May 2017, pp. 1-11. (Biometric data is verified in the encrypted domain using elliptic curve based homomorphic ElGamal encryption to prevent a private information leakage. See Abstract.)
J. Bringer, M. Favre, H. Chabanne and A. Patey, "Faster secure computation for biometric identification using filtering," 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, India, 2012, pp. 257-264. (Secure Multi-party Computations (SMC) is applied to biometric identification. A filtering technique based on Hamming distance is used over 1:1 matchings. SMC enables parties to output results to a function (e.g., biometric authentication) without giving information to other parties. See Section I on pg. 257.)
B. Zhao and E. J. Delp, "Secret Sharing in the Encrypted Domain with Secure Comparison," 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, Houston, TX, USA, 2011, pp. 1-5. (A new secure comparison protocol based on the additive homomorphic property and a new scheme of secret sharing in the encrypted domain with secure comparison. See Abstract.)
Y. Dodis, L. Reyzin, A. Smith, "Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data," In: Cachin, C., Camenisch, J.L. (eds) Advances in Cryptology - EUROCRYPT 2004. Lecture Notes in Computer Science, vol 3027. Springer, Berlin, Heidelberg. (Fuzzy extractors enable distance comparison between two similar encrypted datasets, such as an input password w and a stored password w’. See pg. 524-525.)
Conclusion
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/ROBERT B LEUNG/Primary Examiner, Art Unit 2494
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