DETAILED ACTION
This action is in response to the response filed on February 12, 2026. Claims 1-20 are pending. Of such, claims 1-10 represent a method and claims 11-17 represent a system and claims 18-20 represent a device directed to a switching voltage regulator and control method.
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 .
Specification
The objections to the disclosure have been withdrawn in view of the amendments made to the specification submitted on February 12, 2026.
Response to Arguments
Applicant’s arguments, see Remarks, filed February 12, 2026, with respect to the rejection(s) of claim(s) 1-3, 5, 8-9, 11-13, 16, and 18 under 102(a)(1) 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 Therefore, the rejection has been withdrawn.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 Zhang and Gentry.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8-9, 11-14, 16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20220329438 ), hereinafter referred to as Zhang, in view of Gentry et al. (NPL: Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based), hereinafter referred to as Gentry.
Regarding Claim 1, Zhang discloses:
A method for transmitting data values for privacy protection, the method comprising: collecting, by a plurality of collecting stations, data values associated with a respective environment of each collecting station (In ¶ 9, Zhang discloses “Smart meters encrypt collected user's electricity consumption data to generate a ciphertext, generate a digital signature for the ciphertext and send the ciphertext and signature as reported data to corresponding fog node for data aggregation”); encrypting, by each collecting station, each data value to generate a plurality of encrypted data values by the plurality of collecting stations (In ¶ 14, Zhang discloses “In step S2, smart meters combine a random blinding technique with the homomorphic encryption algorithm to encrypt user's electricity consumption data.”); aggregating, by an aggregator, a subset of the plurality of encrypted data values to generate an encrypted aggregated value of the subset by summing the encrypted data values associated with the subset (In ¶ 10, Zhang discloses “Fog-level Aggregation: After the fog node receives all reported data from smart meters in its managed area in the prespecified period, it firstly verifies all the digital signatures of reported data. If the verification passes, fog node aggregates all the data ciphertexts of reported data to generate the fog-level aggregate ciphertext” and further in ¶ 47 “This invention can provides flexible data statistical analysis and query functionality for the control center such that the control center or a service provider could selectively specify the range of interested user areas, specifically it could specify an arbitrary subset of indexes of user areas for statistical analysis on demand”); communicating, to a using entity, the encrypted aggregated value (In ¶ 10, Zhang discloses “Then fog node sends the fog-level aggregate ciphertext and fog-level signature to the cloud server for long-time storage.”); receiving, by the using entity, the secret key from a server or a cloud device (In ¶ 8, Zhang discloses “Then the trust center publishes all public parameters and sends private keys to corresponding communication entities via a secure channel.” And further in ¶ 21, “The trust authority sends the private key P.sub.1 to the control center”), the using entity located remotely with respect to the plurality of collecting stations (See Figure 1, where the control center is a different entity to the smart meters); decrypting, by the using entity, the encrypted aggregated value using the secret key to obtain a decrypted sum of data values (In ¶ 40, Zhang discloses “the control center uses key-leakage resilient decryption algorithm to compute the discrete logarithm…to get the sum M of all users' electricity consumption data in the user areas specified in the user area list,”); and performing, by the using entity, a data related function on the decrypted sum of the data values (In ¶ 12, Zhang discloses “control center decrypts the aggregate ciphertext and further computes the arithmetic mean and variance of all users' electricity consumption data within the specified user area list.”).
However, Zhang does not explicitly disclose the use of Fully Homomorphic Encryption.
Gentry discloses:
encrypting, by each collecting station, each data value using a secret key (On page 9, Gentry discloses “SecretKeyGen(params): Output sk = s ← (1,−t1,...,−tn) ∈ Zn+1 .”) and a fully homomorphic encryption (FHE) technique (In the abstract, Gentry discloses “We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem.” And on page 9, further discloses “Enc(params,pk,µ): To encrypt a message µ ∈ Zq, sample a uniform matrix R ∈ {0,1}N× m and output the ciphertext”)
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Gentry’s approach of the use of a fully homomorphic encryption scheme as the motivation would be that both Zhang and Gentry disclose the use of homomorphic encryption to enable computation on encrypted data without decryption, substituting Zhang’s encryption technique with Gentry’s fully homomorphic encryption would produce predictable results however would allow the benefit being asymptotically faster than previous homomorphic schemes (See Gentry, Abstract).
Regarding Claim 2, the combination of Zhang and Gentry disclose:
The method of claim 1, wherein the data values are utility consumption values, and wherein the collecting station is a smart meter, the method further comprising: measuring, by the smart meter, the utility consumption value consumed by utility consuming environment (In ¶ 2, Zhang discloses “The core of AMI is an embedded device called smart meter which is installed in the house of users to periodically collect users' electricity consumption data and report them to the control center.”); supplying, by smart meters, the utility consumption values to the using entity (In ¶ 9, Zhang discloses “send the ciphertext and signature as reported data to corresponding fog node for data aggregation.”); and performing the data related function on the utility consumption values at the using entity (In ¶ 12, Zhang discloses “control center decrypts the aggregate ciphertext and further computes the arithmetic mean and variance of all users' electricity consumption data within the specified user area list.”).
Regarding Claim 3, the combination of Zhang and Gentry disclose:
The method of claim 1, wherein the data values are energy consumption values and the collecting station is a smart energy meter (In ¶ 2, Zhang discloses “The core of AMI is an embedded device called smart meter which is installed in the house of users to periodically collect users' electricity consumption data and report them to the control center.”);
Regarding Claim 4, the combination of Zhang and Gentry discloses the limitations of Claim 1.
However, Zhang does not disclose the use of a ring learning with errors methodology.
Gentry discloses:
wherein encrypting each data value using the secret key and the fully homomorphic encryption technique comprises (In the abstract, Gentry discloses “We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem.”): identifying a ring of integers modulo the ring defining integer (On page 23, Gentry discloses “Let R = Z[x]/(f(x)) and let Rq = R/qR.”); generating N random numbers belonging to a set of N-vectors over the ring, where N is an integer (On page 23, Gentry discloses “To encrypt 0, one samples small r ∈ Rq and short vector e ∈
R
q
2
according to distribution χ, and outputs c ← r · A + e ∈
R
q
2
.”); computing a summation modulo for each random number of the N random numbers where a corresponding secret key binary coefficient equals one (On page 23, Gentry discloses “The secret key is s = (1, s1) ∈
R
q
2
….To encrypt µ ∈ {0, 1} in LPR, one adds µ · [q/2] to the first coefficient of c.”)); adding an encrypting data value to the summation modulo to encrypt it as plaintext (On page 23, Gentry discloses “To encrypt µ ∈ {0, 1} in LPR, one adds µ · [q/2] to the first coefficient of c.””); adding Gaussian noise to the summation modulo and the encrypting data value to generate a learning with errors (LWE) summation value (On page 23, Gentry discloses “outputs c = r · A + e ∈
R
q
2
.” Where the gaussian noise is represented by the variable e); and generating an LWE ciphertext comprising the N random numbers and the LWE summation value, the LWE ciphertext being a vector value, the LWE summation value being a last element of the LWE ciphertext (On page 23, Gentry discloses “outputs c = r · A + e ∈
R
q
2
.” Where the LWE ciphertext is represented by the variable c which includes random numbers (A) and further on page 2, Gentry discloses “the ciphertext c and secret key s are n-dimensional vectors”);, and wherein decrypting the encrypted aggregated value comprises: computing the summation modulo for each random number where the corresponding secret key binary coefficient equals one; and subtracting the summation modulo from the last element of the LWE ciphertext to generate the decrypted sum of data values (On page 23, Gentry discloses “Decryption computes <c,s> = r · e + <e,s> + µ ·[q/2], and outputs ‘µ = 0’ or ‘µ = 1’” and on page 4 further discloses “To decrypt, we extract the i-th row Ci from C, compute x ← <Ci , v> = µ · vi + ei , and output µ = [x/vi]”).
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Gentry’s approach of the use of a ring based learning with errors approach when performing fully homomorphic encryption as the motivation would be that the ring based approach is more efficient than the traditional learning with error scheme when performing fully homomorphic encryption (See Gentry, Page 23).
Regarding Claim 5, the combination of Zhang and Gentry disclose:
The method of claim 1, wherein the using entity is a power station that distributes energy to the environments as a function of the sum of the data values (In ¶ 2, Zhang discloses “With the analysis results, the control center could monitor the situation of smart grid system and dynamically adjust and optimize power generation and distribution.”)
Regarding Claim 8, the combination of Zhang and Gentry disclose:
The method of claim 1, wherein generating the encrypted aggregated value is performed without decrypting encrypted data values (In ¶ 5, Zhang discloses “With the homomorphism property of homomorphic encryption algorithms, when user data are encrypted to ciphertexts, they can be efficiently aggregated and the control center could. directly decrypt aggregate ciphertext to get some statistical results with no need of decryption to ciphertext of single user, which effectively preserve user privacy and data confidentiality.”)
Regarding Claim 9, the combination of Zhang and Gentry disclose:
The method of claim 1, wherein the secret key is made available for decryption only to the using entity (In ¶ 64, Zhang discloses “the control center uses corresponding key-leakage resilient homomorphic decryption algorithm to decrypt the response data.”)
Claims 11-12 is directed to a system having functionality corresponding to the method of Claims 1-2, and is rejected by a similar rationale, mutatis mutandis.
Claim 13 is directed to a system having functionality corresponding to the method of Claim 5, and is rejected by a similar rationale, mutatis mutandis.
Claim 14 is directed to a system having functionality corresponding to the method of Claim 4, and is rejected by a similar rationale, mutatis mutandis.
Claim 16 is directed to a system having functionality corresponding to the method of Claims 8 and 9, and is rejected by a similar rationale, mutatis mutandis.
Regarding Claim 18, Zhang discloses:
A smart energy meter configured to: collect energy consumption values In ¶ 9, Zhang discloses “Smart meters encrypt collected user's electricity consumption data to generate a ciphertext, generate a digital signature for the ciphertext and send the ciphertext and signature as reported data to corresponding fog node for data aggregation”); and encrypt each data value to generate an encrypted data value (In ¶ 14, Zhang discloses “In step S2, smart meters combine a random blinding technique with the homomorphic encryption algorithm to encrypt user's electricity consumption data.”), wherein the encrypted data value is aggregated by an aggregator with encrypted data values of other smart energy meters to generate an encrypted aggregated value by summing the encrypted data values (In ¶ 10, Zhang discloses “Fog-level Aggregation: After the fog node receives all reported data from smart meters in its managed area in the prespecified period, it firstly verifies all the digital signatures of reported data. If the verification passes, fog node aggregates all the data ciphertexts of reported data to generate the fog-level aggregate ciphertext” and further in ¶ 47 “This invention can provides flexible data statistical analysis and query functionality for the control center such that the control center or a service provider could selectively specify the range of interested user areas, specifically it could specify an arbitrary subset of indexes of user areas for statistical analysis on demand”), and wherein the encrypted aggregated value is communicated to a power station (In ¶ 10, Zhang discloses “Then fog node sends the fog-level aggregate ciphertext and fog-level signature to the cloud server for long-time storage.”) configured to obtain a decrypted sum of data values using the secret key communicated by a server or a cloud device to perform a data related function on the decrypted sum of data values (In ¶ 12, Zhang discloses “control center decrypts the aggregate ciphertext and further computes the arithmetic mean and variance of all users' electricity consumption data within the specified user area list.”).
However, Zhang does not explicitly disclose the use of Fully Homomorphic Encryption.
Gentry discloses:
and encrypt each data value using a secret key (On page 9, Gentry discloses “SecretKeyGen(params): Output sk = s ← (1,−t1,...,−tn) ∈ Zn+1 .”) and a fully homomorphic encryption (FHE) technique to generate an encrypted data value (In the abstract, Gentry discloses “We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem.” And on page 9, further discloses “Enc(params,pk,µ): To encrypt a message µ ∈ Zq, sample a uniform matrix R ∈ {0,1}N× m and output the ciphertext”)
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Gentry’s approach of the use of a fully homomorphic encryption scheme as the motivation would be that both Zhang and Gentry disclose the use of homomorphic encryption to enable computation on encrypted data without decryption, substituting Zhang’s encryption technique with Gentry’s fully homomorphic encryption would produce predictable results however would allow the benefit being asymptotically faster than previous homomorphic schemes (See Gentry, Abstract).
Claim 19 is directed to a device having functionality corresponding to the method of Claim 4, and is rejected by a similar rationale, mutatis mutandis.
Claims 6-7, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20220329438 ), hereinafter referred to as Zhang, in view of Gentry et al. (NPL: Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based), hereinafter referred to as Gentry, in further view of Qian et al. (NPL: Two Secure and Efficient Lightweight Data Aggregation Schemes for Smart Grid), hereinafter referred to as Qian.
Regarding Claim 6, Zhang and Gentry disclose the limitations of Claim 1.
However, Zhang does not disclose the use of a zero-tokens.
Qian discloses:
wherein each collecting station includes a secure element wherein the server or cloud device shares with each secure element a set of zero-tokens corresponding to an encrypted zero data value that is encrypted by the encryption operation (On page 2630, Qian discloses “TA assigns an ID to each AP. We mark nu,i IDs as {AP u,1,AP u,2,…,AP u,i } , where nu,i is the total number of APs in i -th HAN that is less than n . Each of them encrypts its ID to IDu,j−enc = IDu,j+xuj , and then stores it in a secure place everytime.”), wherein each encrypted zero data is identifiable by an identifier previously stored in the secure element (On page 2630, Qian discloses “TA assigns an ID to each AP.”), wherein, during the encryption, each secure element randomly selects a subset of the set of zero-tokens to generate random scalar weights (On page 2630, Qian discloses “SM generates n×t+1 random numbers ((yij,1≤i,j≤n )), of which are sent n×t to APs and one to itself with the help of TA, and the random numbers are backed up in its own security register.”), a vector containing the scalar weights, the identifiers, and the encrypted sum of data values sent to the using entity through the aggregator (On page 2629, Qian discloses “mi and ci are t times aggregated plaintext and ciphertext of APi , respectively, and rij is t times aggregated random number of APi . And aggregate ciphertext is c=
∑
i
=
1
n
c
i
, while yi is random vector in
Z
r
n
generated by SM. In addition, the random numbers y1,y2,…,yn satisfy
∑
i
=
1
n
y
i
=
0
, , which are distributed by the distributor.”) , and wherein the method further comprises decrypting, by the using entity, the encrypted aggregated value using the identifiers, the random scalar weights, and the vector containing the scalar weights to compute the decrypted sum of data values (On page 2629, Qian discloses in section D. Decryption, the formula for the control center to decrypt the provided aggregated ciphertext).
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Qian’s approach of the homomorphic encryption scheme as the motivation would be to achieve higher security when transmitting data while reducing communication and computational costs between a smart meter and a control center (See Qian, Page 2627).
Regarding Claim 7, the combination of Zhang, Gentry, and Qian disclose the limitations of claim 6.
However, Zhang does not explicitly disclose the use of scalar weights.
Qian discloses:
The method of claim 6, wherein communicating the encrypted aggregated value comprises communicating, by the aggregator to the using entity, the random scalar weights, the identifiers, and the sum of data values. (On page 2629, Qian discloses “mi and ci are t times aggregated plaintext and ciphertext of APi , respectively, and rij is t times aggregated random number of APi . And aggregate ciphertext is c=
∑
i
=
1
n
c
i
, while yi is random vector in
Z
r
n
generated by SM. In addition, the random numbers y1,y2,…,yn satisfy
∑
i
=
1
n
y
i
=
0
, , which are distributed by the distributor.”)
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Qian’s approach of the homomorphic encryption scheme as the motivation would be to achieve higher security when transmitting data while reducing communication and computational costs between a smart meter and a control center (See Qian, Page 2627).
Claim 15 is directed to a system having functionality corresponding to the method of Claim 6, and is rejected by a similar rationale, mutatis mutandis.
Claim 20 is directed to a device having functionality corresponding to the method of Claim 6, and is rejected by a similar rationale, mutatis mutandis.
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20220329438 ), hereinafter referred to as Zhang, in view of Gentry et al. (NPL: Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based), hereinafter referred to as Gentry, in further view of Mustafa et al. (US 20170019248), hereinafter referred to as Mustafa.
Regarding Claim 10, Zhang and Gentry disclose the limitations of claim 1.
However, Zhang does not disclose the use of a secure element.
Mustafa discloses:
The method of claim 1, wherein the secret key is stored in a secure element accessible to each collecting station for performing encryption (In ¶ 120, Mustafa discloses “On the storage unit of the smart meter are also stored the secret keys of the smart meter.”)
One in ordinary skill in the art of cryptography would have been motivated, before the effective filing date of the claimed invention to modify Zhang’s approach by utilizing Mustafa’s approach of storing the secret key within a storage unit as the motivation would be to allow access to the smart meter to perform encryption with the secret key (See Mustafa, ¶ 120)
Claim 17 is directed to a system having functionality corresponding to the method of Claim 10, and is rejected by a similar rationale, mutatis mutandis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Becker et al. (US 10630655) discloses a method for fully homomorphic encryption utilizing learning with errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHADI H KOBROSLI whose telephone number is (571)272-1952. The examiner can normally be reached M-F 9am-5pm ET.
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/SHADI H KOBROSLI/Examiner, Art Unit 2492 /RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492
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