DETAILED ACTION
This action is responsive to the Application filed on 03/23/2026. Claims 1-6 are pending in the case. Claims 1, 5 and 6 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/23/2026 has been entered.
Response to Arguments
Applicant's arguments filed 03/23/2026 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 101 rejection:
Applicant argues that a claim with limitations that cannot practically be performed in the mind does not recite a mental process. Applicant further notes that performing resampling and handling of replicas involves simultaneously tracking and processing of multiple replicas. Additionally, the human mind cannot duplicate or eliminate replicas and therefore require mechanistic manipulation of stored replicas states.
Examiner disagrees with the assertion that sampling and tracking of replicas cannot be performed in the mind. The claims describe the sampling and updating of abstract values of the replicas of a Boltzmann machine . The sampling and tracking and updating of replicas is merely selection and manipulation of abstract data which can be performed in the mind. While the claim recites the storing of the replicas (an additional element), updating and selection does not require mechanistic processes. It is only the storing in computer memory which necessarily cannot be performed in the mind and is consequently analyzed in the subsequent steps in the flow chart.
Applicant further argues that human cognition is not capable of realistically performing such tracking and updating of multiple replicas.
Examiner disagrees. Nothing in the claims indicates the variable tracking cannot be performed in the mind. The claim does not provide any details regarding the features which make such tracking unrealistic for the mind. Merely being variables of a Boltzmann machine or a combinatorial optimization problem sets no limits on the updating of the abstract variables.
Applicant further notes that performing the update process on “a plurality of circuits” provides a concrete computer implemented method for efficiently performing population annealing optimization and further that storing and resampling as described reduces computational cost.
Examiner disagrees. As noted in MPEP 2106.05(f), performing an abstract idea on a generic plurality of circuits or computers does not itself provide an improvement or integrate the judicial exception. The claim sets no limits on the specificity or particularity of these circuits such that they would be particularly enabled for integrating the judicial exception.
Storing and reusing data from memory can not be considered and improvement precisely because, as noted in the rejection “storing and retrieving information in memory” is well understood, routine and conventional activity. Examiner notes that if the storing or retrieving is performing in a particular way so as to avoid repeated recalculation (beyond merely storing and retrieving) such limitations should be reflected in the claim.
Further Applicant argues that the claims includes elements which are significantly more because the claims avoid unnecessary recalculation thus reducing computational resources.
Examiner disagrees. As noted previously causing generic computers to perform variable updates and subsequently storing and retrieving those updates do not improve the judicial exception at least because the courts have identified storing and retrieving information in memory as not providing significantly more. As stated above, any additional elements which describe how the reading and storing is performed (rather than only what abstract data is read in stored) may reflect the supposed improvement. However, no such limitations are claimed.
The rejection has been updated in view of the amendments.
With respect to the 35 U.S.C. 102 rejection in view of Bagherbeik:
Applicant argues that the Swap resampling process described by Bagherbeik differs from the claim method. Particularly, noting that the claims require duplication of first replicas to eliminates second replicas.
Examiner notes the rejection has been updated in view of Weigel, which address the replica duplication and elimination for future new second replica populations.
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 1-6 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding Claim 1, 5 and 6
Under step 1, claim 1 is directed to A non-transitory computer-readable storage medium, which is directed to a product of manufacture, one of the statutory categories. Under step 1, claim 5 is directed to A data processing apparatus, which is directed to a machine, one of the statutory categories. Under step 1, claim 6 is directed to A data processing method, which is directed to a process, one of the statutory categories.
Under Step 2A Prong 1, the claims recites the following limitations which are considered mental evaluations “an update process of updating a value of one of the plurality of discrete variables, the value of the evaluation function, and the values of the plurality of local fields… based on a set value of a temperature parameter and the values of the plurality of local fields stored in the second memory… and each time a number of iterations of the update process reaches a predetermined value, performing a resampling process of a population annealing method, wherein the resampling process involves duplicating a first replica of the plurality of replicas and eliminating a second replica of the plurality of replicas different from the first replica so that two first replicas are generated to search for a solution in a same area of a search space… based on the values of the plurality of discrete variables of the plurality of replicas and values of the evaluation function of the plurality of replicas,”
These steps describe mental manipulations of abstract data including updating and sampling of values associated or related to the replicas. Duplicating and updating values is an evaluation which can be made in the mind.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) the limitations “storing a computer program that causes a computer to perform a process… by the computer … a processor that performs a process… performing, by the computer… each of the plurality of replicas being processed by one of the plurality of replica processing circuits … causing a plurality of replica processing circuits to repeatedly perform” amounts to mere instructions to apply a computer technology to an abstract idea, see MPEP 2106.05(f). In addition, the claim recites additional element(s) “storing a computer program that causes a computer to perform a process comprising …storing, in a first memory, values of a plurality of discrete variables included in an evaluation function of a Boltzmann machine to which a combinatorial optimization problem is transformed, and values of a plurality of local fields with respect to each of a plurality of replicas for the Boltzmann machine, the plurality of local fields each representing a change occurring in a value of the evaluation function when a value of one of the plurality of discrete variables is changed…storing, in a second memory provided for each of the plurality of replicas, the values of the plurality of discrete variables and the values of the plurality of local fields with respect to the each of the plurality of replicas… and storing the read values of the plurality of discrete variables and the read values of the plurality of local fields in the second memory provided for the duplication of the first replica”, “and reading, in response to a duplication of the first replicas being created the resampling process, the values of the plurality of discrete variables of the first replica and the values of the plurality of local fields of the first replica from the first memory or the second memory provided for the first replica,” that amounts to adding insignificant extra-solution activity to the judicial exception. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further, the additional elements, identified previously as insignificant extra-solution activities are considered well-understood, routine, conventional activities, for the following reasons. Examiner notes that storing data in memory amounts to receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2106.05(d)(II)(i) and MPEP 2106.05(d)(II)(iv). Accordingly, “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner”. As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 2
The claim is directed the same category identified in the parent claim. The claim recites the following limitations “wherein the update process is performed by the plurality of replica processing circuits in parallel for the plurality of replicas” Under Step 2A Prong 1, these limitations do not describe additional abstract ideas beyond those described in the parent claim.
Furthermore, under step 2A Prong 2 and 2B: The judicial exception in not integrated into a practical application or provide significantly more. In particular, the limitations “wherein the update process is performed by a plurality of replica processing circuits in parallel for the plurality of replicas” is generally linking the use of the judicial exception to a particular technological environment or field of use. The limitation is merely an “incidental or token addition to the claim that did not alter or affect how” the claimed steps are performed, see MPEP 2106.05(h). Alternatively, the same limitation is also considered mere instructions to apply a computer technology to an abstract idea. The limitation does not set limits on the functions of the circuits but only that they are used to perform the abstract idea. see MPEP 2106.05(f). Accordingly, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 3
The claim is directed the same category identified in the parent claim. The dependent claim recites the following additional limitations which recite abstract ideas:
“detecting a set of discrete variables that are not coupled to each other, based on weight coefficients included in the evaluation function, the weight coefficients each representing an intensity of coupling between the plurality of discrete variables, and the update process allows values of a plurality of first discrete variables included in the set of discrete variables to be updated in one iteration” Under Step 2A Prong 1, these limitations correspond to a mental evaluation. These limitations describe mental evaluations about data such as detection of properties and performance of the update process without further details of the functional operation of any claimed computer or technological components.
Furthermore, under step 2A Prong 2 and 2B, the claim does not recite additional elements to consider other than those considered in the independent claim. Accordingly, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 4
The claim is directed the same category identified in the parent claim. The dependent claim recites the following additional limitations which recite abstract ideas:
“wherein the update process includes determining, in parallel, whether to accept a flip of a value of each of the plurality of discrete variables, and flipping, upon determining to accept flips of values of a plurality of first discrete variables among the plurality of discrete variables, a value of a first discrete variable with an identification number that is smallest next to an identification number of a discrete variable whose value has been last flipped in the update process performed so far among the plurality of first discrete variables.”
Under Step 2A Prong 1, these limitations correspond to a mental evaluation. These limitations describe mental evaluations about data such as detection of properties and performance of the update process without further details of the functional operation of any claimed computer or technological components.
Furthermore, under step 2A Prong 2 and 2B, the claim does not recite additional elements to consider other than those considered in the independent claim. Accordingly, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
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 1-6 are 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.
The term “in a same area of a search space” in claim 1, 5 and 6 is a relative term which renders the claim indefinite. The term “same area” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination, the limitations is understood to describe that the replicase search for a solution in a search space.
Further, dependent claims 2-4 are rejected by virtue of their dependency.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
Claim(s) 1-6 are rejected under 35 U.S.C. § 103 as being unpatentable over Bagherbeik et al. “A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems” further in view of Weigel “Understanding population annealing Monte Carlo simulations”
Regarding Claim 1
Bagherbeik teaches, A non-transitory computer-readable storage medium storing a computer program that causes a computer to perform a process comprising: (abstract pg 317 “We implement this network in combination with a Parallel Tempering algorithm with varying degrees of parallelism ranging from a single-thread variant to a multi-threaded system using a 64- core CPU with SIMD instructions”) values of a plurality of discrete variables included in an evaluation function of a Boltzmann machine (pg 318 Section 2 “BMs, as shown in Fig. 1, are made up of N neurons, {x1, x2,...,xN } with binary states represented by vector S = [s1 s2 ... sN ] ∈ {0, 1}N…” equation 1
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and equations 2-3” the Boltzmann machine includes a plurality of state variables, weights and biases or discrete variables includes in the evaluation function shown in 1. Examiner notes, Equation 2-3 is also an evaluation function which is a function of Temperature and several other discrete variables. ) to which a combinatorial optimization problem is transformed, (Introduction pg 317 “BMs can be used to perform combinatorial optimization on complex problems…In this paper, we present an algorithm for a Permutational Boltzmann Machine (PBM), structured to solve complex, integer based, permutation optimization problems” the Boltzmann machine is structured or transformed to solve a combinatorial optimization problem.) and values of a plurality of local fields (pg 318 section 2 “The cumulative inputs to the neurons, also referred to as their local fields, hi, form H ∈ RN×1 and are calculated using (1).” Boltzmann machines are characterized by their cumulative inputs for each plurality of neurons i) storing, in a first memory, [values of a plurality of discrete variables and values of a plurality of local fields] … with respect to each of a plurality of replicas for the Boltzmann machine… storing, in a second memory provided for each of the plurality of replicas, the values of the plurality of discrete variables and the values of the plurality of local fields with respect to the each of the plurality of replicas; ( pg 324 Section 4.3 “Given the structure of a PBM combined with PT, one of the most intuitive ways to extract parallel speedups is to create a thread for each replica such that they all run on a unique core with their own dedicated L1 and L2 caches.” Each replica which contains the local field and discrete variables for a Boltzmann machine has its one first and second memory or cache.) the plurality of local fields each representing a change occurring in a value of the evaluation function when a value of one of the plurality of discrete variables is changed; (Section 2.1 pg 318 The cumulative inputs to the neurons, also referred to as their local fields, hi, form H ∈ RN×1 and are calculated using (1). …
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” and Figure 1. The local field values are a function of discrete variables which change according to an update routine shown in the figure
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) causing a plurality of replica processing circuits to repeatedly perform for each of the plurality of replicas, an update process of updating a value of one of the plurality of discrete variables, the value of the evaluation function, and the values of the plurality of local fields… each of the plurality of replicas being processed by one of the plurality of replica processing circuits… based on a set value of a temperature parameter and the values of the plurality of local fields stored in the second memory; (Section 4.3 “For our implementation, we targeted a 64-core AMD 3990X CPU” i.e plurality of replica processing circuits section 4.2 pg 324 “The algorithm starts by initializing the temperature ladder and assigning random permutations to each replica and populating their HP matrices and energy values. The system then enters an optimization loop where it runs Y trials for each replica in sequence using the RUN R() function, updating their states every time a trial is accepted by calling Swap(). After all replicas have finished their Y trials, temperature exchanges are performed” as previously noted updating states, i.e discrete variables, involves re-evaluating the evaluation function and consequently the values of the local fields based on the temperature parameters and stored local fields.) and each time a number of iterations of the update process reaches a predetermined value, performing a resampling process of a population annealing method, … based on the values of the plurality of discrete variables of the plurality of replicas and values of the evaluation function of the plurality of replicas,( section 4.2 pg 324 “The system then enters an optimization loop where it runs Y trials for each replica in sequence using the RUN R() function, updating their states every time a trial is accepted by calling Swap().” Pg 322 Section 3.1 “A swap proposal involves picking two unique rows, r and r’ , from the neuron matrix and swapping the states of their ON neurons along columns c and c’… A trial can then be performed by substituting the ΔE value from (14) into (5) and comparing the generated move probability against a value generated by rand()” Running the loop for Y trails amounts to updating until the number of iterations reaches Y, a predetermined value. The Swap proposal process is a resampling process for the population of Replicas as it resamples the states of the neurons in the Boltzmann machine. The Value ΔE for each replica is a function of the discrete variables and the evaluations functions. The equation 13-14 provide details of the evaluation function. The process of swapping involves eliminating a prior replica and replacing it with the updated replica thus the replica count remains unchanged.) and reading, … the first replicas being created the resampling process, and the values of the plurality of local fields of the first replica from the first memory or the second memory provided for the first replica,(Section 3.2 pg 322 “A swap proposal involves picking two unique rows, r and r’ , from the neuron matrix and swapping the states of their ON neurons along columns c and c’ . If accepted, this move results in 4 simultaneous bit-flips within the binary neuron matrix….When a swap proposal is accepted, the system state must be updated. Swapping the two values in φ and adjusting the system energy is simple… the structure of the weight matrix and the PBM itself allow these calculations to be performed efficiently while storing the majority of required data within L2 or L3 caches… we can generate the required weights … using (17)” A swap proposal is a created duplicate of a plurality of replicas. As noted previously, the L2 and L3 cache, or first and second memories, stores the replica discrete variables used or read in order to compute the update described in equation 17) and storing the read values of the plurality of discrete variables and the read values of the plurality of local fields in the second memory provided for the…first replica. (section 3.2 pg 322 “Storing a transposed copy of the matrices, while doubling the required memory, provides significant speedups due to a larger number of cache hits when fetching a small number of rows.” The transposed copies are stored in the first and second memories for each replica. In order to update the field matrix as described in the art.)
Bagherbeik does not explicitly teach, [resampling processing] involves duplicating a first replica of the plurality of replicas and eliminating a second replica of the plurality of replicas different from the first replica so that two first replicas are generated to search for a solution in a same area of a search space… and reading, in response to a duplication of the first replicas being created the resampling…[storing]... the duplication of the first replica
Weigel, however when addressing population annealing resampling process via Monte Carlo simulation teaches, [resampling processing] involves duplicating a first replica of the plurality of replicas and eliminating a second replica of the plurality of replicas different from the first replica so that two first replicas are generated to search for a solution in a same area of a search space ( see pg 2-3 Algorithm II “Population annealing (PA) can hence be summarized as follows: …(1) Set up an equilibrium ensemble of R0 = R independent copies (replicas) of the system …(2) Change the inverse temperature…resulting in a new population of size Ri… The resampling process in step 2 can be implemented in different ways…the process of resampling a total of Ri−1 replicas are chosen according to the probabilities…a useful and particularly simple alternative is to draw a random number r uniformly in [0,1) and take the number of copies of replica j in the new population to be
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…The new population size is …
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…This method requires only a single call to the random number generator for each replica in the current population” the replica update method is performed according to drawing the number of copies of duplicates for each replica and replacing a prior replica with new replicas of a new population size, the plurality of first replicas generated are for the area of the search space for a given solution. Examiner notes this very same replica duplication equation is described in instant specification paragraph 0088.)
Weigel teaches, and reading, in response to a duplication of the first replicas being created the resampling…[storing]... the duplication of the first replica ( pg 19 “The computational overhead incurred by the resampling step results from the calculation of the resampling weights … drawing the numbers rj_i of copies from the chosen resampling distribution, and the actual copy operations of configurations in memory or, for a distributed implementation, over the network… In shared memory systems, this overhead is often rather moderate” in the context of implementation of MCMC on compute systems resampling and distributing the updates and configurations amounts to reading and storing the duplicated replicas.)
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the replica selection method of Bagherbeik to comprise the replica duplication step described by Weigel as both reference describe approaches for replica generation and sampling for a Boltzmann machine. One would have been motivated to make such a combination because as noted by Weigel “While population annealing formally is a sequential Monte Carlo method… This element is conveniently chosen to be a MCMC method, bearing the additional advantage of further driving the population toward equilibrium …This method requires only a single call to the random number generator for each replica in the current population and no lookup tables, and additionally leads to very small fluctuations in the total population size” (Weigel pg 1-3)
Regarding Claim 2
Bagherbeik/Weigel teaches claim 1
Bagherbeik teaches, wherein the update process is performed by the plurality of replica processing circuits in parallel for the plurality of replicas. (Section 4.3 pg 324 and algorithm 1 “For our implementation, we targeted a 64-core AMD 3990X CPU. Given the structure of a PBM combined with PT, one of the most intuitive ways to extract parallel speedups is to create a thread for each replica such that they all run on a unique core with their own dedicated L1 and L2 caches” a thread for each replica is performing the replica processing in parallel circuits for each replica. Algorithm 1 describes the update process for parallel threads.)
Regarding Claim 3
Bagherbeik/Weigel teaches claim 1
Bagherbeik teaches, detecting a set of discrete variables that are not coupled to each other, based on weight coefficients included in the evaluation function …the weight coefficients each representing an intensity of coupling between the plurality of discrete variables, (Section 3.1 pg 320 “The PBM’s structure is an extension of Clustered Boltzmann Machines (CBM)… A CBM places neurons that do not have any connections between them into groups called clusters. Within a cluster, the states of the neurons have no effect on each other’s local fields… In a PBM, the neurons are arranged into an n × n matrix SP = (sr,c), where each row, ri, and each column, cj , forms a cluster, as shown in Fig. 2a” clustered neurons are those which are not coupled or do not have connections between them. Section 2.1 pg 318 “Each neuron, xi, is connected to other neurons, xj , via symmetric, real-valued weights, wi,j ∈ R where wi,j = wj,i and wi,i = 0, forming a 2D matrix” the value or the intensity of the weight coefficient represents the intensity the connections. Defining or detecting a cluster as in the art denotes those neurons that are within a given cluster, i.e with a weight intensity between them of zero, are not coupled.) and the update process allows values of a plurality of first discrete variables included in the set of discrete variables to be updated in one iteration. ( Section 3.1 pg 320-321 “Within a cluster, the states of the neurons have no effect on each other’s local fields; simultaneously flipping the states of multiple neurons in the same cluster has the same effect as flipping them in sequence… we propose trials via moves called swaps as shown in Fig. 2b. A swap proposal involves picking two unique rows, r and r’ , from the neuron matrix and swapping the states of their ON neurons along columns c and c’” swapping states of neurons in a trial is an update process of a plurality of values in a one iteration.)
Regarding Claim 4
Bagherbeik/Weigel teaches claim 1
Bagherbeik teaches, wherein the update process includes determining, in parallel, (pg 325 algorithm 1
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as shown in the algorithm PTExchange is a function which is performed in parallel.) whether to accept a flip of a value of each of the plurality of discrete variables, and flipping, upon determining to accept flips of values of a plurality of first discrete variables among the plurality of discrete variables, a value of a first discrete variable (pg 323 Section 4.1 and 325 algorithm 1 “Replicas are generally arranged in order of increasing T from Tmin to Tmax in a temperature ladder. A replica, Rk, operating at temperature Tk, can stochastically exchange temperature with the replica immediately above it on the ladder, Rk+1, with an Exchange Acceptance Probability (EAP)” flipping or exchanging a temperature of the replica amounts to flipping values of a plurality of discrete temperature variables. This is accepted according to a probability.) [a value of a first discrete variable] with an identification number that is smallest next to an identification number of a discrete variable whose value has been last flipped in the update process performed so far among the plurality of first discrete variables. ( pg 323 figure 3 “A replica, Rk, operating at temperature Tk, can stochastically exchange temperature with the replica immediately above it on the ladder, Rk+1, with an Exchange Acceptance Probability (EAP)” …As implied by (3) and (7), higher T replicas can move around a larger portion of the landscape whereas the moves in lower T replicas are contained to a smaller subspace of the landscape. The ability of replicas to move up or down the ladder, as shown in Fig. 3b” Replicas are simultaneously flipped and updated as shown in the Figure 3. The temperature is exchanged with the next smallest ID value, i.e the next highest index replica in the ladder. Which has been flipped through a prior update process.)
Regarding Claim 5
Bagherbeik teaches the shared limitations that are already addressed in the rejection of claim 1.
Further Bagherbeik teaches, A data processing apparatus comprising: and a processor that performs a process (abstract pg 317 “We implement this network in combination with a Parallel Tempering algorithm with varying degrees of parallelism ranging from a single-thread variant to a multi-threaded system using a 64- core CPU with SIMD instructions”) a first memory that holds values of discrete variables…a second memory that is provided for each of the plurality of replicas and holds the values…( pg 324 Section 4.3 “Given the structure of a PBM combined with PT, one of the most intuitive ways to extract parallel speedups is to create a thread for each replica such that they all run on a unique core with their own dedicated L1 and L2 caches.” Each replica which contains the local field and discrete variables for a Boltzmann machine has its one first and second memory or cache.)
Regarding Claim 6
Bagherbeik teaches the shared limitations that are already addressed in the rejection of claim 1 and/or 5.
Further Bagherbeik teaches, A data processing method comprising: storing, by a computer… by the computer … performing, by the computer (abstract pg 317 “We implement this network in combination with a Parallel Tempering algorithm with varying degrees of parallelism ranging from a single-thread variant to a multi-threaded system using a 64- core CPU with SIMD instructions”)
Conclusion
Prior art not relied upon:
Zhou et al. “Sequential Monte Carlo simulated annealing” describes accepting and rejecting new variables state in a Monte Carlo sampling scheme according to the next sequentially smallest index of the sampled variables.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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