DETAILED ACTION
This action is responsive to Application 18/353,243 filed on 07/17/2023. Claims 1-7 are pending in the case. Claims 1, 6, and 7 are independent claims.
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 .
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.
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-155772, filed on 09/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/17/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claims are directed towards judicial exceptions without significantly more.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites converting first data …into an isometric space with same probability distribution as the data space according to a predetermined transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites determining whether or not to accept the second data…based on the transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
non-transitory computer-readable recording medium storing a program for causing a computer to execute a sampling process recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
in a latent space into second data in a data space…that has the latent space transformable specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
by using a machine learning model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (d) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites …convert the first data into the second data which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass . See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
the converting into the second data uses a variational autoencoder (VAE) as the machine learning model and decodes the first data with a decoder of the VAE to recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 3:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites encode the first sample…to calculate a first mean value, a first variance, and a first metric tensor which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites encode the second data … to calculate a second mean value, a second variance , and a second metric tensor which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites calculate the acceptance probability based on the first mean value, the first variance , the first metric tensor, the second mean value, the second variance , and the second metric tensor which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C)))
Subject Matter Eligibility Analysis Step 2A Prong 2:
with an encoder of the VAE recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 4:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
executing training of the machine learning model by using the second sample recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 5:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
executing, in parallel, a sampling process that includes the converting into the second data, the determining whether or not to accept the second data, and the accepting the second data as the second sample with each of a plurality of processors recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
executing training of the machine learning model by using the second sample accepted by each of the plurality of processors recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites converting first data …into an isometric space with same probability distribution as the data space according to a predetermined transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites determining whether or not to accept the second data…based on the transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
in a latent space into second data in a data space…that has the latent space transformable specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
by using a machine learning model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (a) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (c) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) and (d) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites convert first data …into an isometric space with same probability distribution as the data space according to a predetermined transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites determine whether or not to accept the second data…based on the transformation rule which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
a processor coupled to the memory and configured to: a memory; and a processor coupled to the memory and configured to recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
in a latent space into second data in a data space…that has the latent space transformable specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
by using a machine learning model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
output the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional element (d) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) (d) (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
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.
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.
103 Claim Rejections, Claims 1, 2, 4, 6, 7
Claims 1, 2, 4, 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brofos et al(“Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals” henceforth known as Brofos) in view of Nakagawa et al(“Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding” henceforth known as Nakagawa)
Regarding claim 1:
Brofos discloses converting first data in a latent space into second data in a data space by using a machine learning model that has the latent space transformable(Brofos, Page 7, Col. 1, Paragraph 1, “We estimate an affine normalizing flow from a Gaussian base distribution in order to sample from the target” where the base distribution corresponds to a first data in latent space and the normalizing-flow transformation is applied producing a normalize-flow proposal corresponds to a second data that is converted from a first data (See also Brofos, Page 1, Col. 2, Paragraph 3, “Normalizing flows are defined by a parametric, smooth and invertible function which transforms a simple distribution (e.g., a Gaussian) into a more complex one(e.g., natural images) and uses the change-of-variables formula to exactly determine the resulting probability density function in the complex space.”))… according to a predetermined transformation rule(Brofos, Page 30, Paragraph 3, “As a simple example, we consider sampling Normal(1, 1/2) using a proposal distribution Normal(µ,σ2); the proposal distribution can be interpreted as a simple normalizing flow consisting of a shift and scale applied to a standard normal base distribution”)
Brofos discloses determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule(Brofos, Page 34, Algorithm 1 and Equation 199, “Generate u ∼ Uniform(0,1) and compute the Metropolis-Hastings accept-reject decision. a ←u<min
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if a then Accept the proposal xn+1 ← xn+1. Else Remain at current state xn+1 ← xn”
where xn is the current chain state and xn+1 a second data with the Metropolis-Hastings accept/reject test corresponds to determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule
Brofos discloses outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted(Brofos, Page 34, Algorithm 1, “Sample a proposal state from the current proposal distribution ˜xn+1 ∼ ˜ Πθn−1 …if a then Accept the proposal xn+1 ← ˜xn+1. Else Remain at current state xn+1 ← xn” where xn corresponds to a current/first sample, ˜xn+1 corresponds to a proposed second data, xn+1 corresponds to a second sample of the transition state and where accepting the proposal resulting in xn+1 ← ˜xn+1 corresponds to outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted)
Brofos does not explicitly disclose, however Nakagawa discloses …into an isometric space with same probability distribution the data space…(Nakagawa, Page 2, Col. 2, Paragraph 3, “the probability density of input data at the given metric is preserved in the isometric embedding space.” where the input data probability density being preserved in the isometric embedding space corresponds to an isometric space with same probability distribution the data space)
References Brofos and Nakagawa are analogous art because they are from the same field of endeavor of using probabilistic machine learning to deal with generative models, probability distributions and nonlinear mappings.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Brofos and Nakagawa before him or her, to modify the predetermined transformation of Brofos to include the isometric transformation of Nakagawa to allow for preservation of the probability density of input data and to allow for latent-variable meaning to become quantitatively analyzable . The suggestion/motivation for doing so would have been Nakagawa, Page 2, Col. 2, Paragraph 3,”First of all, the probability density of input data at the given metric is preserved in the isometric embedding space…Thus, the isometric embedding is a powerful tool to analyse input data”.
Regarding claim 2:
The rejection of claim 1 with prior art is incorporated and further:
Nakagawa discloses wherein the converting into the second data uses a variational autoencoder (VAE) as the machine learning model, and decodes the first data with a decoder of the VAE to convert the first data into the second data(Nakagawa, Page 2, Col. 1, Paragraph 3, “ “The original VAE model consists of a latent variable with fixed…a parametric encoder Encφ…and a parametric decoder Decθ : z ⇒ ˆx. In the encoder, qφ(z|x) = N(z;µ(x),σ(x)) is provided by estimating parameters µ(x) and σ(x)” where the latent variable z corresponds to a first data that is mapped into ˆx that corresponds to a second data)
Regarding claim 4:
The rejection of claim 1 with prior art is incorporated and further:
Brofos discloses executing training of the machine learning model by using the second sample(Brofos, Page 2, Col. 1, Paragraph 2,“ “This circumstance includes the case wherein the accepted proposal sampled from the normalizing flow is also used in the computation of the adaptation,” where the proposal being accepted and used in adaptation of the model corresponds to executing training of the machine learning model by using the second sample as adapting the machine learning model corresponds to training the machine learning model (See also Brofos, Page 6, Col. 2, Paragraph 1, “The specific training procedure used by these samplers is to adapt parameters of the normalizing flow as … are a sequence of adaptation step-sizes.” and Brofos, Page 30, Paragraph 4,“We consider adapting the parameters of the proposal distribution by computing the maximum likelihood estimates of the mean and standard deviation using the accepted samples”))
Regarding claim 6:
Brofos discloses converting first data in a latent space into second data in a data space by using a machine learning model that has the latent space transformable(Brofos, Page 7, Col. 1, Paragraph 1, “We estimate an affine normalizing flow from a Gaussian base distribution in order to sample from the target” where the base distribution corresponds to a first data in latent space and the normalizing-flow transformation is applied producing a normalize-flow proposal corresponds to a second data that is converted from a first data (See also Brofos, Page 1, Col. 2, Paragraph 3, “Normalizing flows are defined by a parametric, smooth and invertible function which transforms a simple distribution (e.g., a Gaussian) into a more complex one(e.g., natural images) and uses the change-of-variables formula to exactly determine the resulting probability density function in the complex space.”))… according to a predetermined transformation rule(Brofos, Page 30, Paragraph 3, “As a simple example, we consider sampling Normal(1, 1/2) using a proposal distribution Normal(µ,σ2); the proposal distribution can be interpreted as a simple normalizing flow consisting of a shift and scale applied to a standard normal base distribution”)
Brofos discloses determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule(Brofos, Page 34, Algorithm 1 and Equation 199, “Generate u ∼ Uniform(0,1) and compute the Metropolis-Hastings accept-reject decision. a ←u<min
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,
if a then Accept the proposal xn+1 ← xn+1. Else Remain at current state xn+1 ← xn”
where xn is the current chain state and xn+1 a second data with the Metropolis-Hastings accept/reject test corresponds to determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule
Brofos discloses outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted(Brofos, Page 34, Algorithm 1, “Sample a proposal state from the current proposal distribution ˜xn+1 ∼ ˜ Πθn−1 …if a then Accept the proposal xn+1 ← ˜xn+1. Else Remain at current state xn+1 ← xn” where xn corresponds to a current/first sample, ˜xn+1 corresponds to a proposed second data, xn+1 corresponds to a second sample of the transition state and where accepting the proposal resulting in xn+1 ← ˜xn+1 corresponds to outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted)
Brofos does not explicitly disclose, however Nakagawa discloses …into an isometric space with same probability distribution the data space…(Nakagawa, Page 2, Col. 2, Paragraph 3, “the probability density of input data at the given metric is preserved in the isometric embedding space.” where the input data probability density being preserved in the isometric embedding space corresponds to an isometric space with same probability distribution the data space)
References Brofos and Nakagawa are analogous art because they are from the [insert the phrase “same field of endeavor” or “problem-solving area,” and the name of that field or area.]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Brofos and Nakagawa before him or her, to modify the predetermined transformation of Brofos to include the isometric transformation of Nakagawa to allow for preservation of the probability density of input data and to allow for latent-variable meaning to become quantitatively analyzable . The suggestion/motivation for doing so would have been Nakagawa, Page 2, Col. 2, Paragraph 3,”First of all, the probability density of input data at the given metric is preserved in the isometric embedding space…Thus, the isometric embedding is a powerful tool to analyse input data”.
Regarding claim 7:
Brofos discloses converting first data in a latent space into second data in a data space by using a machine learning model that has the latent space transformable(Brofos, Page 7, Col. 1, Paragraph 1, “We estimate an affine normalizing flow from a Gaussian base distribution in order to sample from the target” where the base distribution corresponds to a first data in latent space and the normalizing-flow transformation is applied producing a normalize-flow proposal corresponds to a second data that is converted from a first data (See also Brofos, Page 1, Col. 2, Paragraph 3, “Normalizing flows are defined by a parametric, smooth and invertible function which transforms a simple distribution (e.g., a Gaussian) into a more complex one(e.g., natural images) and uses the change-of-variables formula to exactly determine the resulting probability density function in the complex space.”))… according to a predetermined transformation rule(Brofos, Page 30, Paragraph 3, “As a simple example, we consider sampling Normal(1, 1/2) using a proposal distribution Normal(µ,σ2); the proposal distribution can be interpreted as a simple normalizing flow consisting of a shift and scale applied to a standard normal base distribution”)
Brofos discloses determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule(Brofos, Page 34, Algorithm 1 and Equation 199, “Generate u ∼ Uniform(0,1) and compute the Metropolis-Hastings accept-reject decision. a ←u<min
1
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π
x
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1
π
θ
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π
θ
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x
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+
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,
if a then Accept the proposal xn+1 ← xn+1. Else Remain at current state xn+1 ← xn”
where xn is the current chain state and xn+1 a second data with the Metropolis-Hastings accept/reject test corresponds to determining whether or not to accept the second data as a transition state in a Markov chain Monte Carlo method from an accepted first sample in the data space with an acceptance probability based on the transformation rule
Brofos discloses outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted(Brofos, Page 34, Algorithm 1, “Sample a proposal state from the current proposal distribution ˜xn+1 ∼ ˜ Πθn−1 …if a then Accept the proposal xn+1 ← ˜xn+1. Else Remain at current state xn+1 ← xn” where xn corresponds to a current/first sample, ˜xn+1 corresponds to a proposed second data, xn+1 corresponds to a second sample of the transition state and where accepting the proposal resulting in xn+1 ← ˜xn+1 corresponds to outputting the second data as a second sample of the transition state from the first sample when the second data is determined to be accepted)
Brofos does not explicitly disclose, however Nakagawa discloses …into an isometric space with same probability distribution the data space…(Nakagawa, Page 2, Col. 2, Paragraph 3, “the probability density of input data at the given metric is preserved in the isometric embedding space.” where the input data probability density being preserved in the isometric embedding space corresponds to an isometric space with same probability distribution the data space)
References Brofos and Nakagawa are analogous art because they are from the [insert the phrase “same field of endeavor” or “problem-solving area,” and the name of that field or area.]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Brofos and Nakagawa before him or her, to modify the predetermined transformation of Brofos to include the isometric transformation of Nakagawa to allow for preservation of the probability density of input data and to allow for latent-variable meaning to become quantitatively analyzable . The suggestion/motivation for doing so would have been Nakagawa, Page 2, Col. 2, Paragraph 3,”First of all, the probability density of input data at the given metric is preserved in the isometric embedding space…Thus, the isometric embedding is a powerful tool to analyse input data”.
103 Claim Rejections, Claim 3
Claim 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brofos et al(“Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals” henceforth known as Brofos) in view of Nakagawa et al(“Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding” henceforth known as Nakagawa) and further in view of Monroe et al(“Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders” henceforth known as Monroe)
Regarding claim 3:
The rejection of claim 2 with prior art is incorporated and further:
Brofos-Nakagawa does not disclose the follow elements/limitations:
does encode the first sample with an encoder of the VAE to calculate a first mean value, a first variance, and a first metric tensor
encode the second data with the encoder of the VAE to calculate a second mean value, a second variance, and a second metric tensor
calculate the acceptance probability based on the first mean value, the first variance, the first metric tensor, the second mean value, the second variance, and the second metric tensor
Monroe discloses encode the first sample with an encoder(Monroe, Page 9, Equation 9, q(z2 ∣ x2;ϕ)) of the VAE to calculate a first mean value, a first variance, and a first metric tensor(Monroe, Page 6, Paragraph 1, “We follow typical convention in VAEs by using a multivariate normal distribution with zero covariance for q(z ∣ x; ϕ), with the means and variances represented by ϕ and determined by neural network g(x)” and Monroe, Page 10, Paragraph 1, “All models are implemented in TensorFlow” where using VAEs by using (q(z2 ∣ x2;ϕ)) corresponds to calculating a first mean value and a first variance and where values used to calculate q with (z2 ∣ x2;ϕ) are stored as TensorFlow tensors is considered calculating a first matrix tensor)
Monroe discloses encode the second data with the encoder(q(z1 ∣ x1 ; ϕ)) of the VAE to calculate a second mean value, a second variance, and a second metric tensor(Monroe, Page 6, Paragraph 1, “We follow typical convention in VAEs by using a multivariate normal distribution with zero covariance for q(z ∣ x; ϕ), with the means and variances represented by ϕ and determined by neural network g(x)” and Monroe, Page 10, Paragraph 1, “All models are implemented in TensorFlow” where using VAEs by using (q(z1 ∣ x1;ϕ)) corresponds to calculating a second mean value and a second variance and where values used to calculate q with (z2 ∣ x2;ϕ) are stored as TensorFlow tensors is considered calculating a second matrix tensor)
Monroe discloses calculate the acceptance probability based on the first mean value, the first variance , the first metric tensor, the second mean value, the second variance, and the second metric tensor(Monroe, Page 7, Paragraph 2, “With the following ratio of acceptance probabilities, we impose superdetailed balance…[Equation 9]” where equation 9 being a ratio of acceptance probabilities corresponds to calculating the acceptance probability based on the first mean value, the first variance , the first metric tensor, the second mean value, the second variance, and the second metric tensor)
References Brofos-Nakagawa and Monroe are analogous art because they are from the same field of endeavor of using machine-learning with the Monte Carlo methods for efficient sampling of probability distributions.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Brofos-Nakagawa and Monroe before him or her, to modify the model of Brofos-Nakagawa to include the use of Monte Carlo with a VAE as disclosed in Monroe to provide the ability to learn a low-dimensional with accelerating sampling. The suggestion/motivation for doing so would have been Nakagawa, Page 2, Col. 2, Paragraph 3,” Here, we demonstrate how training a VAE not only learns a low-dimensional collective variable and its probability density, but also efficient Monte Carlo (MC) moves that pass into and out of that latent space, accelerating sampling.”
103 Claim Rejections, Claim 5
Claim 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brofos et al(“Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals” henceforth known as Brofos) in view of Nakagawa et al(“Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding” henceforth known as Nakagawa) and further in view of Gabrie et al(“Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods” henceforth known as Gabrie)
Regarding claim 5:
The rejection of claim 1 with prior art is incorporated and further:
Brofos discloses executing, in parallel(Brofos, Pages 7-8, Col. 2, Paragraph 3, “Because the distribution is high dimensional and multimodal, it is necessary1 to run multiple parallel walkers initialized around the different modes… In the experiments, we initialize 100 walkers with uneven proportions in each mode (20-80) and test for the ergodicity of the parallel chains.”)
Gabrie discloses a sampling process that includes the converting into the second data, the determining whether or not to accept the second data, and the accepting the second data as the second sample(Page 2, Algorithm 1, Line 9, where the proposed θ`i is accepted with probability of acc(θi(k),θ`i), or otherwise θi(k + 1) = θi(k) {resampling step} corresponds to a sampling process that includes the converting into the second data, the determining whether or not to accept the second data, and the accepting the second data as the second sample as Algorithm 1, Line 7 shows drawing a base sample of θ`B,i to convert into a second data as shown in Algorithm 1, Line 8) with each of a plurality of processors and executing training of the machine learning model by using the second sample accepted by each of the plurality of processors(Gabrie, Page 3, Col. 2, Paragraph 1, “In practice, we run n walkers in parallel in the chain”)
References Brofos-Nakagawa and Gabrie are analogous art because they are from the same field of endeavor of using machine-learning and computational statistics to develop a normalizing-flow with Markov Chain Monte Carlo methods for sampling probability distributions.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Brofos-Nakagawa and Gabrie before him or her, to modify the parallelism of Brofos-Nakagawa to include concurrent sampling-and-learning structure of Gabrie to provide a structure for the parallelism chaining of Brofos. The suggestion/motivation for doing so would have been Page 2, Algorithm 1, where the concurrent sampling/training strategy in which the samples produced from the target are simultaneously used to train the flow.
Relevant Art
Adaptive Monte Carlo augmented with normalizing flows, Gabrie et al, as the publication discusses Monte Carlo normalizing flows.
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space, Kato et al, as the publication discusses scaled-isometric latent embedding.
Balanced Training of Energy-Based Models with Adaptive Flow Sampling, Grenioux et al, as the publication discusses training with adaptive flow sampling
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC, Kuzina et al, as the publication discusses Variation Autoencoders (VAE) with Markov Chain Monte Carlo(MCMC) techniques.
Conclusion
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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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