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 .
Information Disclosure Statement
The 7/7/2025 IDS document has been considered by the examiner. It is noted that the “Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network” NPL has been split up into multiple documents in the image file wrapper.
Response to Amendment / Arguments
Regarding objections to minor informalities:
Applicant’s amendment has overcome the objections. As such, the objections have been withdrawn.
Regarding claims rejected under 35 USC 103:
Applicant’s arguments have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lee (“Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network”).
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, 4-8, 11-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (“Approximate Homomorphic Encryption with Reduced Approximation Error”) in view of Lee (“Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network” [Applicant’s IDS]).
Regarding claim 1, Kim discloses: A computer-implemented method for controlling scale values in fully-homomorphic encryption schemes (i.e., CKKS as in Kim), the method comprising:
obtaining a series of prime numbers of a modulus chain of a fully-homomorphic encryption scheme;
Refer to at least paragraph 3 on page 2, bullet 2 on page 4, and to the last paragraph on page 4 of Kim with respect to selecting a chain of primes q1,q2,…,qn. As per the cited portions of Kim, a software library such as HEAAN is used for obtaining the series of primes.
Note: Applicant’s disclose teaches the same library in [0054] of the instant specification.
initializing a ciphertext scale at a highest level of the modulus chain to a value that ensures ciphertext scales of levels lower than the highest level of the modulus chain are within a range of a highest prime number and a lowest prime number of the modulus chain;
Refer to at least the second paragraph on page 3, bullet 2 on page 4, the table on page 17, and pages 18-20 of Kim with respect to choosing the primes to avoid divergence of scaling factors.
calculating all ciphertext scales below the highest level of the modulus chain based on the initialized ciphertext scale for the highest level of the modulus chain;
Refer to at least pages 17 and 19-20 of Kim, the scaling factors for each level are calculated based on the highest level downward.
performing computations using the fully-homomorphic encryption scheme;
Refer to at least section 2.1 of Kim with respect to computations performed by CKKS.
rescaling one or more ciphertext levels below the highest level of the modulus chain according to one or more of the calculated ciphertext scales during the performance of the computations by the fully-homomorphic encryption scheme; and
Refer to at least pages 8 and 9 of Kim with respect to CKKS rescaling; pages 17 -18 and 20-22 of Kim with respect to the modified rescaling.
encrypting of a plaintext into a ciphertext at a particular level of the modulus chain, wherein the ciphertext is associated with a respective scale of that particular level of the modulus chain.
Refer to at least the introduction, section 2.1, “Using a Different Scaling Factor for each Level” on page 17, and “Handling the Operations between Ciphertexts at Different Levels for Reduced-Error CKKS” on page 22 of Kim with respect to encrypting ciphertexts at particular levels of the modulus chain.
Kim concerns the encryption algorithm itself, and does not specify: the computer-implemented method further comprising using a neural network; performing computations further comprising the computations prescribed by various neural network layers of the neural network. However, Kim in view of Lee discloses: the computer-implemented method further comprising using a neural network; performing computations further comprising the computations prescribed by various neural network layers of the neural network.
Refer to at least the abstract, Algorithms 3-4, Algorithms 6-8, and the conclusion of Lee with respect to applying a CKKS scheme to a standard neural network to implement privacy preserving machine learning. As per the cited algorithms, Lee performs computations prescribed by various layers of the neural network using the CKKS scheme.
The teachings of Kim and Lee both concern homomorphic encryption, and are considered to be within the same field of endeavor and combinable as such.
Therefore it would have been obvious to one of ordinary skill in the art before the filing date of Applicant’s invention to modify the teachings of Kim to further implement the homomorphic encryption algorithm as part of protecting a neural network because design incentives or market forces provided a reason to make an adaptation, and the invention resulted from application of the prior knowledge in a predictable manner (e.g., the abstract and introduction of Lee concerning the need for privacy preserving machine learning utilizing FHE).
Regarding claims 4-5, they are rejected for substantially the same reasons as claim 1 above (i.e., citations concerning the software libraries such as HEANN, SEAL, and so forth).
Regarding claim 6, it is rejected for substantially the same reasons as claim 1 above (i.e., the citations to Lee and the obviousness rationale).
Regarding claim 7, Kim-Lee discloses: The method as recited in claim 1, wherein an output of the computations performed by the fully-homomorphic encryption scheme is an inference of a neural network.
Refer to at least the abstract and introduction of Kim with respect to using CKKS for implementing practical performance in machine learning applications.
Regarding independent claim 8, it is substantially similar to independent claim 1 above, and is therefore likewise rejected.
Regarding claims 11-14, they are substantially similar to claims 4-7 above, and are therefore likewise rejected.
Regarding independent claim 15, it is substantially similar to independent claim 1 above, and is therefore likewise rejected.
Regarding claims 18-20, they are substantially similar to claims 4-7 above, and are therefore likewise rejected.
Allowable Subject Matter
Claims 2-3, 9-10, and 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims (pending resolution of minor informalities noted above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/V.S/Examiner, Art Unit 2432
/ALI SHAYANFAR/Supervisory Patent Examiner, Art Unit 2432