DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending and examined herein.
Claims 1-20 are rejected.
Priority
Claims 1-20 are granted the claim to the benefit of priority to U.S. Provisional application 63/481585 filed 25 January 2023. Thus, the effective filling date of claims 1-20 is 25 January 2023.
Information Disclosure Statement
The information disclosure statements (IDS) were received on 25 May 2023 and 21 May 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 07 March 2023 are accepted.
Claim Interpretation
Claim 15 recites “a computer system comprising a processor and associated computer memory storing a molecular dynamics program that when executed causes the processor to… input engine configured to receive a primary structure of the molecule”, and “an output engine configured to output the future conformation of the molecule as returned by the accelerator engine”. It is interpreted that the input engine configured to receive a primary structure and the output engine configured to output the future conformation do not invoke 112/f because these engines are modified by sufficient structure and acts for performing the claimed function. The computer system in which these engines are implemented provides sufficient structure for the input engine (e.g., a computer system is capable of receiving information), and output engine (e.g., a computer system is capable of outputting information).
112/f
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) are:
“a generator engine configured to generate the current conformation of the molecule based on the primary structure received” in claim 15.
“a molecular dynamics accelerator engine configured to: receive the current conformation in a trained machine-learning model… map the current conformation to a proposed conformation via the trained machine-learning model…, submit the proposed conformation to a Metropolis-Hastings test configured to accumulate the Boltzmann distribution… return the proposed conformation as the future conformation if the proposed conformation is accepted by the Metropolis-Hastings test” in claim 15.
Claim 15 provides that the engines are implemented on a computer system with a processor and memory. Further the molecular dynamics accelerator engine configured to perform the recited steps in the claims provides a sufficient disclosure of an algorithm for performing the steps of the engine such as a disclosure of the trained machine learning model and the Markov Chain Monte Carlo test (see instant disclosure [0027]-[0035]). However, there is not a sufficient disclosure of the algorithm for the generator engine configured to generate the current conformation of the molecule based on the primary structure received.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
112/a Written Description based on 112/f claim interpretation
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 15 and 16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 15 recites “a generator engine configured to generate the current conformation of the molecule based on the primary structure received” which invokes 112/f. The MPEP states at “When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a)”. The instant disclosure only provides that the generator engine is configured to generate the current conformation of the molecule based on the primary structure received (instant disclosure [0004] and [0080]). However, there is not an adequate written description of the algorithm/acts which performs this process. Dependent claim 16 is rejected by virtue of its dependency on a rejected claim without alleviating the issue.
112/b
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 14-16 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.
Claim 14 recites “the rate constant”, “the isomeric configuration”, “the predetermined increment” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear what “the rate constant”, “the isomeric configuration”, or “the predetermined increment” is referring to in the claims. For the sake of furthering examination, claim 14 will be interpreted as wherein receiving and mapping the current conformation and submitting and returning the proposed conformation are enacted repeatedly, the method further comprising: estimating a rate constant based on a probability that the future conformation approaches a conformation within a predetermined time.
112/b Indefiniteness based on 112/f claim interpretation
Claim limitation “a generator engine configured to generate the current conformation of the molecule based on the primary structure received” in claim 15 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
There is not a sufficient disclosure of the algorithm for the generator engine configured to generate the current conformation of the molecule based on the primary structure received. The MPEP states “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite” (MPEP 2181(II)(B)). The instant disclosure only provides that the generator engine is configured to generate the current conformation of the molecule based on the primary structure received (instant disclosure [0004] and [0080]). However, there is not an adequate written description of the algorithm/acts which performs this process. Dependent claim 16 is rejected by virtue of its dependency on a rejected claim without alleviating the indefiniteness. For the sake of furthering examination this limitation will be interpreted as a computer implemented step of generating a current conformation from data representing the molecule.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1)
Claims 1-14 and 17-20 fall under the statutory category of a process and claims 15 and 16 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Claims 1 and 15 recite mathematical concepts of “receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed”, “mapping the current conformation to a proposed conformation via the trained machine-learning model”, “submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate the Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed”, “returning the proposed conformation as the future conformation if the proposed conformation is accepted by the Metropolis-Hastings test”.
Claim 15 recites a mental process of “generate the current conformation of the molecular based on the primary structure received”.
Claim 17 recites mathematical concepts of “receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed”, “mapping the current conformation to a proposed conformation via the trained machine-learning model…” and “returning the proposed conformation as the future conformation”.
Claim 9 recites mathematical concepts of “subjecting a first conformation of the molecule to a succession of kinematic nuclear displacements to yield a second conformation of the molecule advanced in time by a predetermined interval, wherein each displacement is based on a temperature of the Boltzmann distribution according to a molecular dynamics algorithm”, “receiving the first and second training sets in the machine-learning model during training”, and “adjusting parameter values of the machine-learning model to minimize a residual for mapping elements of the first training set to corresponding elements of the second training set, to thereby generate the trained machine-learning model”.
Claim 9 recites a mental process of “coordinately incorporating the first conformation into a first training set and the second conformation into a second training set”.
Claim 13 recites mathematical concepts for reciting repeatedly enacting the steps of abstract steps of “receiving…”, “mapping…”, submitting…”, and returning the proposed conformation…” set out in claim 1. Claim 14 recites mathematical concepts for reciting repeatedly enacting the steps of abstract steps of “receiving…”, “mapping…”, submitting…”, and returning the proposed conformation…” set out in claim 1 and further recites a mathematical concept of “estimating the rate constant based on a probability that the future conformation approaches the isomeric configuration within the predetermined increment”. Claim 20 recites a mathematical concept of “submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate a Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed, wherein returning the proposed conformation comprises returning only if the proposed conformation is accepted by the Metropolis-Hastings test”.
The claims recite mental processes of analyzing/evaluating data and organizing data of incorporating the first conformation into a first training set and the second conformation into a second training set (which encompasses partitioning/organizing data into different datasets based on criteria).
The claims recite mathematical concepts as mathematical calculations as receiving the current conformation in a trained machine-learning model and mapping the current conformation to a proposed conformation via the trained machine-learning model (which encompass inputting numerical values of the atoms in cartesian coordinates into a model that maps these numerical values using a normalized flow function which is interpreted as being a mathematical function which intakes numerical values representing atoms in cartesian coordinates and produces a numerical outputs update numerical values representing atoms in cartesian coordinates see instant disclosure [0028] and dependent claims 2 and 16), submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate the Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed and returning the proposed conformation as the future conformation if the proposed conformation is accepted by the Metropolis-Hastings test (which encompasses calculating an acceptance ratio using numerical values of the configuration of atoms in Cartesian coordinates see instant disclosure [0033]), subjecting a first conformation of the molecule to a succession of kinematic nuclear displacements to yield a second conformation of the molecule advanced in time by a predetermined interval, wherein each displacement is based on a temperature of the Boltzmann distribution according to a molecular dynamics algorithm (which encompass calculations of integration of differential equations of motion of the molecule over time see dependent claim 10), adjusting parameter values of the machine-learning model to minimize a residual for mapping elements of the first training set to corresponding elements of the second training set, to thereby generate the trained machine-learning model (which encompasses mathematical calculations to adjust the model parameters such as adjusting neural network numerical weights see instant disclosure [0028] and using optimization algorithms see instant disclosure [0046]-[0048]) and estimating the rate constant based on a probability that the future conformation approaches the isomeric configuration within the predetermined increment (which encompasses a mathematical calculation of which calculates a rate constate based on the probability and predetermined time using molecular dynamics data such as velocity and atomic positions). It is noted for claims 13 and 14 repeating mathematical calculations does not change their nature as mathematical calculations.
Dependent claims 2-4, 6-12, 18, and 19 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1-20 recite abstract ideas.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1 and 17 of using a computer to perform judicial exceptions (for reciting “a computerized method”) and the additional element in claim 15 of a computer system comprising a processor and associated computer memory does not integrate the judicial exceptions into a practical application because this is applying the judicial exceptions to a generic computer without an improvement to computer functionality (see MPEP 2106.04(d)(1)). This additional element of a generic computer only interacts with the judicial exceptions in a manner by utilizing the computer as a tool to perform the judicial exceptions.
The additional element in claim 15 of receive a primary structure of the molecule and output the future conformation of the molecule (i.e., receiving data and outputting data) do not integrate the judicial exceptions into a practical application because this is insignificant extra solution activity of data gathering and data outputting (see MPEP 2106.05(g)). These additional elements additional elements constitute as data gathering and data outputting because they only interact with the judicial exceptions in a manner by providing data to be processed by the judicial exceptions and outputting the solution of the judicial exceptions. It is noted that the content of the data being received and outputted is part of the abstract idea and does not change the nature of receiving data/outputting data in a computer environment.
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-20 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claims 1 and 17 of using a computer to perform judicial exceptions (for reciting “a computerized method”) and the additional element in claim 15 of a computer system comprising a processor and associated computer memory are conventional see (MPEP 2106.05(b) and MPEP 2106.05(d)(II)).
The additional element in claim 15 of receive a primary structure of the molecule and output the future conformation of the molecule (i.e., receiving data and outputting data) are conventional see (MPEP 2106.05(b) and MPEP 2106.05(d)(II)). It is noted that the content of the data being received and outputted is part of the abstract idea and does not change the nature of receiving data/outputting data in a computer environment.
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
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.
Claims 1, 3, 4, 6-9, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (arXiv preprint arXiv:2203.02923 (2022); cited in IDS received 25 May 2023) in view of Albergo et al. (Physical Review D 100.3 (2019): 034515) in view of Wu et al. (arXiv preprint arXiv:2204.08672 (2022)).
Independent claim 1 is directed to a computerized method for forecasting a future conformation of a molecule based on a current conformation of the molecule, wherein at least the future conformation is a sample drawn from a Boltzmann distribution of conformations of the molecule, the method comprising: receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed
Xu et al. shows a geometric diffusion machine-learning model which is trained to recover conformations (proposed conformations) from white noise give specified molecular graphs (current confirmation) (Xu et al. page 4 section 4.1 Formulation). Xu et al. shows the goal is for learning a generative model which is easy to draw samples from, to approximate the Boltzmann function (Xu et al. page 3 section 3.1). Xu et al. shows a generative procedure which is a reverse dynamics of diffusion process starting from noisy particles where this reverse dynamic is formulated as a conditional Markov chain with learnable transitions such that given a graph, its 3D structure is generated by first drawing chaotic particles from a distribution, and then is iteratively refined through the reverse Markov kernels (Xu et al. page 4 section 4.1 Formulation subsection Reverse process). It is interpreted that receiving the current conformation is the initial 3D structure drawing from the chaotic particles from the initial distribution which is a standard Gaussian distribution (Xu et al. page 4 section 4.1 Formulation subsection Reverse process).
mapping the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain
Xu et al. shows that mapping the current conformation to a proposed conformation with the trained machine learning model is produced by mapping the current conformation with a Markov Kernal in the conditional Markov chain (Xu et al. page 4 section 4.1 Formulation subsection Reverse process and page 5 section 4.2 Equivariant Reverse Generative process subsection Equivariant Markov Kernels). Xu et al. further shows that with a learned reverse dynamic, the transition means can be calculated and thus given a graph, its geometry is generated by first sampling chaotic particles from the initial gaussian distribution then progressively sample from the distribution after each Markov Kernal is applied for each step in the iterative refinement (Xu et al. page 7 section 4.4 Sampling). It is interpreted that the sampling produces a proposed conformation from the current conformation.
Xu et al. does not show submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate the Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed; and returning the proposed conformation as the future conformation if the proposed conformation is accepted by the Metropolis-Hastings test.
Like Xu et al., Albergo et al. shows sampling from a distribution generated by a generative machine learning model. Albergo et al. shows a Markov chain Monte Carlo process which utilizes a Metropolis Hastings algorithm which generates a chain by steps through configurational space starting with a configuration where the steps are determined by the probabilities associated with each possible transition from one configuration to another configuration (Albergo et al. page 2 right col. page 4 right col.). Albergo et al. shows that given a generative model which allows sampling from a known probability distribution, a Markov chain for a desired probability distribution can be constructed with a Metropolis-Hastings method and for each step i of the chain, an update proposal is generated by sampling from the know probability distribution which is either accepted or rejected (Albergo et al. page 2 right col. – page 3 left col.). Albergo et al. shows a generative model may be optimized (trained) to produce samples from a distribution approximating a Boltzmann distribution (Albergo et al. page 1 abstract). Albergo et al. shows that the accept/reject statistics of the Metropolis-Hastings algorithm serve as a diagnostic for closeness of the approximate and desired distributions, if the distributions are equal, proposals are accepted with probability 1 and the Markov chain process is equivalent to a direct sampling of the desired distribution (Albergo et al. page 3 left col.).
Xu et al. in view of Albergo et al. does not show that the proposed conformation is a future conformation.
Like Xu et al. in view of Albergo et al., Wu et al. shows a diffusion model with a reverse diffusion process. Wu et al. shows constructing a diffusion process which is indexed by a continuous time variable in which the reverse process allows sampling the next time frame of the molecular conformation (Wu et al. page 4 left col.).
Independent claim 15 is directed to a computer system for forecasting a future conformation of a molecule based on a current conformation of the molecule, wherein at least the future conformation is a sample drawn from a Boltzmann distribution of conformations of the molecule, the computer system comprising: a processor and associated computer memory storing a molecular dynamics program that when executed causes the processor to implement: an input engine configured to receive a primary structure of the molecule; a generator engine configured to generate the current conformation of the molecule based on the primary structure received
Xu et al. shows a geometric diffusion machine-learning model which is trained to recover conformations (proposed conformations) from white noise give specified molecular graphs (current confirmation) (Xu et al. page 4 section 4.1 Formulation). Xu et al. shows the goal is for learning a generative model which is easy to draw samples from, to approximate the Boltzmann function (Xu et al. page 3 section 3.1). Xu et al. shows that given a graph of a molecule (which is interpreted as a primary structure of the molecule which holds information about the connections and atom types), its 3D structure is generated by first drawing chaotic particles from a distribution, and then is iteratively refined through the reverse Markov kernels (Xu et al. page 4 section 4.1 Formulation subsection Reverse process). Xu et al. shows implementing the process with a graphical processor unit (Xu et al. page 17 section C Experimental details).
a molecular dynamics accelerator engine configured to: receive the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed; map the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain
Xu et al. shows a generative procedure which is a reverse dynamics of diffusion process starting from noisy particles where this reverse dynamic is formulated as a conditional Markov chain with learnable transitions such that given a graph, its 3D structure is generated by first drawing chaotic particles from a distribution, and then is iteratively refined through the reverse Markov kernels (Xu et al. page 4 section 4.1 Formulation subsection Reverse process). It is interpreted that receiving the current conformation is the initial 3D structure drawing from the chaotic particles from the initial distribution which is a standard Gaussian distribution (Xu et al. page 4 section 4.1 Formulation subsection Reverse process).
Xu et al. does not show submit the proposed conformation to a Metropolis-Hastings test configured to accumulate the Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed; and return the proposed conformation as the future conformation if the proposed conformation is accepted by the Metropolis-Hastings test; and an output engine configured to output the future conformation of the molecule as returned by the accelerator engine.
Like Xu et al., Albergo et al. shows sampling from a distribution generated by a generative machine learning model. Albergo et al. shows a Markov chain Monte Carlo process which utilizes a Metropolis Hastings algorithm which generates a chain by steps through configurational space starting with a configuration where the steps are determined by the probabilities associated with each possible transition from one configuration to another configuration (Albergo et al. page 2 right col. page 4 right col.). Albergo et al. shows that given a generative model which allows sampling from a known probability distribution, a Markov chain for a desired probability distribution can be constructed with a Metropolis-Hastings method and for each step i of the chain, an update proposal is generated by sampling from the know probability distribution which is either accepted or rejected (Albergo et al. page 2 right col. – page 3 left col.). Albergo et al. shows a normalized flow generative model may be optimized (trained) to produce samples from a distribution approximating a Boltzmann distribution (Albergo et al. page 1 abstract). Albergo et al. shows that the accept/reject statistics of the Metropolis-Hastings algorithm serve as a diagnostic for closeness of the approximate and desired distributions, if the distributions are equal, proposals are accepted with probability 1 and the Markov chain process is equivalent to a direct sampling of the desired distribution (Albergo et al. page 3 left col.). Albergo et al. further shows that this algorithm satisfies a balance which guarantees to produce samples from the desired probability distribution (Boltzmann distribution) in the limit of an infinite chain (Albergo et al. page 7 right col.).
Xu et al. in view of Albergo et al. does not show that the proposed conformation is a future conformation.
Like Xu et al. in view of Albergo et al., Wu et al. shows a diffusion model with a reverse diffusion process. Wu et al. shows constructing a diffusion process which is indexed by a continuous time variable in which the reverse process allows sampling the next time frame of the molecular conformation (Wu et al. page 4 left col.).
Claim 3 is directed to wherein the trained machine-learning model includes one or more transformer blocks adapted to transform vectorized representations of atomic nuclei corresponding to each of the conformations received, and wherein each of the one or more transformer blocks includes a multi-head self-attention mechanism. Claim 4 is directed to wherein the multi-head self-attention mechanism computes a kernel-weighted self-attention where the influence of a first atomic nucleus on a transformation of a second atomic nucleus varies as a function of distance between the first and second atomic nuclei.
Xu et al. in view of Albergo et al. in view of Wu et al. shows the trained machine learning model includes a self-attention network which intakes a vectorized representations of atomic nuclei which were generated from the embedding of the conformation received to provide as output updated atomic coordinates, velocities, and features (it is interpreted that the self-attention network is multi-head due to the network providing three outputs) based on information of atomic nuclei within an interaction cutoff (Wu et al. page 5 left col. - page 5 right col.).
Claim 6 is directed to wherein the Markov chain is grown according to a Markov-chain Monte Carlo algorithm. Claim 7 is directed to wherein rejecting the balance of the conformations proposed comprises pruning the balance of the conformations from the Markov chain.
Xu et al. in view of Albergo et al. in view of Wu et al. shows growing the Markov chain according to a Markov-chain Monte Carlo algorithm (Albergo et al. page 2 right col.). Albergo et al. further shows that this algorithm satisfies a balance which guarantees to produce samples from the desired probability distribution (Boltzmann distribution) in the limit of an infinite chain (Albergo et al. page 7 right col.). Albergo et al. shows rejecting and accepting conformations which in a manner which produces a Markov chain process which is equivalent to sampling the desired distribution (Albergo et al. page 3 left col.).
Claim 8 is directed to wherein each of the conformations received comprises nuclear coordinates on a Cartesian coordinate system or on a coordinate system obtained from the Cartesian coordinate system by a linear transformation.
Xu et al. in view of Albergo et al. in view of Wu et al. shows the conformations received comprises nuclear coordinates on a three-dimensional atomic coordinate system (Wu et al. page 2 left col.).
Claim 9 is directed to further comprising training a machine-learning model to generate the trained machine-learning model, at least in part by: for each of a plurality of molecules: subjecting a first conformation of the molecule to a succession of kinematic nuclear displacements to yield a second conformation of the molecule advanced in time by a predetermined interval, wherein each displacement is based on a temperature of the Boltzmann distribution according to a molecular dynamics algorithm,
Xu et al. in view of Albergo et al. in view of Wu et al. shows utilizing trajectories of molecules at a temperature of 500 Kelvin and a resolution of 0.5 femtoseconds where the molecular dynamics algorithm generates a force field of a molecule which the trajectory of the molecule is determined using an integrator to advance the trajectory of the molecule through time which generates frames of the molecule through time (Wu et al. page 6 right col. and page 10 left col.).
and coordinately incorporating the first conformation into a first training set and the second conformation into a second training set; receiving the first and second training sets in the machine-learning model during training;
Xu et al. in view of Albergo et al. in view of Wu et al. shows using frame pairs for the training set which is interpreted a first conformation in a training set and the second conformation in a second training set which are paired and used for training the machine learning model (Wu et al. page 2 right col. and page 6 right col.).
and adjusting parameter values of the machine-learning model to minimize a residual for mapping elements of the first training set to corresponding elements of the second training set, to thereby generate the trained machine-learning model.
Xu et al. in view of Albergo et al. in view of Wu et al. shows adjusting parameter values of the reverse process of the diffusion model by training a time dependent score-based model with score matching minimizes an error function between samples from different distributions (Wu et al. page 2 right col. – page 3 left col.).
Claim 13 is directed to wherein receiving and mapping the current conformation and submitting and returning the proposed conformation are enacted repeatedly, such that each future conformation comprises a sample from the Boltzmann distribution.
Xu et al. in view of Albergo et al. in view of Wu et al. shows that the generative model may be optimized (trained) to produce samples from a distribution approximating a Boltzmann distribution (Albergo et al. page 1 abstract). Albergo et al. shows Markov chain can be intuitively considered a method to correct an approximate distribution generated by the generative model to the desired distribution (Boltzmann distribution) (Albergo et al. page 3 left col). Albergo et al. shows that the accept/reject statistics of the Metropolis-Hastings algorithm serve as a diagnostic for closeness of the approximate and desired distributions, if the distributions are equal, proposals are accepted with probability 1 and the Markov chain process is equivalent to a direct sampling of the desired distribution (Albergo et al. page 3 left col.).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the sampling of the distributions of generative diffusion process of Xu et al. with the importance sampling which utilizes the Metropolis-Hastings algorithm for generating a Markov chain where the Metropolis-Hastings algorithm accepts/rejects samples where accepted samples is equivalent to direct sampling of Albergo et al. because this would allow for a sampling from probability distributions generated from a diffusion model that provides a statistics which serve as a diagnostic for closeness of the approximate (probability distribution generated from the generative model) and desired distribution (the Boltzmann distribution) (Albergo et al. page 3 left col.). It would have been further obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the diffusion model for generating geometric conformations of a molecule of Xu et al. in view of Albergo et al. to implement the forward and reverse diffusion process on continuous time to predict future conformations of a molecule in which reversing the diffusion process allows the prediction of the next time step conformation of a molecule of Wu et al. because this would allow for a diffusion model trained on molecular dynamics simulation data to predict future conformations of a molecule by updating atomic positions based on a current conformation as the molecule progresses through time which performs well for predicting long-term trajectories of a molecule when trained on some short term trajectories (Wu et al. page 3 figure 1 and page 6 left col.). One would have a reasonable expectation of success because Xu et al. and Albergo et al. both show sampling distributions generated from generative machine learning models and Xu et al. in view of Albergo et al. and Wu et al. both show diffusion models from generating molecular conformations of a molecule.
Claims 2, 5, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. in view of Albergo et al. in view of Wu et al. as applied to claims 1 and 15 above, and further in view of Satorras et al. (Advances in Neural Information Processing Systems 34 (2021): 4181-4192).
Claims 2 and 16 are directed to wherein the trained machine-learning model maps the conformations received to the conformations proposed by enacting an invertible, normalizing-flow function on a sample drawn from a normal distribution around each conformation received, and wherein the normalizing-flow function is parameterized by learned parameter values of the trained machine-learning model. Claim 5 is directed to wherein the trained machine-learning model comprises a diffusion model.
Xu et al. in view of Albergo et al. in view of Wu et al. does not show wherein the trained machine-learning model maps the conformations received to the conformations proposed by enacting an invertible, normalizing-flow function on a sample drawn from a normal distribution around each conformation received, and wherein the normalizing-flow function is parameterized by learned parameter values of the trained machine-learning model
Like Xu et al. in view of Albergo et al. in view of Wu et al., Satorras et al. shows a generative model to map to molecules in three dimensions by sampling a normal distribution undergoes transformations. Satorras et al. shows using a learnable invertible transformation to map molecular samples from a normal distribution to a complex distribution using a normalizing flow function (which is interpreted as being the change of variables function) (Garcia et al. page 2).
It would have been obvious to one of ordinary skill in the art before the effective filling date to have substituted the functions in the reverse process of the diffusion model which maps samples from a normal distribution to a complex distribution (the distribution characterizing the data before corruption) of Xu et al. in view of Albergo et al. in view of Wu et al. with the use of the learnable invertible transformation maps that are normalizing flow functions of Satorras et al. because both functions are learnable transformations which map samples from a normal distribution to a complex distribution to generate three dimensional molecular conformations and would yield predictable results of a diffusion model which implements these normalizing flow functions for the reverse process that maps from a normal distribution to a complex distribution.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. in view of Albergo et al. in view of Wu et al. as applied to claims 1 and 9 above, and further in view of Wu and Mardt et al. (Advances in Neural Information Processing Systems 31 (2018)).
Claim 10 is directed to wherein the molecular-dynamics algorithm approximates integration of differential equations of motion of the molecule over time using a discretizing timestep, and wherein the predetermined interval is at least six orders of magnitude longer than the discretizing timestep. Claim 11 is directed to wherein the predetermined interval is at least one nanosecond.
Like Xu et al. in view of Albergo et al. in view of Wu et al., Wu and Mardt et al. shows a training a generative machine learn to model using training data from a molecular dynamics simulation of a molecular system. Wu and Mardt et al. shows utilizing training data of a 250-nanosecond trajectory (which is interpreted as a predetermined interval) with a storage interval of 1 picosecond (which is interpreted as the discrete timestep) (Wu and Mardt et al. page 6 section 3.2 Alanine dipeptide).
Claim 12 is directed to wherein the molecule comprises one or more of an oligopeptide, polypeptide, protein, biomolecule, or polymer.
Wu and Mardt et al. shows the molecule modeling a polypeptide as modeling an Alanine dipeptide system (Wu and Mardt et al. page 6 section 3.2 Alanine dipeptide).
It would have been obvious to one of ordinary skill in the art before the effective filling date to have substituted the molecular dynamics training data of diffusion model which models future conformations of molecules utilizing a diffusion process of Xu et al. in view of Albergo et al. in view of Wu et al. with molecular dynamics simulation training data of a 250-nanosecond trajectory with an interval of 1 picosecond of a polypeptide of Wu and Mardt et al. because both methods train generative machine learning models with molecular dynamic trajectories and would lead to predictable results of training a diffusion model to predict future conformations of a molecule using a 250-nanosecond trajectory molecular dynamics data.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. in view of Albergo et al. in view of Wu et al. as applied to claim 1 above, and further in view of Bolhuis et al. (Adv. Theory Simul., 4: 2000237 (2021)).
Claim 14 is directed to wherein receiving and mapping the current conformation and submitting and returning the proposed conformation are enacted repeatedly, the method further comprising: estimating a rate constant based on a probability that the future conformation approaches a conformation within a predetermined time.
Xu et al. in view of Albergo et al. in view of Wu et al. does not show wherein receiving and mapping the current conformation and submitting and returning the proposed conformation are enacted repeatedly, the method further comprising: estimating a rate constant based on a probability that the future conformation approaches a conformation within a predetermined time.
Like Xu et al. in view of Albergo et al. in view of Wu et al., Bolhuis et al. shows Markov chain Monte Carlo sampling methods with molecular dynamics data. Bolhuis et al. shows estimating a rate constant based on a probability that the future conformation approaches a conformation during a predetermined time using crossing probabilities (Bolhuis et al. page 7 right col. - page 8 left col.).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to combine reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have combined the generative diffusion model that predicts molecular conformations at future time steps of Xu et al. in view of Albergo et al. in view of Wu et al. with the process of estimating a rate constant based on a probability that the future conformation approaches a conformation during a predetermined time using crossing probabilities of Bolhuis et al. because this would allow for a comprehensive method with a generative machine learning model which produces future conformations of a molecule with trajectory data with the ability to estimate a rate constant of the trajectory path of the future conformation approaching a conformation during a predetermined time (i.e., a rate constant that describes a conformational change from one initial conformational state to a final conformational state of the molecule) (Bolhuis et al. page 7 right col. - page 8 left col.). One would have a reasonable expectation of success because Xu et al. in view of Albergo et al. in view of Wu et al. and Bolhuis et al. show analyzing molecular dynamics trajectory data of a molecule.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (arXiv preprint arXiv:2203.02923 (2022); cited in IDS received 25 May 2023) in view of Wu et al. (arXiv preprint arXiv:2204.08672 (2022)).
Claim 17 is directed to a computerized method for forecasting a future conformation of a molecular system based on a current conformation of the molecular system, the method comprising: receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed;
Xu et al. shows a geometric diffusion machine-learning model which is trained to recover conformations (proposed conformations) from white noise give specified molecular graphs (current confirmation) (Xu et al. page 4 section 4.1 Formulation). Xu et al. shows the goal is for learning a generative model which is easy to draw samples from, to approximate the Boltzmann function (Xu et al. page 3 section 3.1). Xu et al. shows a generative procedure which is a reverse dynamics of diffusion process starting from noisy particles where this reverse dynamic is formulated as a conditional Markov chain with learnable transitions such that given a graph, its 3D structure is generated by first drawing chaotic particles from a distribution, and then is iteratively refined through the reverse Markov kernels (Xu et al. page 4 section 4.1 Formulation subsection Reverse process). It is interpreted that receiving the current conformation is the initial 3D structure drawing from the chaotic particles from the initial distribution which is a standard Gaussian distribution (Xu et al. page 4 section 4.1 Formulation subsection Reverse process).
mapping the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain; and returning the proposed conformation as the future conformation.
Xu et al. shows that mapping the current conformation to a proposed conformation with the trained machine learning model is produced by mapping the current conformation with a Markov Kernal in the conditional Markov chain (Xu et al. page 4 section 4.1 Formulation subsection Reverse process and page 5 section 4.2 Equivariant Reverse Generative process subsection Equivariant Markov Kernels). Xu et al. further shows that with a learned reverse dynamic, the transition means can be calculated and thus given a graph, its geometry is generated by first sampling chaotic particles from the initial gaussian distribution then progressively sample from the distribution after each Markov Kernal is applied for each step in the iterative refinement (Xu et al. page 7 section 4.4 Sampling). It is interpreted that the sampling produces a proposed conformation from the current conformation.
Xu et al. does not show that the proposed conformation is a future conformation.
Like Xu et al., Wu et al. shows a diffusion model with a reverse diffusion process. Wu et al. shows constructing a diffusion process which is indexed by a continuous time variable in which the reverse process allows sampling the next time frame of the molecular conformation (Wu et al. page 4 left col.).
Claim 18 is directed to wherein the molecular system comprises a subset of atoms of a molecule.
Xu et al. shows the molecular system is denoted as a graph which includes a subset of atoms of a molecule (Xu et al. page 7 section 4.4 Sampling).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the diffusion model for generating geometric conformations of a molecule of Xu et al. to implement the forward and reverse diffusion process on continuous time to predict future conformations of a molecule in which reversing the diffusion process allows the prediction of the next time step conformation of a molecule of Wu et al. because this would allow for a diffusion model trained on molecular dynamics simulation data to predict future conformations of a molecule by updating atomic positions based on a current conformation as the molecule progresses through time which performs well for predicting long-term trajectories of a molecule when trained on some short term trajectories (Wu et al. page 3 figure 1 and page 6 left col.). One would have a reasonable expectation of success because both Xu et al. and Wu et al. shows diffusion models from generating molecular conformations of a molecule.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. in view of Wu et al. as applied to claim 17 above, and further in view of Wu and Mardt et al. (Advances in Neural Information Processing Systems 31 (2018)).
Claim 19 is directed to wherein the future conformation corresponds to a metastable state of the molecular system.
Like Xu et al. in view of Wu et al., Wu and Mardt et al. shows a training a generative machine learn to model using training data from a molecular dynamics simulation of a molecular system. Wu and Mardt et al. shows utilizing training data of a 250-nanosecond trajectory (which is interpreted as a predetermined interval) with a storage interval of 1 picosecond (which is interpreted as the discrete timestep) (Wu and Mardt et al. page 6 section 3.2 Alanine dipeptide). Wu and Mardt et al. shows the trained machine learning model maps time-lagged input configurations to metastable states whose dynamics are governed by the Markov model transition matrix (Wu and Mardt et al. page 3 section 2.1 “Kinetics” and page 3 Figure 1). Wu and Mardt et al. further shows that the training data includes information about metastable samples from which the machine learning model can learned and sample from (Wu and Mardt et al. page 8 figure 6).
It would have been obvious to one of ordinary skill in the art before the effective filling date to have substituted the molecular dynamics training data of diffusion model which models future conformations of molecules utilizing a diffusion process of Xu et al. in view of Wu et al. with molecular dynamics simulation training data of a 250-nanosecond trajectory with an interval of 1 picosecond of a polypeptide which includes information about metastable states which is sampled by the generative model of Wu and Mardt et al. because both methods train generative machine learning models with molecular dynamic trajectories and would lead to predictable results of training a diffusion model to predict future conformations of a molecule using a 250-nanosecond trajectory molecular dynamics data which includes information about metastable states which can be sampled by the trained machine learning model to produce as a future conformation of a molecular system.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. in view of Wu et al. as applied to claim 17 above, and further in view of Albergo et al. (Physical Review D 100.3 (2019): 034515).
Claim 20 is directed to comprising submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate a Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed, wherein returning the proposed conformation comprises returning only if the proposed conformation is accepted by the Metropolis-Hastings test.
Xu et al. in view of Wu et al. does not show submitting the proposed conformation to a Metropolis-Hastings test configured to accumulate a Boltzmann distribution without asymptotic bias, by accepting some of the conformations proposed and rejecting a balance of the conformations proposed, wherein returning the proposed conformation comprises returning only if the proposed conformation is accepted by the Metropolis-Hastings test.
Like Xu et al., Albergo et al. shows sampling from a distribution generated by a generative machine learning model. Albergo et al. shows that given a generative model which allows sampling from a known probability distribution, a Markov chain for a desired probability distribution can be constructed with a Metropolis-Hastings method and for each step i of the chain, an update proposal is generated by sampling from the know probability distribution which is either accepted or rejected (Albergo et al. page 2 right col. – page 3 left col.). Albergo et al. shows a generative model may be optimized (trained) to produce samples from a distribution approximating a Boltzmann distribution (Albergo et al. page 1 abstract). Albergo et al. shows that the accept/reject statistics of the Metropolis-Hastings algorithm serve as a diagnostic for closeness of the approximate and desired distributions, if the distributions are equal, proposals are accepted with probability 1 and the Markov chain process is equivalent to a direct sampling of the desired distribution (Albergo et al. page 3 left col.).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the sampling of the distributions of generative diffusion process of Xu et al. in view of Wu et al. with the importance sampling which utilizes the Metropolis-Hastings algorithm for generating a Markov chain where the Metropolis-Hastings algorithm accepts/rejects samples where accepted samples is equivalent to direct sampling of Albergo et al. because this would allow for a sampling from probability distributions generated from a diffusion model that provides a statistics which serve as a diagnostic for closeness of the approximate (probability distribution generated from the generative model) and desired distribution (the Boltzmann distribution) (Albergo et al. page 3 left col.). One would have a reasonable expectation of success because Xu et al. in view of Wu et al. show sampling from a distribution generated by a generative machine learning model while Albergo et al. shows a particular sampling method for a distribution generated by a generative machine learning model.
Conclusion
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