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 application filed on 03/27/2025.
Claims 1-20 have been examined.
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 abstract idea without significantly more.
Regarding independent claims 1 and 13:
The claims recite a “applying a first compression operation to each of the n data shares, the first compression operation comprising: applying a rounding operation to each of the n data shares, resulting in n integer rounding values (into,---, intn_1); and applying a pseudo-fractional operation, to each of the n data shares, resulting in n pseudo-fractional values (fo,---,fn-1); generating n corrected compressed data shares (yo,n-,y_1) by applying a correction operation to each of the n rounding values, based on then pseudo-fractional values” the limitations recited mathematical concepts/mental steps. These limitations are abstract ideas under Step 2A Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, as described in MPEP § 2106.04(a)(2)(1), and fall within the judicial exception of a mathematical concept. The additional limitations of executing the method on an “processing device” are generic computer components performing conventional functions. These elements do not integrate the abstract idea into a practical application because they do not improve the functioning of the computer, memory, or processor, and merely implement the abstract idea on generic hardware. Therefore, the claims do not recite significantly more than the judicial exception, under Step 2A Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance.
There are no additional elements or limitations in claims 1 and 13 that provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The claimed steps of dividing a data element, storing the divided parts, and applying an XOR function are performed on generic computer hardware. These are considered routine and conventional activities, as described in MPEP § 2106.05(d). Accordingly independent claims 1 and 13 are directed to a judicial exception without significantly more and is not directed to statutory subject matter under 35 USC § 101.
Dependent claims 2-12 and 14-20 also recite additional mathematical operations and do not recite additional limitations that integrates the abstract idea into practical application. These limitations do not provide additional elements sufficient to transform the abstract idea into statutory subject matter. The claims remain directed to an abstract idea implemented on generic hardware. Accordingly dependent claims 2-12 and 14-20 are also directed to a judicial exception without significantly more and is not directed to statutory subject matter under 35 USC § 101.
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.
Claim(s) 1-6, 9-14 and 16-20, are rejected under 35 U.S.C. 103 as being unpatentable over Azouaoui et al (US 2024/0126511), hereinafter Azouaoui in view Fritzmann et al. “Masked Accelerators and Instruction Set Extensions for Post-Quantum Cryptography” published on11/19/2021, ISSN, hereinafter Fritzmann.
Regarding claim 1,
Asouaoui discloses a method comprising:
masking, based on a digital algorithm, by a processing device, a sensitive data item (x), the masking comprising dividing the sensitive data item into a number n of data shares (xo,---, xn_1), n being an integer equal to or greater than 2, such that their arithmetic sum, modulo an integer q associated with the digital algorithm, is equal to a value of the sensitive data item; (para. [0046]-[0049], the function computes the bit-wise AND of two inputs. In a Boolean masking context, if one of the inputs is a constant or a public value, the & operation is applied on each share of the other input independently. [0047] A2B: This function converts n.sub.s arithmetic shares x.sup.(⋅).sup.A ϵ custom-character.sub.q.sup.n.sup.s to n.sub.s Boolean shares x.sup.(⋅).sup.B ϵ custom-character.sub.2.sub.ω.sup.n.sup.s, which encode the same secret value x ϵ custom-character.sub.q. [0048] SecAnd: … a masked AND-operation performed on Boolean shares. [0049] SecOR: a masked OR-operation performed on Boolean shares)
applying a first compression operation to each of the n data shares, the first compression operation comprising:
applying a rounding operation to each of the n data shares, resulting in n integer rounding values (into,---, intn_1); ([050]-[0052] At line 1 of the MaskedCompress function shifts the value of the first share a.sup.(0).sup.A by input mask λ.sub.1. At line 2 the value of the first share a.sup.(0).sup.A is divided by q and further scaled by 2.sup.φ.sup.1.Math.δ and rounded, where δ is a compression factor. A further offset of 2.sup.φ.sup.1.sup.−1 is applied to this first share. Line 3 to 5 implement a loop over all of the remaining shares where each share is divided by q and further scaled by 2.sup.φ.sup.1.Math.δ. Lines 1 to 5 compress the range of the share values to a smaller range); and
applying a [[pseudo-fractional]] operation, to each of the n data shares, resulting in n pseudo-fractional values (fo,---,fn-1); (para. [0050]-[0052], instead of using only two compression parameters, the algorithm is modified to receive five parameters as input. This allows the compression to be controlled much more precisely and adapt it to the different situations that occur …The MaskedCompress function takes arithmetically shared values a.sup.(⋅).sup.A mod q and compression parameters δ, φ.sub.1, φ.sub.2, λ.sub.1, λ.sub.2 as inputs and returns a compressed value in Boolean shares. The parameters δ, φ.sub.1, φ.sub.2, λ.sub.1, λ.sub.2 provide various bounds used in the compression, where λ.sub.1 indicates an input mask, λ.sub.2 indicates an output mask, φ.sub.1 is a masking scaling factor, δ is a first compression factor, and φ.sub.2 is a second compression factor. The output of the MaskedCompress function includes Boolean shared values of the compressed coefficients ā.sup.(⋅).sup.B.)
generating n corrected compressed data shares (yo,n-,y_1) by applying a correction operation to each of the n rounding values, based on then pseudo-fractional values. (para. [0059]-[0062, the compression parameters needed for various cryptographic protocols. Given a modulus q and a rejection bound λ parameter sets (δ, φ.sub.1, φ.sub.2, λ.sub.1, λ.sub.2) for the compression may be found. Note that depending on the case, there can be multiple working parameter sets. …with x ϵcustom-character.sub.q, that maps an input range of size I=λ.Math.2−1 to the same output value. Note that it can be any input interval mapping to any fixed output value and does not necessarily have to already comply with the desired compression function. To reduce the search space, the range of φ.sub.2 to [0, log.sub.2 q] and δ to which compresses the inputs x ϵ custom-character.sub.q according to the desired rejection function, i.e., the values in the range are mapped to 0, all others to a values≠0. The offset λ.sub.2 is found by setting it to the fixed output value of the previous step, and λ.sub.1 may be found either by iterating over all possibilities or deriving it from the number of false negatives (non-rejected inputs mapped to≠0), i.e., set such that it corrects this number to zero false negatives)
While the reference does not explicitly disclose the claimed pseudo-fractional term, it teaches the use of scaled or shifted values and separate handling of shares that function like or equivalent to the claimed pseudo-fractional term. In addition, Fritzmann discloses psudo-fractional term (Section 2.2.3 MaskedCompressq Compressq(x, d) = (2d/q) · x + e- mod 2d , (3) remains correct for a small bounded error e. The reason for this is apparent if we express (2d/q) · x as a binary fraction)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Azouaoui to incorporate the teachings of Fritzmann. The suggestion/motivation for doing so would be to extend masked decoding to masked compression (see Section 2.2.3 Fritzmann)
Regarding claims 2 and 14:
Azouaoui in view of Fritzmann teaches the method of claim 1 as described above. Azouaoui in view of Fritzmann further discloses wherein the arithmetic sum, modulo an integer p associated with the digital algorithm, between the n shares (yo,---,yn_1) corresponds to the sensitive data item (x), compressed, based on a second compression operation (compress_(q, p, r)), associated with the digital algorithm, the second compression operation being based on a calculation of a rounding or truncation value having a form 6ompress,p,r where value q is an integer associated with the digital algorithm, value p is an integer corresponding to a range having a form \{0, 1, ---, p – 1} expected for a result of the second compression operation, and integer value r is a term defining the second compression operation, integer r being: equal to o in response to the second compression operation associated with the digital algorithm being a truncation operation; or equal to [Z] in response to the second compression operation associated with the digital algorithm being a rounding operation. (Azouaoui, para. [0050]-[0053], [0050], [0060]-[0063] A function that compresses values above the high boundary and values below the low boundary to non-zero values, while mapping the inputs in between the high and low boundaries to zero values. The function MaskedCompress illustrated in pseudocode below is a generalization of Algorithm; See also Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claims 3 and 19:
Azouaoui in view of Fritzmann teaches the method of claim 2 as described above. Azouaoui in view of Fritzmann further discloses wherein: the rounding operation, on a share x, of the sensitive data item, corresponds to a calculation of integer into =mod p , where r is a truncation term associated with share x, and L. J is the truncation operation towards an equal or immediately lower integer; and the pseudo-fractional operation, on share x1 corresponds to a calculation of value f1=(xgp+r1)mod q. (Azouaoui [0008]–[0012], [0052]–[0053]; See also Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claim 4:
Azouaoui in view of Fritzmann teaches the method of claim 3 as described above. Azouaoui in view of Fritzmann further discloses wherein a sum of n truncation terms (ro,---,rn_1) is equal to integer r. (Azouaoui [0012], [0052]–[0053]; See also Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claim 5:
Azouaoui in view of Fritzmann teaches the method of claim 4 as described above. Azouaoui in view of Fritzmann further discloses wherein the n truncation terms (ro,---,r_1) are generated by a random number generator of the first device. (Azouaoui [0012], [0052]–[0053]; See also Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claims 6 and 18,
Azouaoui in view of Fritzmann teaches the method of claim 2 as described above. Azouaoui in view of Fritzmann further discloses wherein the correction operation comprises: determining an integer c such that c is equal to value j,je{0,--- n – 1} when a sum of the pseudo-fractional values fo + ---+f_1 belongs to interval [jq, (j + 1)q];generating a correction vector (co,---, cn_1) of size n, such that an arithmetic sum modulo p of n components of the correction vector is equal to integer c; and for each index I e {1, n}, adding an i-th component (c1) of the correction vector to the rounding value int, of an i-th share (x1). (Azouaoui [0005]–[0013], [0050]–[0053], [0058], [0060]–[0063] See also Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above.
Regarding claim 8,
Azouaoui in view of Fritzmann teaches the method of claim 1 as described above. Azouaoui in view of Fritzmann further discloses wherein the digital algorithm is a cryptographic scheme and the processing device is a cryptographic processor or a cryptographic coprocessor (Azouaoui [0005]–[0013], [0050]–[0053], [0058], [0060]–[0063] See also Fritzmann Introduction, Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claims 9 and 17,
Azouaoui in view of Fritzmann teaches the method of claim 8 as described above. Azouaoui in view of Fritzmann further discloses wherein the cryptographic scheme is a lattice-based encapsulation scheme Azouaoui [0005]–[0013], [0050]–[0053], [0058], [0060]–[0063] See also Fritzmann Introduction, Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claim 10,
Azouaoui in view of Fritzmann teaches the method of claim 9 as described above. Azouaoui in view of Fritzmann further discloses wherein the lattice is a lattice of ML-KEM type, a lattice of ML-DSA type, a Kyber-type lattice, or a NewHope-type lattice. (Azouaoui [0005]–[0013], [0050]–[0053], [0058], [0060]–[0063] See also Fritzmann Introduction, Section 2.2.3 MaskedCompressq). The same rationale as claim 1 above applies.
Regarding claims 11 and 20,
Azouaoui in view of Fritzmann teaches the method of claim 8 as described above. Azouaoui in view of Fritzmann further discloses wherein the number n is equal to 2. (Azouaoui [0005]–[0013] general ns share count; Fritzmann 4.1 Masking Keccak). The same rationale as claim 1 above applies.
Regarding claims 12 and 16,
Azouaoui in view of Fritzmann teaches the method of claim 1 as described above. Azouaoui in view of Fritzmann discloses processing, by the processing device, the n corrected compressed data shares (yo, -,Yn-i), as part of a decapsulation operation. (Azouaou [0005]–[0013], [0056], [0058], [0063] (Azouaoui [0013], [0056], [0063], Fritzmann 4.1 Masking Keccak).
Regarding claim 13,
Claim 13 is a device with substantially similar limitations to the method of claim 1, and is rejected under the same rationale.
Claim(s) 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Azouaoui in view of Fritzmann and further in view Socek et al. (US 2019/0045192), hereinafter, Socek.
Regarding claims 7 and 15,
Azouaoui in view of Fritzmann teaches the method of claim 1 as described above. Azouaoui in view of Fritzmann does not explicitly disclose, however, Socek discloses wherein the processing device is configured to control a deleting of the n values as a consequence of the generating the correction vector (para. [0092], [0197], vectors are filtered is described so that noisy matches during motion estimation stage are removed and replaced with more correct motion vectors in respect to the actual motion of the underlying visual objects in the scene). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Azouaoui and Frizmann to incorporate the teachings of Socek. The suggestion/motivation for doing so would be to filter noisy matches (para. [0197], Socek)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Son et al. (US 2025/0307627) directed to data encryption protects the confidentiality of data by encrypting user data. Differential privacy uses statistical techniques to desensitize user data to remove personal information. Data masking protects user data by masking parts of it to hide sensitive information.
Uzun et. al. (US 2023/0289469) directed to each identifier label on the database is converted into t-out-of-T secret shares, wherein the t-out-of-T secret shares are associated with the second set of masked Hamming encoded data, wherein the identifier is only matched if at least t secret shares of the t-out-of-T secret shares are obtained.
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/SHEWAYE GELAGAY/ Supervisory Patent Examiner, Art Unit 2436
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