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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Poole et al. (US 2020/0364505 A1), hereinafter “Poole”.
As per claim 1, Poole teaches a method for generating time series data, performed by at least one computing device, the method comprising:
“obtaining an autoencoder trained using original time series data, wherein the autoencoder includes an encoder and a decoder” at [0018]-[0024], [0042] and Fig. 1;
(Poole teaches a variational autoencoder neural network system is trained using time series data, the autoencoder includes an encoder and a decoder)
“obtaining a score predictor trained using latent vectors of original time series data generated through the encoder” at [0018]-[0019], [0050],
(Poole teaches training a score predictor for determining a gradient (i.e., “score”) of the objective function from a difference between the training data item and the corresponding output data item and from a difference between the posterior distribution and the prior distribution of the set of latent variables, wherein the set of latent variables defines values for a latent variable vector z, backpropagating the gradient through the variational autoencoder neural network system to adjust parameters of the encoder and the decoder to optimize the objective function. The training may employ stochastic gradient descent (SGD) with an objective includes a reconstruction cost and a closed-form KL divergence term.
“extracting a plurality of noise vectors from a prior distribution” at [0014], [0027], [0059]-[0062];
(Poole teaches at time step t, adding noise component ϵt to prior distribution latent variable Zt-1 to generate noise vector Zt= ɑ.zt-1 + ϵt from a prior distribution for each time step)
“generating a plurality of synthetic latent vectors by updating the plurality of noise vectors using scores of the plurality of noise vectors predicted through the score predictor” at [0070]-[0079];
(Poole teaches the training data item is processed using the encoder neural network to obtain parameters defining the posterior distribution, and a set of latent variables is sampled from this distribution. The set of latent variables is then processed by the decoder neural network to obtain output data item. The process then backpropagates gradients (i.e., “scores”) of an objective function of the type previously described to update the parameter of the encoder and decoder neural network. To backpropagate through the latent variable sampling the “reparameterization trick” may be used, rewriting the sampling operation for each latent variable as z=μ + Ϭ2ϵ, where ϵ is standard Gaussian noise. The objective function may have the general form log p(x|z) – DKL (q(z|x) || p(z))
“reconstructing the plurality of synthetic latent vectors into a plurality of synthetic time series samples through the decoder and outputting them” at [0022], [0051], [0066].
(Poole teaches the trained decoder neural network may be used to generate example data item by sampling a set of latent variables from the prior distribution and providing the set to the decoder neural network)
As per claim 2, Poole teaches the method of claim 1, wherein “the score predictor is configured to further receive a latent vector at a previous time point in addition to a latent vector at a current time point and predict a score for the latent vector at the current time point” at [0059]-[0062], [0070]-[0079].
As per claim 3, Poole teaches the method of claim 2, wherein “the generating the plurality of synthetic latent vectors comprises: updating a first noise vector to generate a first synthetic latent vector, wherein the first synthetic latent vector is a vector at a time point before a second synthetic latent vector; inputting a second noise vector and the first synthetic latent vector into the score predictor to predict a score of the second noise vector; and generating the second synthetic latent vector by updating the second noise vector based on the score of the second noise vector” at [0059]-[0062], [0070]-[0079].
As per claim 4, Poole teaches the method of claim 1, wherein “the score predictor is trained based on a difference between a predicted score for noisy vectors generated by adding noise to the latent vectors and a value calculated by Equation 1 below, [Equation 1] Δhst logp (hst | h0t) , where H0t means a latent vector at a t-th time point, hst means a noise vector generated by adding noise to the latent vector at the t-th time points, logp (hst | h0t) means a log probability density of hst for h0t and Δhst means a gradient” at [0052]-[0062], [0070]-[0079].
As per claim 5, Poole teaches the method of claim 1, wherein “the encoder or the decoder is implemented as a RNN (Recurrent Neural Network)-based neural network” at [0003], [0018]-[0019].
As per claim 6, Poole teaches the method of claim 1, wherein “the encoder or the decoder is implemented as a transformer-based neural network” at [0003], [0018]-[0019].
As per claim 7, Poole teaches the method of claim 1, wherein “the score predictor is implemented as a CNN (Convolutional Neural Network)-based neural network performing an 1D convolution operation” at [0016]-[0022].
As per claim 8, Poole teaches the method of claim 7, wherein “the score predictor is implemented based on a neural network of a U-Net structure” at [0003], [0018]-[0019].
As per claim 9, Poole teaches the method of claim 1, wherein “the original time series data comprises real-world data, the method further comprises: replacing the real-world data with the plurality of synthetic time series samples or transforming the real-world data using the plurality of synthetic time series samples” at [0022]-[0024].
Claims 10-17 recite similar limitations as in claims 1-9 and are therefore rejected by the same reasons.
Response to Arguments
Applicant's arguments filed 7/10/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding claim 1, Applicant argued that Poole fails to disclose the second limitations, namely “a score predictor that is trained separately from the encoder and decoder, using latent vector to which noise has been added”. On the contrary, Pool teaches at [0018]-[0019], [0050] the steps of training a score predictor for determining a gradient (i.e., “score”) of the objective function from a difference between the training data item and the corresponding output data item and from a difference between the posterior distribution and the prior distribution of the set of latent variables, wherein the set of latent variables defines values for a latent variable vector z, backpropagating the gradient through the variational autoencoder neural network system to adjust parameters of the encoder and the decoder to optimize the objective function. The training may employ stochastic gradient descent (SGD) with an objective includes a reconstruction cost and a closed-form KL divergence term.
Applicant further argued that “with respect to second limitation, the Examiner maps the claimed “score predictor” to the gradient of the objective function disclosed at paragraphs [0018]-[0019] and [0050] of Pool. However, this gradient is merely a training signal used to update the parameters of the encoder and decoder neural network themselves via stochastic gradient descent (SGD); it is not the output of a separate inference module that receives a noise-added latent vector as input and output a corresponding score”. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “the output of a separate inference module that receives a noise-added latent vector as input and output a corresponding score”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant further argued that Pool fails to disclose “a generating step in which a plurality of synthetic latent vectors are produced by iteratively updating a plurality of noise vectors using scores predicted by the score predictor”. On the contrary, Poole teaches at [0070]-[0079] that the training data item is processed using the encoder neural network to obtain parameters defining the posterior distribution, and a set of latent variables is sampled from this distribution. The set of latent variables is then processed by the decoder neural network to obtain output data item. The process then backpropagates gradients (i.e., “scores”) of an objective function of the type previously described to update the parameter of the encoder and decoder neural network. To backpropagate through the latent variable sampling the “reparameterization trick” may be used, rewriting the sampling operation for each latent variable as z=μ + Ϭ2ϵ, where ϵ is standard Gaussian noise. The objective function may have the general form log p(x|z) – DKL (q(z|x) || p(z))
Applicant further argued that “Poole fails to disclose a score predictor that is trained separately from the encoder and decoder using noise-added latent vectors, or a step of generating a plurality of synthetic latent vectors by iteratively updating a plurality of noise vectors using scores predicted by such a score predictor, as recited in claim 1”. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a step of generating a plurality of synthetic latent vectors by iteratively updating a plurality of noise vectors using scores predicted by such a score predictor”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In light of the foregoing arguments, the 35 U.S.C 102 rejection is hereby sustained.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
August 10, 2026
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