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 .
Response to Amendment
The amendment filed on 26-FEBURARY-2026 in response to the non-final office action mailed 28-NOVEMBER-2025 has been entered. Claims 1-20 remain pending in the application.
With regards to the 103 rejections, the applicant’s amendments to the claims have been considered. Examiner has introduced new prior art MOHAMMADY in response to the amendments.
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, 3, 7-8, 10, 13-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over LAI et al (U.S. Pub. No. U.S. 20230059367 A1) in view of Zhang (U.S. Pub No. US 20220147818 A1) in view of MOHAMMADY(U.S. Pub No. US 12399985 B2)
Regarding claim 1, LAI substantially teaches the claimed invention, including:
A computer implemented method performed by a computer system having a memory and at least one hardware processor, the computer implemented method comprising: Obtaining a privacy loss parameter of differential privacy; ([0085] As further shown in the algorithm, the user-entity differential privacy system 106 utilizes the moments accountant to determine the T training steps privacy budget consumption (lines 14-15). In some embodiments, use of the moments accountant facilitates user-entity differential privacy by bounding the total privacy loss of T steps of the Gaussian mechanism) and Training a neural network to perform data obfuscation operation, ([0085] As further shown in the algorithm, the user-entity differential privacy system 106 utilizes the moments accountant [AltContent: rect] to determine the T training steps privacy budget consumption (lines 14-15). In some embodiments, use of the moments accountant facilitates user-entity differential privacy by bounding the total privacy loss of T steps of the Gaussian mechanism with the noise [AltContent: rect](0, Iσ.sup.2). In other words, given the bounded sensitivity of the estimator f.sub.ε, the user-entity differential privacy system 106 uses the moments accountant [AltContent: rect] to obtain a tight bound on the total privacy consumption of T steps of the Gaussian mechanism. Thus, the user-entity differential privacy system 106 provides a user-entity differential privacy (UeDP) guarantee. In some cases, the user-entity differential privacy system 106 determines that, for the estimator f.sub.ε, the moments accountant [AltContent: rect] of the sampled Gaussian mechanism correctly computes the UeDP privacy loss with the scale z=σ/)
While LAI does teach generating a model to perform data obfuscation, it does not explicitly teach:
the training of the neural network comprising training the neural network using training data to automatically and dynamically select a variance parameter that obeys a privacy loss parameters, with an objective of achieving a value of a privacy loss parameter;
However, in analogous art that similarly teaches data obfuscation, MOHAMMADY teaches:
the training of the neural network comprising training the neural network using training data to automatically and dynamically select a variance parameter that obeys a privacy loss parameters, with an objective of achieving a value of a privacy loss parameter;0 ((Col. 18 line 40 – Col 19 line 3)The Kelly criterion is known from literature. Step 4. Data node 12 via DP Mechanism Chooser 52 provides a proper DP mechanism, i.e., selects a DP mechanism based at least on, for example, the epsilon value (e.g., ε.sub.1, ε.sub.3, etc.) as described herein. In this step, DP mechanism chooser 52 chooses a DP mechanism from various DP mechanisms, such as one of a Laplace mechanism, Gaussian mechanism, Exponential mechanism, DP histogram, etc. Each DP Mechanism corresponds to a noise pattern/profile that is configured to add noise to a dataset where the additional/higher noise applied to data leads to higher privacy for the data compared to a lower level of noise applied to data. For anomaly detection, a choice may be a DP histogram which is a low in complexity but effective mechanism in outsourcing differentially private data types to third party analysts. Step 5. Data node 12 such as via the DP histogram entity 54 is configured to release the private histogram [AltContent: rect] (i.e., privatized dataset) that is based at least on the inputted DP mechanism, data set and the privacy budget (e.g., epsilon). A histogram is typically defined over a specific domain and a dataset where the histogram summarizes the occurrence counts of domain values over the dataset. For example, if the domain is a set of diseases D (such as cancer, flu, HIV, hepatitis, etc.), then a histogram over a patient dataset assigns the number of patients with a specific disease in the dataset. The privatized histogram is then outsourced to the third-party outlier analyst such as to data analyst node 14.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with MOHAMMADY‘s parameter selection method and, with LAI‘s training of a model that obfuscates, with a reasonable expectation of success, a method that selects a parameter, as in MOHAMMADY, for a model trained to obfuscate data, as found in LAI. A person of ordinary skill would have been motivated to improve privacy. (MOHAMMADY Col 4, lines 50-64).
LAI further teaches:
Using the trained neural network to perform data obfuscation by: (In other words, given the bounded sensitivity of the estimator f.sub.ε, the user-entity differential privacy system 106 uses the moments accountant [AltContent: rect] to obtain a tight bound on the total privacy consumption of T steps of the Gaussian mechanism. Thus, the user-entity differential privacy system 106 provides a user-entity differential privacy (UeDP) guarantee. In some cases, the user-entity differential privacy system 106 determines that, for the estimator f.sub.ε, the moments accountant [AltContent: rect] of the sampled Gaussian mechanism correctly computes the UeDP privacy loss with the scale z=σ/[AltContent: rect](f.sub.ε) for f.sub.ε for T training steps. In some instances, the user-entity differential privacy system 106 employs a moments accountant as described in Abadi, M. et al., Deep Learning with Differential Privacy, in ACM SIGSAC Conference on Computer and Communications Security, pp. 308-18, 2016, which is incorporated herein by reference in its entirety.
[0086] Thus, the user-entity differential privacy system 106 generates a natural language model that provides user-entity differential privacy to simultaneously protect the users and the sensitive entities represented by the underlying data)
While LAI does teach generating a model to perform data obfuscation, it does not explicitly teach:
Encoding input data different from the training data into a latent space representation of the input data, the encoding of the input data comprising inferring latent space parameters of a latent space probability distribution based on the input data and sampling data from the latent space distribution, the latent probability space distribution being based on the variance parameter; and Decoding the sampled data of the latent space representation into output data.
However, in analogous art that similarly obfuscates data, ZHANG teaches:
Encoding input data different from the training data into a latent space representation of the input data, (ZHANG P[0144] ” The encoder 208q is arranged to receive the observed feature vector Xo as an input and encode it into a latent vector Z (a representation in a latent space).”), the encoding of the input data comprising inferring latent space parameters of a latent space probability distribution based on the input data and sampling data from the latent space distribution, (ZHANG P[0170-0171] “In some embodiments each of the latent representations Z, is one-dimensional, i.e. consists of only a single latent variable ( element). Note however this does not imply the latent variable Z, is a modelled only as simple, fixed scalar value. Rather, as the auto-encoder is a variational auto-encoder, then for each latent variable Z, the encoder learns a statistical or probabilistic distribution, and the value input to the decoder is a random sample from the distribution. This means that for each individual element of latent space, the encoder learns one or more parameters of the respective distribution, e.g. a measure of centre point and spread of the distribution. For instance each latent variable Z, (a single dimension) may be modelled in the encoder by a respective mean value and standard deviation or variance. However preferably each of the latent space representations Z, is multi-dimensional, in which case each dimension is modelled by one or more parameters of a respective distribution.”), the latent probability space distribution being based on the variance parameter; and (ZHANG P[0170] “This means that for each individual element of latent space, the encoder learns one or more parameters of the respective distribution, e.g. a measure of centre point and spread of the distribution. For instance each latent variable Z, (a single dimension) may be modelled in the encoder by a respective mean value and standard deviation or variance.”);
Decoding the sampled data of the latent space representation into output data. (ZHANG “The encoder 208q is arranged to receive the observed feature vector Xo as an input and encode it into a latent vector Z (a representation in a latent space). The decoder 208p is arranged to receive the latent vector Z and decode back to the original feature space of the feature vector. The version of the feature vector output by the decoder 208p may be labelled herein X.”).
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with ZHANG‘s obfuscation method and, with LAI‘s, as modified by MOHAMMADY, training of a model that obfuscates, with a reasonable expectation of success, a method that obfuscates data, as in ZHANG, using a model trained to obfuscate data, as found in LAI, as modified by MOHAMMADY. A person of ordinary skill would have been motivated to reduce processing time. (ZHANG: P[0126]).
Regarding claim 3, ZHANG further teaches:
The neural network comprises a variational autoencoder. (ZHANG P[0170] “In some embodiments each of the latent representations Z, is one-dimensional, i.e. consists of only a single latent variable ( element). Note however this does not imply the latent variable Z, is a modelled only as simple, fixed scalar value. Rather, as the auto-encoder is a variational auto-encoder, then for each latent variable Z, the encoder learns a statistical or probabilistic distribution, and the value input to the decoder is a random sample from the distribution.” ).
Regarding claim 7, ZHANG further teaches:
The latent space probability distribution is further based on a mean that is bound within a finite space. (ZHANG P[0170] “This means that for each individual element of latent space, the encoder learns one or more parameters of the respective distribution, e.g. a measure of centre point and spread of the distribution. For instance each latent variable Z, (a single dimension) may be modelled in the encoder by a respective mean value and standard deviation or variance.” P[0113] “k-Nearest Neighbour Head Parameters: Generate the new head parameters en as the mean of the head parameters of the k- nearest neighbour features in terms of Euclidean distance [finite space], where colunm-wise mean imputing is used to fill in unobserved values.”).
Regarding claim 13, ZHANG further teaches:
The variance parameter is configured to comprise a global value that is independent of the input data. (ZHANG P[0010] “In this case, for each element of Z the encoder learns one or more parameters of the distribution, e.g. a measure of centre point and spread of the distribution. For instance the centre point could be the mean and the spread [global value] could be the variance or standard deviation”. P[0170] “In some embodiments each of the latent representations Z, is one- dimensional, i.e. consists of only a single latent variable (element). Note however this does not imply the latent variable Z, is a modelled only as simple, fixed scalar value. Rather, as the auto-encoder is a variational auto-encoder, then for each latent variable Z, the encoder learns a statistical or probabilistic distribution [independent of the input data], and the value input to the decoder is a random sample from the distribution. This means that for each individual element of latent space, the encoder learns one or more parameters of the respective distribution, e.g. a measure of centre point and spread of the distribution. For instance each latent variable Z, (a single dimension) may be modelled in the encoder by a respective mean value and standard deviation or variance.” ).
Regarding claims 8, 10, and 14, they comprise of limitations similar to those of claims 1, 3, and 7 and are therefore rejected for similar rationale. Regarding claims 15 and 17, they comprise of limitations similar to those of claims 1 and 3 and are therefore rejected for similar rationale. Regarding claim 20, it comprises of limitations similar to those of claim 13 and is therefore rejected for similar rationale.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over LAI et al (U.S. Pub. No. U.S. 20230059367 A1), Zhang (U.S. Pub No. US 20220147818 A1), MOHAMMADY(U.S. Pub No. US 12399985 B2) in further view of CAO (U.S. Pub. No. US 11847538 B2).
Regarding claim 2, while LAI, as modified by ZHANG, does teach claim 1, which claim 2 is dependent upon, it does not explicitly teach:
Obtaining the input data from a client machine; Computing the output data by feeding the obtained input data into the trained neural network; Transmitting the computed output data to a server machine via a network.
However, in analogous art that similarly maintains privacy of users, CAO teaches:
Obtaining the input data from a client machine; (Column 4 Lines 42-45, “In various embodiments, the set of data curators 110A, 110B, and 110C include distinct entities that collect and/or maintain data including private data that is maintained in the private datasets 102A, 102B, or 102C.”)
Computing the output data by feeding the obtained input data into the trained neural network; (Column 5 Lines 35-39, “However, in other embodiments, the set of differentially private generative models 104A, 104B, and 104C is maintained by the corresponding data curators 110A, 110B, and 110C and the training data is provided to the data consumer 116.” discloses feeding input data into the generative models)
Transmitting the computed output data to a server machine via a network. (Column 5 Lines 55-59, “The set of differentially private generative models 104A, 104B, and 104C trained with privacy constraints on private data (e.g., the private datasets 102A, 102B, or 102C), in various embodiments, provide the data consumer 116 indirect access to the private data.” discloses providing data to a consumer)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with CAO‘s data computation and transmission methods and, with LAI‘s, as modified by ZHANG, method of data privacy, with a reasonable expectation of success, a method for handling data and computing data, as in CAO, for the sake of preserving user privacy, as found in LAI, as modified by ZHANG. A person of ordinary skill would have been motivated to increase efficiency. (CAO Column 3, lines 34-48).
Regarding claims 9 and 16, they comprise of limitations similar to those of claim 2 and are therefore rejected for similar rationale
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over LAI et al (U.S. Pub. No. US 20230059367 A1), Zhang (U.S. Pub No. US 20220147818 A1), MOHAMMADY(U.S. Pub No. US 12399985 B2) in further view of Amroabadi et al (U.S. Pub No. US 20210133590 A1).
Regarding claim 4, while LAI, as modified by ZHANG, does teach claim 1, which claim 4 is dependent upon, it does not explicitly teach:
The input data comprises sequential data.
However, in analogous art that similarly handles private data, AMROABADI teaches:
The input data comprises sequential data. (AMROABADI P[0197] “Input data 110 can be time- series signals such as those illustrated in FIG. 4, may be sequential data such as credit card transactions, debit card transactions, or other financial transactions.”).
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with AMROABADI‘s sequential input data and, with LAI‘s, as modified by ZHANG, data privatization, with a reasonable expectation of success, sequential input data, as in AMROABADI, which is then encoded, as found in LAI, as modified by ZHANG. A person of ordinary skill would have been motivated to improve privacy. (AMROABADI: P[0046]).
Regarding claims 11 and 18, they comprise of limitations similar to those of claim 4 and are therefore rejected for similar rationale.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over LAI et al (U.S. Pub. No. U.S. 20230059367 A1), Zhang (U.S. Pub No. US 20220147818 A1), MOHAMMADY(U.S. Pub No. US 12399985 B2) in further view of WANG (U.S. Pub No. US 20190318268 A1)
While LAI, as modified by ZHANG, does teach claim 1, which claim 5 is dependent upon, it does not explicitly teach:
The variance parameter comprises a global value that is independent of the input data.
However, in analogous art that similarly teaches training models, WANG teaches:
The variance parameter comprises a global value that is independent of the input data. (WANG [0068] “Thus, edge nodes 204A, 204B, . . . , 204N receive synchronized values of the global model parameters 208 but provide the synchronization node 202 with locally generated values of the new model parameters 210A, 210B, . . . , 210N, which can vary between edge nodes 210A-201N. The global model parameters 208 can include a global value of a model parameter (w(t))”).
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with WANG‘s global value and, with LAI‘s, as modified by ZHANG, variance parameter, with a reasonable expectation of success, a global value, as in WANG, that is in a variance parameter, as found in LAI, as modified by ZHANG. A person of ordinary skill would have been motivated to improve user privacy. (WANG: P[0053]).
Regarding claim 6, it comprises of limitations similar to claim 5 and is therefore rejected for similar rationale.
Response to Arguments
Applicant’s arguments filed 26-FEBURARY-2026 have been fully considered, but they are found to be non-persuasive.
With regards to applicant’s remarks regarding the 103 rejection, the applicant argues that the prior art does not teach the newly amended claim 1. The examiner acknowledges this argument and has replaced part of the mapping of the LAI reference of claim 1 with new prior art MOHAMMADY.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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