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 Objections
Claim 4 objected to because of the following informalities: "a training a" should be "a training of a". Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "performing … a training of a variational model … and performing … a training a variational model …" in lines 6-8. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “a variational model” is referring to one of the previously mentioned variational models in claim 1, or a new variational model.
Claim 5 recites the limitation "performing sampling by using a trained variational model …" in line 3. There is insufficient antecedent basis for this limitation in the claim. It is unclear which of the previously mentioned variational models “a trained variational model” is referring to, or if it referring to a new variational model.
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.
Claim(s) 1,2, and 4-7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20200272682 A1 by Dote et al., hereafter Dote.
Regarding claim 1, Dote teaches:
A non-transitory computer-readable recording medium (“memory”) storing a sampling program for causing a computer to execute a process (“by executing a program stored in memory”) comprising: (Paragraph [0064])
performing sampling (Paragraph [0040], “generating a sample”) of a second probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by adding an inverse temperature parameter (Paragraph [0036], “In equation (3), β denotes an inverse temperature (reciprocal of a temperature value representing a temperature). The state transition involving an increase in energy is also allowable in terms of the probability.”) based on an inverse temperature that is a physical amount to a first probability distribution (Paragraph [0040], “…obtained through a process of repeating a state transition with a fixed temperature using the MCMC method or values based on the state.”) and training (Paragraph [0039], optimizing using “the optimization apparatus”) a first variational model (Paragraph [0040], “sampler”) based on first data obtained through sampling (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”);
performing sampling (Paragraph [0040], “generating a sample”) of a third (Paragraph [0039], “The optimization apparatus repeatedly performs the above-described processing a certain number of trials.”) probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by increasing a value of the inverse temperature parameter (Paragraph [0037], “Based on the energy change ΔE in response to the state transition in which the value of the state variable changes”), by using the trained first variational model (Paragraph [0040], “sampler”) and training a second (Paragraph [0039], repeated performance) variational model (Paragraph [0040], “sampler”) based on sampled second data (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”); and
outputting a sample (“…outputting, as the sample,…”) that corresponds to the first probability distribution (“…values based on the state”), based on a result of the sampling of the third probability distribution (“state transition) by using the trained second variational model (sampler). (Paragraph [0040])
Regarding claim 2, Dote teaches the material in claim 1, and additionally teaches:
setting (“sets”) a model parameter (“temperature value T”) of the trained first variational model (“in accordance with a temperature change schedule”) as an initial value (“initial value”)(Paragraph [0153) and training the second (Paragraph [0039], repeated performance) variational model (Paragraph [0040], “sampler”) by using the second data (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”).
Regarding claim 4, Dote teaches the material in claim 1, and additionally teaches:
generating two different probability distributions (“simultaneously performing the MCMC method…”) obtained by adding each of two different inverse temperature parameters (Paragraph [0213], “trials for replicas in which different temperature values T (in the example of FIG. 18, inverse temperatures) are set.”) based on the inverse temperature (“..using a plurality of temperatures”) to the first probability distribution; (Paragraph [0210]) and
performing, by using data sampled from one probability distribution (energy and state of a replica), a training of a variational model (the replica) based on another probability distribution (energy and state from a replica to exchange with)and performing, by using data sampled from the another probability distribution, a training a variational model (the replica to exchange with) based on the one probability distribution (energy and state from original replica). (Paragraph [0215])
Regarding claim 5, Dote teaches the material in claim 1, and additionally teaches:
performing sampling (Paragraph [0152], “sampling operation”) by using a trained variational model (Paragraph [0153], “control unit”) trained by using sampled data from a probability distribution (Paragraph [0153], “in accordance with a temperature change schedule”), from an expanded probability distribution obtained by adding an inverse temperature parameter (Paragraph [0153], “temperature value T”) based on the inverse temperature to the probability distribution and performing a training of a variational model (Paragraph [0156], “the control unit 17 updates the temperature value T to decrease in accordance with the temperature change schedule”) that trains the expanded probability distribution by using the sampled data;
repeating the processing of performing the training (Paragraph [0157], “…the processing is repeated”) until the increased value of the inverse temperature parameter reaches a predetermined value (Paragraph [0157], “If the number of times, the processing of steps S21 to S26 has been performed has not reached the number of trials N2…” The temperature value T is decreased by a set amount each trial, so after a certain number of trials N2 will reach a predetermined value) while increasing the value of the inverse temperature parameter (Fig. 11, step S28, Paragraph [0156], “updates the temperature value T”); and
outputting data ( “outputs the state”) obtained by performing sampling by using the trained variational model that has trained an immediately preceding probability distribution (“If the number of times the processing of steps S21 to S26 has been performed has reached the number of trials N2”), from the expanded probability distribution obtained by increasing the value of the inverse temperature parameter (“at the time of the lowest energy”) that is the predetermined value as an optimum solution of the probability distribution (“as a solution to the combinatorial optimization problem”), after the processing of repeating has been completed. (Paragraph [0157])
Regarding claim 6, Dote teaches:
A computer-performed sampling method comprising:
performing sampling (Paragraph [0040], “generating a sample”) of a second probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by adding an inverse temperature parameter (Paragraph [0036], “In equation (3), β denotes an inverse temperature (reciprocal of a temperature value representing a temperature). The state transition involving an increase in energy is also allowable in terms of the probability.”) based on an inverse temperature that is a physical amount to a first probability distribution (Paragraph [0040], “…obtained through a process of repeating a state transition with a fixed temperature using the MCMC method or values based on the state.”) and training (Paragraph [0039], optimizing using “the optimization apparatus”) a first variational model (Paragraph [0040], “sampler”) based on first data obtained through sampling (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”);
performing sampling (Paragraph [0040], “generating a sample”) of a third (Paragraph [0039], “The optimization apparatus repeatedly performs the above-described processing a certain number of trials.”) probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by increasing a value of the inverse temperature parameter (Paragraph [0037], “Based on the energy change ΔE in response to the state transition in which the value of the state variable changes”), by using the trained first variational model (Paragraph [0040], “sampler”) and training a second (Paragraph [0039], repeated performance) variational model (Paragraph [0040], “sampler”) based on sampled second data (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”); and
outputting a sample (“…outputting, as the sample,…”) that corresponds to the first probability distribution (“…values based on the state”), based on a result of the sampling of the third probability distribution (“state transition) by using the trained second variational model (sampler). (Paragraph [0040])
Regarding claim 7, Dote teaches:
An information processing apparatus comprising:
a memory, and
a processor coupled to the memory and configured to: (Paragraph [0064], “In such a case, the processor performs the processing described above by executing a program stored in a memory”)
perform sampling (Paragraph [0040], “generating a sample”) of a second probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by adding an inverse temperature parameter (Paragraph [0036], “In equation (3), β denotes an inverse temperature (reciprocal of a temperature value representing a temperature). The state transition involving an increase in energy is also allowable in terms of the probability.”) based on an inverse temperature that is a physical amount to a first probability distribution (Paragraph [0040], “…obtained through a process of repeating a state transition with a fixed temperature using the MCMC method or values based on the state.”) and training (Paragraph [0039], optimizing using “the optimization apparatus”) a first variational model (Paragraph [0040], “sampler”) based on first data obtained through sampling (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”);
perform sampling (Paragraph [0040], “generating a sample”) of a third (Paragraph [0039], “The optimization apparatus repeatedly performs the above-described processing a certain number of trials.”) probability distribution (Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”) obtained by increasing a value of the inverse temperature parameter (Paragraph [0037], “Based on the energy change ΔE in response to the state transition in which the value of the state variable changes”), by using the trained first variational model (Paragraph [0040], “sampler”) and training a second (Paragraph [0039], repeated performance) variational model (Paragraph [0040], “sampler”) based on sampled second data (Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”); and
output a sample (“…outputting, as the sample,…”) that corresponds to the first probability distribution (“…values based on the state”), based on a result of the sampling of the third probability distribution (“state transition) by using the trained second variational model (sampler). (Paragraph [0040])
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.
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dote in view of State Space Emulation and Annealed Sequential Monte Carlo for High Dimensional Optimization by Cai et al., hereafter Cai.
Regarding claim 3, Dote teaches the material in claim 2, and additionally teaches:
wherein in the training of the first variational model, acquiring the first data ((Dote) Paragraph [0039], “the energy change that occurs in response to each of the state transitions and the temperature value”) from the second probability distribution through sampling by using the Monte Carlo method ((Dote) Paragraph [0040], “…obtained through a process of repeating a state transition with a fixed temperature using the MCMC method or values based on the state.” MCMC stands for Markov Chain Monte Carlo) and training the first variational model ((Dote) Paragraph [0040], “sampler”)that trains ((Dote) Paragraph [0039], optimizing using “the optimization apparatus”) the first probability distribution based on the first data ((Dote) Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”), and
wherein in the training of the second variational model, sampling ((Dote) Paragraph [0040], “generating a sample”) on the second data from the third ((Dote) Paragraph [0039], “The optimization apparatus repeatedly performs the above-described processing a certain number of trials.”) probability distribution ((Dote) Paragraph [0040], “a probability distribution indicating occupancy probabilities of the individual states is the Boltzmann distribution”), by a […] Monte Carlo method […]((Dote) Paragraph [0040], “…obtained through a process of repeating a state transition with a fixed temperature using the MCMC method or values based on the state) and training the second ((Dote) Paragraph [0039], repeated performance) variational model ((Dote) Paragraph [0040], “sampler”)that trains the third probability distribution((Dote) Paragraph [0039], “updates the state”), based on the second data ((Dote) Paragraph [0039], “Based on the energy change that occurs in response to each of the state transitions and the temperature value”).
Dote does not explicitly disclose:
… by a self-learning Monte Carlo method that uses the trained first variational model as a proposal probability distribution …
Cai teaches:
A sequential Monte Carlo Method (SMC) that self-learns (update weights) using a function (qt) including a previous model (x^(i)_t-1_) as a proposal probability distribution ((Cai) pg. 9. Figure 1; pg. 9. Section 3.1. paragraph 2, “The function qt(*) in the propagation step in Figure 1 is the proposal distribution.”)
Cai and Dote are analogous art because they are in the same area of invention: simulated annealing using Monte Carlo methods.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the self-learning sequential Monte Carlo method that uses a trained first variational model as a proposal probability distribution taught by Cai and the training of the second variational model as Dote teaches. The motivation for this would be to take advantage of a sequential Monte Carlo method’s properties that make it extremely powerful in sampling from complex dynamic systems.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to sampling annealing, and Monte Carlo methods.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN H LAI whose telephone number is (571)272-8628. The examiner can normally be reached Monday - Friday 7:30am-5:00pm.
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D. H. L.
Examiner
Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights