DETAILED ACTION
This action is in response the communications filed on 03/16/2026 in which claims 1, 3, 7-8, 10, 14-15, and 17 are amended, and claims 1-20 are pending
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 § 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.
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Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: Claims 1-7 recite a method. Claims 8-14 recite a non-transitory machine-readable medium. Claims 15-20 recite a system comprising a non-transitory memory and one or more hardware processors. Therefore, claims 1-7 are directed to a process, claims 8-14 are directed to a manufacture, and claims 15-20 are directed to a machine.
With respect to claims 1, 8 and 15:
2A Prong 1: The claim recites a judicial exception.
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wherein the first state-space model includes a first nonparametric transition function for generating one or more latent variables of a latent space for the time series dataset, and an emission model including a post-nonlinear transformation associated with distortion in instrument measurements; (mathematical concept – mathematical equation, in light of specification [0054][0071])
determining… one or more estimated latent variables; and (mental process – evaluation or judgement, or mathematical concept – mathematical equation, in light of specification [0054][0071])
providing… a first prediction result for the time series dataset based on the estimated latent variables (mental process – evaluation or judgement, or mathematical concept – mathematical equation, in light of specification [0054][0071])
2A Prong 2: The judicial exception is not integrated into a practical application.
(claims 1, 8 and 15) via an input interface (claim 8) a plurality of machine-readable instructions which, when executed by one or more processors (claim 15) a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory (mere instructions to apply an exception – MPEP 2106.05(f), (2) invoking generic computer components)
receiving… a time series dataset that includes datapoints at a plurality of timestamps in an observed space; (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting)
providing a neural network system based on a first state-space model of a dynamical system underlying the time series dataset (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; Using a neural network system, with the input to the model being the time series dataset.)
using the neural network system based on the first state-space model… using the neural network system… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
(claims 1, 8 and 15) via an input interface (claim 8) a plurality of machine-readable instructions which, when executed by one or more processors (claim 15) a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory (mere instructions to apply an exception – MPEP 2106.05(f), (2) invoking generic computer components)
receiving… a time series dataset that includes datapoints at a plurality of timestamps in an observed space; (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
providing a neural network system based on a first state-space model of a dynamical system underlying the time series dataset (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; Using a neural network system, with the input to the model being the time series dataset.)-
using the neural network system based on the first state-space model… using the neural network system… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claims 2, 9 and 16:
2A Prong 1: The claim recites a judicial exception.
wherein each of the latent variables is identifiable from a corresponding observed data (mathematical concept – mathematical relation, in light of specification [0071] and [0054][0057])
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With respect to claims 3, 10 and 17:
2A Prong 1: The claim recites a judicial exception.
wherein the first nonparametric transition function has a first input including the one or more parent time-lagged variables of the latent factor and a second input including a first noise (mathematical concept – mathematical equation, in light of specification [0071])
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With respect to claims 4, 11 and 18:
2A Prong 1: The claim recites a judicial exception.
wherein the first nonparametric transition function has a third input including one or more time-varying change factors (mathematical concept – mathematical equation, in light of specification [0071])
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With respect to claims 5, 12 and 19:
2A Prong 1: The claim recites a judicial exception.
wherein the one or more time-varying change factors are associated with a second nonparametric transition function (mathematical concept – mathematical equation, in light of specification [0071])
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With respect to claims 6, 13 and 20:
2A Prong 1: The claim recites a judicial exception.
wherein the second nonparametric transition function includes an input including a second noise, and wherein the first noise and the second noise are mutually independent (mathematical concept – mathematical equation, in light of specification [0071])
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With respect to claims 7 and 14:
2A Prong 1: The claim recites a judicial exception.
determine the one or more estimated latent variables (mental process – evaluation or judgement, or mathematical concept – mathematical equation, in light of specification [0079])
generate a first latent-space prediction result based on the one or more estimated latent variables (mental process – evaluation or judgement, or mathematical concept – mathematical equation, in light of specification [0079]
transform the first latent-space prediction result to the first prediction result in the observed space (mental process – evaluation or judgement)
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the neural network system includes: an encoder configured to… using the neural network system… an auxiliary predictor configured to… using the neural network system… a decoder configured to… using the neural network system… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the neural network system includes: an encoder configured to… using the neural network system… an auxiliary predictor configured to… using the neural network system… a decoder configured to… using the neural network system… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-10 and 14-17 rejected under 35 U.S.C. 103 as being unpatentable over Liu ("Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems" 20220413) in view of Yao ("Learning Temporally Causal Latent Processes from General Temporal Data" 20220208) in further view of Li ("Joint state estimation for nonlinear state-space model with unknown time-variant noise statistics" 20190923)
In regard to claims 1, 8 and 15, Liu teaches: A method of providing a neural network system for time series forecasting, comprising: (Liu, p. 1, Abstract "The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios... we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) [a neural network system] assisted variational sequence model on an augmented recurrent input space...")
receiving, via an input interface, a time series dataset that includes datapoints at a plurality of timestamps in an observed space; (Liu, p. 3, 2. Preliminaries "It is assumed that the sequence data D = {u_1:T…, y_1:T...} at T discrete and evenly distributed time points [a time series dataset that includes datapoints at a plurality of timestamps in an observed space] are sampled from an unknown dynamic system.") (Liu, p. 11, 4. Numerical experiments "We implement our model using PyTorch on a Linux workstation with TI-TAN RTX GPU."; the python code and its implementation on a workstation inherently teach all the computer components, such as processors, memory, input interface)
providing a neural network system based on a first state-space model of a dynamical system underlying the time series dataset, wherein the first state-space model includes a first... transition function for generating one or more latent variables of a latent space for the time series dataset, and… (Liu, p. 3, 2. Preliminaries "Specifically, we assume that the target dynamic system could be described by the state space model [a first state-space model of a dynamical system] as h_t = f(h_t-1; u_t), (1)… where… the variable h_t... is the system hidden state to be inferred at time t... the input signal (action) u_t is known at all time points... the transition function f... [a first transition function] evolves the hidden state h_t-1 to the next state h_t [for generating latent variables of a latent space]"; p. 4-5, 3.1. Model definition "In order to incorporate stochasticity into the sequence model… Under the framework of latent variable model, the joint prior with additional latent variables z1:T is assumed to factorize over time as... p(y|z; u) p(z|z0:t-1; u)... (3)... similar to the… in (3) … Eq. (5) which describes the generation of augmented latent states given observations and input signals up to current time, and each state transition q (zt|z0:t1; u1:t; y1:t)”; p. 7 "Figure 1: The inference and generative networks of the proposed VRNNaug model [a neural network system] for probabilistic time series forecasting."; see Figure 1 posterior q(zt|·) [a transition function for generating latent variables, where zt represents latent variables]; VRNNaug model is built on top of the sequence model M, therefore both VRNNaug model and the sequence model M teach the concept of ‘a transition function for generating latent variables.’ )
determining, using the neural network system based on the first state-space model, one or more estimated latent variables; and (Liu, p. 7, 3.3. Amortized VRNNaug "the next is to elaborate the practical implementations for constructing the variational posterior q(zt|·) and the likelihood p(yt|·). The detailed implementations of the proposed VRNNaug sequence model are depicted in Fig. 1. It is observed that the whole model follows the encoder-decoder structure, wherein the encoder embeds the temporal patterns into the stochastic, time-aware manifold represented by z_t... [estimated latent variables]")
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providing, using the neural network system, a first prediction result for the time series dataset based on the estimated latent variables. (Liu, p. 7, 3.3. Amortized VRNNaug "the next is to elaborate the practical implementations for constructing the variational posterior q(zt|·) and the likelihood p(yt|·)... wherein the encoder embeds the temporal patterns into the stochastic, time-aware manifold represented by z_t, while the decoder generates outputs from the time-aware manifold. [a first prediction result based on the estimated latent variables]")
Liu does not teach, but Yao teaches: a first nonparametric transition function (Yao, p. 1, Abstract "In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes..."; p. 1, 1 INTRODUCTION AND RELATED WORK "In the nonparametric setting, the generating process of each latent causal factor z_it is characterized by nonparametric assignment z_it = f_i (Pa(z_it), ε_it), [a first non-parametric transition function] in which the parents of z_it... together with noise term ε_it... generate z_it via unknown nonparametric function fi with some time delay."; a non-parametric function does not assume a fixed functional form between variables, e.g. variables z and ε)
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 Liu to incorporate the teachings of Yao by including temporally causal latent processes with nonparametric setting. Doing so would be reliably identified from observed variables under different dependency structures and achieve better performance. (Yao, p. 1, Abstract "Experimental results on various datasets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not properly leverage history or nonstationarity information.")
Liu and Yao do not teach, but Li teaches: an emission model including a post-nonlinear transformation associated with distortion in instrument measurements; (Li, p. 1, Summary "This paper considers the joint estimation problem of state and unknown measurement noise covariance for nonlinear state-space models."; p. 2, 2.1 Problem formulation "Consider the nonlinear state and observation equations with additive noise terms... yk = gk(xk) + vk. (2) [an emission model, a post-nonlinear transformation] where k denotes the time index, xk... is the vector of state variables... wk~N(0,Qk) and vk~N(0, Rk) are independent identically distributed Gaussian process noise and measurement noise, [distortion in instrument measurements] respectively... and Rk is an unknown measurement noise diagonal covariance."; in light of specification [0042] '(1)… g(.) denote… the nonlinear emission model...' [0054]-[0055]'(2)… g1(.) denotes invertible post-nonlineat distortion… ηt… noises… g1(.) may model sensor or measurement distortion...')
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 Liu and Yao to incorporate the teachings of Li by including unknown measurement noise covariance for nonlinear state-space models. Doing so would achieve a more reasonable and simpler estimation for nonlinear state-space model with unknown noise. (Li, p. 2, 1 Introduction "sensors are located far away from each other and their working statuses are always uncorrelated. For those cases, it is more reasonable and simpler to consider diagonal measurement noise covariance, rather than full measurement noise covariance.")
Claims 8 and 15 recite substantially the same limitation as claim 1, therefore the rejection applied to claim 1 also apply to claims 8 and 15. In addition, Liu teaches: A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising… A system, comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform a method comprising: (Liu, p. 11, 4. Numerical experiments "We implement our model using PyTorch on a Linux workstation with TI-TAN RTX GPU."; the python code and its implementation on a workstation inherently teach all the computer components, such as processors, memory, input interface)
In regard to claims 2, 9 and 16, Liu does not teach, but Yao teaches: wherein each of the latent variables is identifiable from a corresponding observed data. (Yao, p. 1, 1 INTRODUCTION AND RELATED WORK "In both settings, we establish the identifiability of the latent factors and their causal influences, rendering them recoverable from the observed data. [identifiable from an observed data]")
The rationale for combining the teachings of Liu and Yao is the same as set forth in the rejection of claim 1.
In regard to claims 3, 10 and 17, Liu does not teach, but Yao teaches: wherein the first nonparametric transition function has a first input including one or more parent time-lagged variables of a latent factor and a second input including a first noise. (Yao, p. 1, 1 INTRODUCTION AND RELATED WORK "In the nonparametric setting, the generating process of each latent causal factor z_it is characterized by nonparametric assignment z_it = f_i (Pa(z_it), ε_it), in which the parents of z_it (i.e., the set of latent factors that directly cause z_it) [the parent time-lagged variables of a latent factor] together with noise term ε_it... [a first noise] generate z_it via unknown nonparametric function fi with some time delay."; p. 3, 2.2 OUR PROPOSED CONDITIONS "Theorem 1 (Nonparametric Processes)... let Pa(zit) denote the set of (time-delayed) parent nodes of zit... zj,t−τ ∈ Pa(zit)... (2)")
The rationale for combining the teachings of Liu and Yao is the same as set forth in the rejection of claim 1.
In regard to claims 7 and 14, Liu does not teach, but Yao teaches: wherein the neural network system includes: (Yao, p. 4, Figure 2 "LEAP: Encoder (A) and Decoder (D) with MLP or CNN for specific data types; (B) Bidirectional inference network that approximates the posteriors of latent variables z^1:T and (C) Causal process network [(A)+(B)+(C)+(D): the neural network system]")
an encoder configured to determine, using the neural network system, the one or more estimated latent variables; (Yao, p. 4, Figure 2 "LEAP: Encoder (A) and Decoder (D) with MLP orCNN for specific data types"; see Fig. 2, Encoder encodes x1:T to latent variables z^1:T; the neural network system includes (A) encoder)
an auxiliary predictor configured to generate, using the neural network system, a first latent-space prediction result based on the one or more estimated latent variables; and (Yao, p. 4, 3.1.1 TRANSITION PRIOR MODELING "The transition prior p(zˆt|{zˆt−τ}τ=1:L) [a first latent-space prediction result p(zˆt|{zˆt−τ}) based on the estimated latent variables z^t−τ] can thus be evaluated using factorized noise distributions…"; in light of specification [0082] "in the latent space p(z^t|z^_t-τ}τ=1:L)"; the neural network system includes (C) causal process network)
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a decoder configured to transform, using the neural network system, the first latent-space prediction result to the first prediction result in the observed space. (Yao, p. 4, Figure 2 "LEAP: Encoder (A) and Decoder (D) with MLP orCNN for specific data types"; see Fig. 2, Decoder decodes z^1:T to latent variables x^1:T; the neural network system includes (D) decoder)
The rationale for combining the teachings of Liu and Yao is the same as set forth in the rejection of claim 1.
Claims 4-5, 11-12 and 18-19 rejected under 35 U.S.C. 103 as being unpatentable over Liu, Yao and Li as applied to claims 3, 10 and 17, and in further view of Feng ("Factored Adaptation for Non-Stationary Reinforcement Learning" 20220330)
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In regard to claims 4, 11 and 18, Liu, Yao and Li do not teach, but Feng teaches: wherein the first nonparametric transition function has a third input including one or more time-varying change factors. (Feng, p. 1, Abstract "FANS-RL learns jointly the structure of a factored MDP and a factored representation of the time-varying change factors, [time-varying change factors] as well as the specific state components that they affect, via a factored non-stationary variational autoencoder..."; p. 2, 3. Factored Non-stationary MDPs "Definition 1 (FN-MDP). A Factored Non-stationary Markov Decision Process (FN-MDP) is defined... where S is the state space... θ_s is space of the change factors for transition dynamics... We define the factored state transition distribution P_s [the first transition function] as : P_s(s_t|s_t-1, ..., θs_t)... [a third input θs_t] We define the factored latent change factors transition distributions Pθs... Pθs (θs_t|θs_t−1) [time-varying change factors t-1, t-2, ...]"; the state transition function in the state-space model (SSM) shares the same concept as Markov Decision Processes (MDP))
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 Liu, Yao and Li to incorporate the teachings of Feng by including time-varying change factors. Doing so would make better approaches in terms of compactness of the latent state representation and robustness to varying degrees of non-stationarity. (Feng, p. 1, Abstract " Experimental results demonstrate that FANS-RL outperforms existing approaches in terms of rewards, compactness of the latent state representation and robustness to varying degrees of non-stationarity.")
In regard to claims 5, 12 and 19, Liu, Yao and Li do not teach, but Feng teaches: wherein the one or more time-varying change factors are associated with a second nonparametric transition function. (Feng, p. 2, 3. Factored Non-stationary MDPs "θs is space of the change factors [time-varying change factors] for transition dynamics... We adapt the generative process from Eq. 1 & 2 and introduce two additional equations to model the dynamics of the latent change factors... θs_t = gs(c...θs_t-1, εt)...[θs_t-1 being associated with a function gs, a second nonparametric transition function, ] (3), εt..., the i.i.d. random noises."; a non-parametric function does not assume a fixed functional form between variables, e.g. variables θ and ε)
The rationale for combining the teachings of Liu, Yao, Li and Feng is the same as set forth in the rejection of claim 4.
Claims 6, 13 and 20 rejected under 35 U.S.C. 103 as being unpatentable over Liu, Yao, Li and Feng as applied to claims 5, 12 and 19, and in further view of Deb ("Spatio-temporal models with space-time interaction and their applications to air pollution data" 20171231)
In regard to claims 6, 13 and 20, Liu, Yao and Li do not teach, but Feng teaches: wherein the second nonparametric transition function includes an input including a second noise, and (Feng, p. 2, 3. Factored Non-stationary MDPs "We adapt the generative process from Eq. 1 & 2 and introduce two additional equations to model the dynamics of the latent change factors... θs_t = gs(c...θs_t-1, εt)... (3), εt..., the i.i.d. random noises. [gs takes εt as an input, a second noise]")
The rationale for combining the teachings of Liu, Yao, Li and Feng is the same as set forth in the rejection of claim 4.
Liu, Yao, Li and Feng do not teach, but Deb teaches: wherein the first noise and the second noise are mutually independent. (Deb, p. 10, 3.1 The proposed model "In the proposed model, we consider the following hierarchical structure: Y(s, t) := U(s, t) + ε(s, t), (3.1) where U(s, t) describes a spatio-temporal process and ε(s, t) denotes a white noise process, [space-time white noise, the first/second noise being independent across time and space] to account for the measurement errors. We assume the white noise process to follow heteroskedastic N(0, σ2i) distributions independently..."; in light of specification [0054] [0074], the first noise ϵit being spatially and temporally independent, and the second noise ϛt being spatially and temporally independent)
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 Liu, Yao, Li and Feng to incorporate the teachings of Deb by including space-time white noise. Doing so would allow the method to accommodate errors. (Deb, p. 10, 3.1 The proposed model "In the proposed model, we consider the following hierarchical structure: Y(s, t) := U(s, t) + ε(s, t), (3.1) where U(s, t) describes a spatio-temporal process and ε(s, t) denotes a white noise process, to account for the measurement errors.")
Response to Arguments
Applicant's amendments with respect to the claim objections and claim rejections 112 (b) have been fully considered and are sufficient to overcome the objections. The objections have been withdrawn.
Applicant's arguments with respect to the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive:
Applicant argues: (p. 7) A. Under Step 2A Prong One, claim 1 does not recite mental process-type abstract ideas… Claim 1 as amended provides a method of performing time series forecasting using a neural network system… the claimed "method" in amended claim 1 is performed using different hardware components of a computing device and is performed in a way that cannot be practically performed in the human mind. Accordingly, under Step 2A Prong One, claim 1 does not recite any mental processes or concepts performed in the human mind.
Examiner answers: if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical concepts but for the recitation of generic computer components, then it is still in the mental processes or mathematical concepts.
Applicant argues: (p. 7-8) B. Under Step 2A Prong Two, claim 1 qualifies as eligible subject matter because it as a whole integrates the alleged abstract idea into a practical application… As explained in the Specification, the claim as a whole integrates the alleged abstract idea into a practical application for systems better handling "sensor or measurement distortion that usually happens when the underlying processes are measured with instruments," thereby improving the performance of time series forecasting.
Examiner answers: The limitation “an emission model including a post-nonlinear transformation associated with distortion in instrument measurements;” in light of specification [0054] is evaluated as a mathematical concept – mathematical equation. If the claim is directed to a judicial exception, it cannot provide an improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement.”
Applicant's arguments with respect to the rejection of the claims under 35 U.S.C. 103 have been fully considered but they are moot:
Applicant argues: (p. 9) In fact Liu is silent to any emission model, let alone an emission model "including a post- nonlinear transformation associated with distortion in instrument measurements."
Examiner answers: the arguments do not apply to the references (Li) being used in the current rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/S.C./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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