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 .
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.
Claims Status
Claims 1-20 are pending. Claim 1, 9 and 15 are independent. Claims 1-20 are examined below.
Priority
As detailed on the 02/23/2023 filing receipt, this application claims domestic priority to as early as 06/29/2017. This application is a CIP of 18/056,857 filed 11/18/2022 which is a CON of 16/624,833 filed 12/19/2019 PAT 11,508,463 which is a 371 of PCT/US18/40348 filed 06/29/2018 which claims benefit of 62/526,470 filed 06/29/2017.
Information Disclosure Statement
The Information Disclosure Statements filed 01/27/2023(2) and 02/14/2023 are in compliance with the provisions of 37 CFR 1.97 and has therefore been considered in full. The Information Disclosure Statement filed on 02/14/2023 containing 14 references is in compliance with the provisions of 37 CFR 1.97 and have been considered in part because reference #12 has not been considered and is lined-through, as it does not comply with the requirements set forth in 37 CFR 1.97. Reference #12 lacks appropriate dates and/or page numbers. A signed copy of the IDS document is included with this Office Action.
Drawings
The drawings filed 01/27/2023 are accepted.
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 an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "method for simulating a nanoscale device using a modeling system," with process steps such as "receiving…, simulating…"
Independent claim 9 is directed to a 101 process, here a "method for simulating a nanoscale device using a modeling system," with process steps such as "receiving…, simulating…"
Independent claim 15 is directed to a 101 process, here a "method for simulating a nanoscale device using a modeling system," with process steps such as "receiving…, simulating…"
[Step 1: claims 1-20: YES]
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claims 1, 9 and 15 recite: "…identifying at least one of a type of molecule and a type of solvent to be modeled for the system…determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor." Identifying and determining are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 2, 10 and 16 recite: "…determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian ." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 3, 11 and 17 recite: "wherein the Hamiltonian is determined using a Wannierization procedure." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 4, 12 and 18 recite: "wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region." This limitation is involved with comparing non-locality between the nested shell regions that are farther away from the device region and nested shell regions that are closer to the device region and solving Green’s functions, which requires evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claims 1, 9 and 15 recite: "generating a quantum model of the system…determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor." The claim recites mathematical concepts and/or formulas.
Claims 2, 10 and 16 recite: "determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian " The claim recites mathematical concepts and/or formulas.
Claims 3, 11 and 17 recite: "wherein the Hamiltonian is determined using a Wannierization procedure." The claim recites mathematical concepts and/or formulas.
Claims 4, 12 and 18 recite: "wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region." The claim recites mathematical concepts and/or formulas.
Claims 1, 9 and 15 recite determining a first property of the lead region… identifying at least one of a type of molecule and a type of solvent to be modeled for the system…; claims 2, 10 and 16 recite determining a Hamiltonian for the system; and determining the Green’s function for the device; claims 3, 11 and 17 recite Hamiltonian is determined using a Wannierization procedure and claims 4, 12 and 18 are involved with comparing values as discussed above and solving Green’s functions. These claim elements are involved with acts of evaluating, analyzing, observing and judging data as indicated above. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Therefore, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "Mental processes" grouping of abstract ideas.
As indicated above, claims 1, 9 and 15 recite generating a quantum model and using Non-Equilibrium Green’s Function methods; claims 2, 10 and 16 recite determining a Hamiltonian and determining the Green’s function, claims 3, 11 and 17 recite using a Wannierization procedure, and claims 4, 12 and 18 recite dephasing and matrix inversions required to solve Green’s functions, which are all mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
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). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claims 1, 9 and 15 recite "…simulating a nanoscale device using a modeling system... receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system…"
Claims 2-4, 10-12 and 16-18 do not recite any additional elements.
Claims 5-8, 13-14 and 19-20 are providing information on what the data represents.
The elements of claims 1, 9 and 15 as indicated above equate to insignificant extra solutional activities of data gathering. Data gathering serves as input to the recited judicial exception in the claims. Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
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 claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claims 1, 9 and 15 recite "…simulating a nanoscale device using a modeling system... receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system…"
Claims 2-4, 10-12 and 16-18 do not recite any additional elements.
Claims 5-8, 13-14 and 19-20 are providing information on what the data represents.
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitation of receiving model parameters equate to mere data gathering activities, which are insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Additionally, simulating a nanoscale device is a known method as disclosed by Hadi Arjmandi-Tash (“Single molecule detection with graphene and other two-dimensional materials: nanopores and beyond.” Chem. Soc. Rev. 2016; 45 (3): 476–493.; as cited on the attached 892 form). For example, Hadi Arjmandi-Tash discloses “Graphene and other two dimensional (2D) materials are currently integrated into nanoscaled devices…” (abstract). Also, Fig. 1 (page 478) of Hadi Arjmandi-Tash depicts nanopores for DNA detection and Figure 1(b) is Schematic representation of a typical nanopore device: an electrostatic field applied between two Ag/AgCl electrodes immersed in respectively the cis and trans reservoirs drives electrophoretically the ions and DNA molecules through the nanopore. Overall, Hadi Arjmandi-Tash teaches a nanoscale device using graphene for DNA detection. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 5-10, 13-16 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feliciano (“Addressing the Environment Electrostatic Effect on Ballistic Electron Transport in Large Systems: A QM/MM-NEGF Approach.” The journal of physical chemistry. B vol. 122,2 (2018): 485-492; as cited on the attached 892 form) in view of Settnes (EP3040889A1, published 06/07/2016; as cited on the attached 892 form).
Regarding independent claim 1, Feliciano teaches receiving model parameters for the system with “In essence, we perform a three-step procedure, namely, first a classical molecular dynamics simulation to obtain a set of configurations. They are subsequently used, by appropriately partitioning the system, in a single-point QM/MM calculation. With the resulting Kohn−Sham Hamiltonian, we calculate the transmission probability as a function of energy.” (page 487, col. 1, para. 6); and “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6).
Feliciano teaches as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system with “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6) and “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (Abstract).
Feliciano teaches generating a quantum model of the system using the processor with "As previously described, the configurational space is sampled by classical molecular dynamics (MD) simulations, employing the standard all-atom version of the AMBER99SB45 empirical force field, implemented in the GROMACS46 package, and the particle-mesh Ewald (PME)47 method is employed for the calculation of the electrostatic energy. We used the SPC water model48 and parameters for benzene to model graphene, with partial charges only in the hydrogen atoms terminating the nanopore edges and their neighboring carbon atoms.” (page 487, col. 2, para. 3) and “Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B.” (page 487, col. 2, para. 6)
Feliciano teaches the quantum model partitioning the system into a device region and a lead region with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region.” (page 486, col. 2, para. 2)
Feliciano teaches the device region encompassing the at least one molecule and a portion of the solvent of the system with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region. In the particular case of a nanopore used for DNA sequencing, the electrodes consist of pristine graphene (a unit cell of the semi infinite electrodes and the scattering region would, in principle, be a graphene sheet containing the nanopore, a strand of DNA that is sieved through the pore, water molecules, and the counterions).” (Page 486, col. 2, para. 2) and Fig. 1 with caption: “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.” (Fig. 1, page 486).
Feliciano teaches simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor with “Empirical molecular dynamics simulation (MD) is employed to sample over possible atomic configurations that represent the system, and then, density functional theory (DFT) is used to obtain the electronic structure and the system Hamiltonian. Transport calculations are then performed within the nonequilibrium Green’s function (NEGF) method.” (page 485, col. 2, para. 2); “In order to do this, the open system is partitioned into three: a central scattering region and two electrodes. Typically, the electrodes are semi-infinite periodic structures.” (page 486, col. 2, para. 1) and “In turn, the transmission can be obtained via Green’s functions for the open system…” (page 486, col. 2, para. 1).
Feliciano teaches determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor with “In order to obtain the electronic transport properties, one can use a Green’s function approach to obtain the low-bias conductance…” (page 486, col. 2, para. 3).
Feliciano teaches a Green’s function for the device region being determined based on a Green’s function for the device-lead interface with “In turn, the transmission can be obtained via Green’s functions for the open system” (page 486, col. 2, para. 3) and equation (3) “the retarded Green's function for the scattering region…” (page 486 col. 2).
Feliciano teaches combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor with “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (abstract).
Feliciano teaches the device-lead interface defining where the device region meets the lead region with Figure 1 (page 486). Figure 1 depicts Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green
Feliciano does not teach the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; the Green's function for the device-lead interface being determine base on the Green's functions for the plurality of nested shell regions of the lead region in claim 1. However, these limitations are taught by Settnes.
Settnes teaches the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]; The device region itself, containing nanostructures and/or leads… (para. [0004]); “We consider the computational setup schematically shown in the left panel of Fig. 1, where a device region is embedded within an extended two dimensional system.” (para. [0006]); Left panel of Figure 1 (page 3); “We begin by considering the setup shown in Fig. 2a, where a device region embedded into an extended sheet is indicated by the dashed square. In this case both are assumed to be graphene-based, but the following arguments are general to any two dimensional material. We consider a division of the system into two parts: sites in the device (D) or sites in the extended sheet region. Furthermore, we subdivide the extended sheet into boundary sites (B) which have a non-zero Hamiltonian element coupling them to the device region, or 'sheet' sites which do not couple to the device region.” (para. [0007]) and Figure 2 (page 4).
Settnes teaches the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region with “Typically, the electronic structure of the system is described with a tight-binding type Hamiltonian and a popular approach is then to construct the entire system in a piece-wise manner using recursive Green's functions (RGFs).” (para. [0001]); “We focus first on the general partitioning process, and then demonstrate how it can be quickly modified to account for the edge self-energy terms. We begin by placing all these sites of interest into recursive cell 1, as shown by the red sites in Fig. 3. We emphasize that the cells in this process are not of a fixed size and may consist of arbitrary sites which are not necessarily connected. Cell 2 is determined by selecting all the remaining unpartitioned sites which couple directly to sites in cell 1 via a non-zero Hamiltonian matrix element. In the example in Fig. 3, this consists of nearest neighbor sites of those in cell 1, which are not themselves in cell 1. This process is repeated until all sites in the device region have been allocated, and is demonstrated schematically in the panels of Fig. 3 where red sites indicate the current cell, and dark gray or white sites indicate sites added to the previous cell, or to earlier cells, respectively.” (page [0023]) and Fig. 3 (page 7).
Settnes teaches the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system15,15 with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]).
It would have been prima facia obvious to combine the teachings of Feliciano and Settnes to arrive at the claimed invention. Settnes’s Green's function (GF) method is able to efficiently treat large and finite sized 'patches' embedded in an extended system (para. [0003]). A person of ordinary skill in the art would have been motivated to modify the method of Feliciano to include a recursive Green’s function algorithm for the plurality of nested shell regions as taught by Settnes to efficiently treat large and finite sized 'patches' embedded in an extended system. Furthermore, there would have been a reasonable expectation of success, since Feliciano and Settnes teach methods that pertain to the use of graphene to study the electronic transport of molecules.
Regarding independent claim 9, Feliciano teaches receiving model parameters for the system with “In essence, we perform a three-step procedure, namely, first a classical molecular dynamics simulation to obtain a set of configurations. They are subsequently used, by appropriately partitioning the system, in a single-point QM/MM calculation. With the resulting Kohn−Sham Hamiltonian, we calculate the transmission probability as a function of energy.” (page 487, col. 1, para. 6); and “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6).
Feliciano teaches as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solid bulk medium to be modeled for the system with “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6) and “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (Abstract).
Feliciano teaches generating a quantum model of the system using the processor with "As previously described, the configurational space is sampled by classical molecular dynamics (MD) simulations, employing the standard all-atom version of the AMBER99SB45 empirical force field, implemented in the GROMACS46 package, and the particle-mesh Ewald (PME)47 method is employed for the calculation of the electrostatic energy. We used the SPC water model48 and parameters for benzene to model graphene, with partial charges only in the hydrogen atoms terminating the nanopore edges and their neighboring carbon atoms.” (page 487, col. 2, para. 3) and “Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B.” (page 487, col. 2, para. 6)
Feliciano teaches the quantum model partitioning the system into a device region and a lead region with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region.” (page 486, col. 2, para. 2)
Feliciano teaches the device region encompassing the at least one molecule and a portion of the solid bulk medium of the system with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region. In the particular case of a nanopore used for DNA sequencing, the electrodes consist of pristine graphene (a unit cell of the semi infinite electrodes and the scattering region would, in principle, be a graphene sheet containing the nanopore, a strand of DNA that is sieved through the pore, water molecules, and the counterions).” (Page 486, col. 2, para. 2) and Fig. 1 with caption: “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.” (Fig. 1, page 486).
Feliciano teaches simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor with “Empirical molecular dynamics simulation (MD) is employed to sample over possible atomic configurations that represent the system, and then, density functional theory (DFT) is used to obtain the electronic structure and the system Hamiltonian. Transport calculations are then performed within the nonequilibrium Green’s function (NEGF) method.” (page 485, col. 2, para. 2); “In order to do this, the open system is partitioned into three: a central scattering region and two electrodes. Typically, the electrodes are semi-infinite periodic structures.” (page 486, col. 2, para. 1) and “In turn, the transmission can be obtained via Green’s functions for the open system…” (page 486, col. 2, para. 1).
Feliciano teaches determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor with “In order to obtain the electronic transport properties, one can use a Green’s function approach to obtain the low-bias conductance…” (page 486, col. 2, para. 3).
Feliciano teaches a Green’s function for the device region being determined based on a Green’s function for the device-lead interface with “In turn, the transmission can be obtained via Green’s functions for the open system” (page 486, col. 2, para. 3) and equation (3) “the retarded Green's function for the scattering region…” (page 486 col. 2).
Feliciano teaches combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor with “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (abstract).
Feliciano teaches the device-lead interface defining where the device region meets the lead region with Figure 1 (page 486). Figure 1 depicts Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green
Feliciano does not teach the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solid bulk medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; the Green's function for the device-lead interface being determine base on the Green's functions for the plurality of nested shell regions of the lead region in claim 9. However, these limitations are taught by Settnes.
Settnes teaches the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]; The device region itself, containing nanostructures and/or leads… (para. [0004]); “We consider the computational setup schematically shown in the left panel of Fig. 1, where a device region is embedded within an extended two dimensional system.” (para. [0006]); Left panel of Figure 1 (page 3); “We begin by considering the setup shown in Fig. 2a, where a device region embedded into an extended sheet is indicated by the dashed square. In this case both are assumed to be graphene-based, but the following arguments are general to any two dimensional material. We consider a division of the system into two parts: sites in the device (D) or sites in the extended sheet region. Furthermore, we subdivide the extended sheet into boundary sites (B) which have a non-zero Hamiltonian element coupling them to the device region, or 'sheet' sites which do not couple to the device region.” (para. [0007]) and Figure 2 (page 4).
Settnes teaches the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region with “Typically, the electronic structure of the system is described with a tight-binding type Hamiltonian and a popular approach is then to construct the entire system in a piece-wise manner using recursive Green's functions (RGFs).” (para. [0001]); “We focus first on the general partitioning process, and then demonstrate how it can be quickly modified to account for the edge self-energy terms. We begin by placing all these sites of interest into recursive cell 1, as shown by the red sites in Fig. 3. We emphasize that the cells in this process are not of a fixed size and may consist of arbitrary sites which are not necessarily connected. Cell 2 is determined by selecting all the remaining unpartitioned sites which couple directly to sites in cell 1 via a non-zero Hamiltonian matrix element. In the example in Fig. 3, this consists of nearest neighbor sites of those in cell 1, which are not themselves in cell 1. This process is repeated until all sites in the device region have been allocated, and is demonstrated schematically in the panels of Fig. 3 where red sites indicate the current cell, and dark gray or white sites indicate sites added to the previous cell, or to earlier cells, respectively.” (page [0023]) and Fig. 3 (page 7).
Settnes teaches the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system15,15 with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]).
It would have been prima facia obvious to combine the teachings of Feliciano and Settnes to arrive at the claimed invention. Settnes’s Green's function (GF) method is able to efficiently treat large and finite sized 'patches' embedded in an extended system (para. [0003]). A person of ordinary skill in the art would have been motivated to modify the method of Feliciano to include a recursive Green’s function algorithm for the plurality of nested shell regions as taught by Settnes to efficiently treat large and finite sized 'patches' embedded in an extended system. Furthermore, there would have been a reasonable expectation of success, since Feliciano and Settnes teach methods that pertain to the use of graphene to study the electronic transport of molecules.
Regarding independent claim 15, Feliciano teaches receiving model parameters for the system with “In essence, we perform a three-step procedure, namely, first a classical molecular dynamics simulation to obtain a set of configurations. They are subsequently used, by appropriately partitioning the system, in a single-point QM/MM calculation. With the resulting Kohn−Sham Hamiltonian, we calculate the transmission probability as a function of energy.” (page 487, col. 1, para. 6); and “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6).
Feliciano teaches as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of medium to be modeled for the system with “Subsequently, the electronic structure of the system is obtained for each frame from the MD simulations. Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B. In each partition, the MM region contains the remainder of the system (water, counterions, and remaining nucleotides).” (page 487, col. 2, para. 6) and “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (Abstract).
Feliciano teaches generating a quantum model of the system using the processor with "As previously described, the configurational space is sampled by classical molecular dynamics (MD) simulations, employing the standard all-atom version of the AMBER99SB45 empirical force field, implemented in the GROMACS46 package, and the particle-mesh Ewald (PME)47 method is employed for the calculation of the electrostatic energy. We used the SPC water model48 and parameters for benzene to model graphene, with partial charges only in the hydrogen atoms terminating the nanopore edges and their neighboring carbon atoms.” (page 487, col. 2, para. 3) and “Two different QM/MM partitions are considered in this work, regarding the QM region composition; the partitions are (A) graphene nanopore and one nucleotide, illustrated in Figure 2A, and (B) graphene nanopore, one nucleotide, and a layer of water molecules within 8 Å of the nucleotide, comprising 108 water molecules, illustrated in Figure 2B.” (page 487, col. 2, para. 6)
Feliciano teaches the quantum model partitioning the system into a device region and a lead region with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region.” (page 486, col. 2, para. 2)
Feliciano teaches the device region encompassing the at least one molecule and a portion of the medium of the system with “The prototypical device we are considering consists of two metallic terminals coupled via a so-called scattering region. In the particular case of a nanopore used for DNA sequencing, the electrodes consist of pristine graphene (a unit cell of the semi infinite electrodes and the scattering region would, in principle, be a graphene sheet containing the nanopore, a strand of DNA that is sieved through the pore, water molecules, and the counterions).” (Page 486, col. 2, para. 2) and Fig. 1 with caption: “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.” (Fig. 1, page 486).
Feliciano teaches simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor with “Empirical molecular dynamics simulation (MD) is employed to sample over possible atomic configurations that represent the system, and then, density functional theory (DFT) is used to obtain the electronic structure and the system Hamiltonian. Transport calculations are then performed within the nonequilibrium Green’s function (NEGF) method.” (page 485, col. 2, para. 2); “In order to do this, the open system is partitioned into three: a central scattering region and two electrodes. Typically, the electrodes are semi-infinite periodic structures.” (page 486, col. 2, para. 1) and “In turn, the transmission can be obtained via Green’s functions for the open system…” (page 486, col. 2, para. 1).
Feliciano teaches determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor with “In order to obtain the electronic transport properties, one can use a Green’s function approach to obtain the low-bias conductance…” (page 486, col. 2, para. 3).
Feliciano teaches a Green’s function for the device region being determined based on a Green’s function for the device-lead interface with “In turn, the transmission can be obtained via Green’s functions for the open system” (page 486, col. 2, para. 3) and equation (3) “the retarded Green's function for the scattering region…” (page 486 col. 2).
Feliciano teaches combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor with “In this work, we present a methodology that combines quantum mechanics/molecular mechanics methods (QM/MM) with the nonequilibrium Green’s function framework to simulate the electronic transport properties of nanoscopic devices in the presence of solvents.” (abstract).
Feliciano teaches the device-lead interface defining where the device region meets the lead region with Figure 1 (page 486). Figure 1 depicts Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green
Feliciano does not teach the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solid bulk medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; the Green's function for the device-lead interface being determine base on the Green's functions for the plurality of nested shell regions of the lead region in claim 15. However, these limitations are taught by Settnes.
Settnes teaches the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]; The device region itself, containing nanostructures and/or leads… (para. [0004]); “We consider the computational setup schematically shown in the left panel of Fig. 1, where a device region is embedded within an extended two dimensional system.” (para. [0006]); Left panel of Figure 1 (page 3); “We begin by considering the setup shown in Fig. 2a, where a device region embedded into an extended sheet is indicated by the dashed square. In this case both are assumed to be graphene-based, but the following arguments are general to any two dimensional material. We consider a division of the system into two parts: sites in the device (D) or sites in the extended sheet region. Furthermore, we subdivide the extended sheet into boundary sites (B) which have a non-zero Hamiltonian element coupling them to the device region, or 'sheet' sites which do not couple to the device region.” (para. [0007]) and Figure 2 (page 4).
Settnes teaches the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region with “Typically, the electronic structure of the system is described with a tight-binding type Hamiltonian and a popular approach is then to construct the entire system in a piece-wise manner using recursive Green's functions (RGFs).” (para. [0001]); “We focus first on the general partitioning process, and then demonstrate how it can be quickly modified to account for the edge self-energy terms. We begin by placing all these sites of interest into recursive cell 1, as shown by the red sites in Fig. 3. We emphasize that the cells in this process are not of a fixed size and may consist of arbitrary sites which are not necessarily connected. Cell 2 is determined by selecting all the remaining unpartitioned sites which couple directly to sites in cell 1 via a non-zero Hamiltonian matrix element. In the example in Fig. 3, this consists of nearest neighbor sites of those in cell 1, which are not themselves in cell 1. This process is repeated until all sites in the device region have been allocated, and is demonstrated schematically in the panels of Fig. 3 where red sites indicate the current cell, and dark gray or white sites indicate sites added to the previous cell, or to earlier cells, respectively.” (page [0023]) and Fig. 3 (page 7).
Settnes teaches the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region with “In this paper, we develop a Green's function (GF) method which is able to efficiently treat large and finite sized 'patches' embedded in an extended system, as shown in Fig. 1. The method combines an analytical formulation of the Green's functions describing a pristine system15,15 with an adaptive recursive Green's function method to described the patches. It allows for calculation of both local electronic and transport properties and for the inclusion of multiple leads and arbitrary geometries embedded within an extended sample.” (para. [0003]).
It would have been prima facia obvious to combine the teachings of Feliciano and Settnes to arrive at the claimed invention. Settnes’s Green's function (GF) method is able to efficiently treat large and finite sized 'patches' embedded in an extended system (para. [0003]). A person of ordinary skill in the art would have been motivated to modify the method of Feliciano to include a recursive Green’s function algorithm for the plurality of nested shell regions as taught by Settnes to efficiently treat large and finite sized 'patches' embedded in an extended system. Furthermore, there would have been a reasonable expectation of success, since Feliciano and Settnes teach methods that pertain to the study of electronic transport of molecules through a graphene sheet.
Regarding claims 2, 10 and 16, Feliciano teaches determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian with “Empirical molecular dynamics simulation (MD) is employed to sample over possible atomic configurations that represent the system, and then, density functional theory (DFT) is used to obtain the electronic structure and the system Hamiltonian. Transport calculations are then performed within the nonequilibrium Green’s function (NEGF) method”. (page 485, col. 2, para. 2).
Regarding claim 5, Feliciano teaches wherein the solvent comprises at least one of hydrophobic membranes, organic molecules, inorganic molecules, emulsions, solids, and alloys with “Typically, biological molecules are immersed in a solution containing a solvent--mostly water and salt, in different concentrations.” (page 485, col. 1, para. 2)
Regarding claim 7, Feliciano teaches wherein the solvent comprises as solid bulk medium that incorporates the at least one molecule with “The protocol was applied as a test case study of electron transport across a graphene sheet containing a nanopore through which a DNA molecule is translocated. This system has been heralded as a potential setup for electronic DNA sequencing. The general idea is that a graphene sheet containing a nanopore can be used as a sieve for DNA. Concomitantly, the conductance on graphene can be measured and the nucleobases can be differentiated.” (page 486, col. 1, para. 6) and Figure 1, (page 486) “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.” The recited “solid bulk” corresponds to the “graphene sheet” as taught by Feliciano.
Regarding claims 8 and 14, Feliciano teaches wherein the solid bulk medium includes at least one of a graphene disc and a carbon nanotube with “The protocol was applied as a test case study of electron transport across a graphene sheet containing a nanopore through which a DNA molecule is translocated. This system has been heralded as a potential setup for electronic DNA sequencing. The general idea is that a graphene sheet containing a nanopore can be used as a sieve for DNA. Concomitantly, the conductance on graphene can be measured and the nucleobases can be differentiated. (page 486, col. 1, para. 6); and Figure 1. (page 486) “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.”
Regarding claim 20, Feliciano teaches wherein the medium includes at least one of a graphene disc and a carbon nanotube with “The protocol was applied as a test case study of electron transport across a graphene sheet containing a nanopore through which a DNA molecule is translocated. This system has been heralded as a potential setup for electronic DNA sequencing. The general idea is that a graphene sheet containing a nanopore can be used as a sieve for DNA. Concomitantly, the conductance on graphene can be measured and the nucleobases can be differentiated. (page 486, col. 1, para. 6); and Figure 1. (page 486) “Atomistic illustration of the system in which the QM/MM NEGF protocol was applied, composed by graphene, DNA, water, and counterions. The electrode region, at the extremities of the graphene sheet, is highlighted in red. Sodium atoms are in blue, and chloride atoms are in green.”
Feliciano does not explicitly teach that the solvent comprises as crystal structure that incorporates the at least one molecule of claims 6, 13 and 19. However, this limitation is taught by Settnes.
Regarding claims 6, 13 and 19, Settnes teaches wherein the solvent comprises as crystal structure that incorporates the at least one molecule with “…we investigated the current flow around perforations of a graphene lattice…” (para. [0048]); Figure 2 (page 4) and Figure 3 (page 7). Figures 2 and 3 depicts the lattice crystal structure that incorporates the molecule.
It would have been prima facia obvious to combine the teachings of Feliciano and Settnes to arrive at the claimed invention because both Feliciano and Settnes teaches using graphene to study the transport properties of molecules. Graphene is known to have a crystal structure as taught by Settnes. Settnes discloses “…we investigated the current flow around perforations of a graphene lattice…” (para. [0048]).
Claim(s) 3, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feliciano (“Addressing the Environment Electrostatic Effect on Ballistic Electron Transport in Large Systems: A QM/MM-NEGF Approach.” The journal of physical chemistry. B vol. 122,2 (2018): 485-492; as cited on the attached 892 form) in view of Settnes (EP3040889A1, published 06/07/2016; as cited on the attached 892 form) as applied to claims 1-2, 5-10, 13-16 and 19-20 above and in further view of Bernholc ("Recent developments and applications of the real-space multigrid method." Journal of Physics: Condensed Matter 20.29 (2008): 294205.; as cited on the attached 892 form).
Feliciano and Settnes are applied to claims 1-2, 5-10, 13-16 and 19-20 as discussed above.
Feliciano does not teach wherein the Hamiltonian is determined using a Wannierization procedure of claims 3, 11 and 17. However, this limitation is taught by Bernholc.
Regarding claims 3, 11 and 17, Bernholc teaches wherein the Hamiltonian is determined using a Wannierization procedure with “These methods usually make a localization approximation, which involves either the use of a localized, Wannier-like basis or a neglect of off-diagonal elements of the density matrix ρ(r,r′) for |r − r′| greater than an appropriate cutoff radius. Real-space methods are inherently local, and therefore suitable for imposing localization constraints on the basis functions that span the subspace of the both occupied and unoccupied orbitals.” (page 2, col. 2, para. 4).
It would have been prima facia obvious to combine the teachings of Feliciano and Bernholc to arrive at the claimed invention. A person of ordinary skill in the art would have been motivated to modify the method of Feliciano to include using a Wannierization procedure to determine the Hamiltonian as taught by Bernholc to evaluate the total energy in O(N) operations (page 2, col. 2, para. 4). Furthermore, there would have been a reasonable expectation of success, since Feliciano and Bernholc teach methods that pertain to the study of electronic properties of a system.
Claim(s) 4, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feliciano (“Addressing the Environment Electrostatic Effect on Ballistic Electron Transport in Large Systems: A QM/MM-NEGF Approach.” The journal of physical chemistry. B vol. 122,2 (2018): 485-492; as cited on the attached 892 form) in view of Settnes (EP3040889A1, published 06/07/2016; as cited on the attached 892 form) as applied to claims 1-2, 5-10, 13-16 and 19-20 above and in further view of Gruebele ("Quantum dynamics and control of vibrational dephasing." Journal of Physics: Condensed Matter 16.30 (2004): R1057-R1088.; as cited on the attached 892 form).
Feliciano and Settnes are applied to claims 1-2, 5-10, 13-16 and 19-20 as discussed above.
Feliciano does not teach wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region of claims 4, 12 and 18. However, this limitation is taught by Gruebele.
Regarding claims 4, 12 and 18, Gruebele teaches wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region with “Although a number of beautiful results have been obtained for the strict spin boson Hamiltonian with a purely harmonic continuous bath, equation (31) is probably a more accurate model for real molecule–solvent interactions: solvation shells exist in real solvents (typically 4–10 molecules in the first shell), and have a limited number of degrees of freedom. Thus even for interactions with a solvent, a local density of states rather than a global density of states is important, and it is strong couplings among states comprising the local density of states that give rise to exponential dephasing. In that case, decoherence in a weakly coupled solvent will ultimately be limited by diffusion of degrees of freedom from one shell to another, a process that usually occurs on a timescale of picoseconds, thus potentially leaving a window for quantum control of the solute even in a condensed phase environment.” (page R1085, para. 2) and Figure 17. Figure 17 caption discloses “The toy model for interaction of solvent shells with a two-level solute. The greater the number of shells added, the closer the approach of the spectral density J(ω) to a continuous function. On the right the decoherence of the TLS according to equation (32) is computed for different intrabath coupling strengths (relative to the TLS splitting). Intrabath couplings, not a continuous spectral density or large system–bath couplings, are the most efficient mechanism for obtaining exponential decoherence as opposed to a power law.”
It would have been prima facia obvious to combine the teachings of Feliciano and Gruebele to arrive at the claimed invention. Gruebele teaches that dephasing is a known quantum effect that decreases with each further out concentric shell (FIG. 17 (page R1085)). A person of ordinary skill in the art would have been motivated to modify the method of Feliciano to include dephasing as taught by Gruebele to improve the accuracy of solvation shells. Furthermore, there would have been a reasonable expectation of success, since Feliciano and Gruebele teach methods that pertain to the study of a system’s properties.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-4, 7, 9, 11-12, 15 and 17-20 of Application No. 16624833, US Patent # 11508463 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims recite a method of simulating a nanoscale device using a modeling system. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
App. # 18160578, 01/27/2023
(Instant Application)
App. # 16624833, US Patent # 11508463, 06/24/2022 (Reference Application)
Claims 1, 9, 15
Claims 1, 9, 17
1. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule in a solvent, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the solvent of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
9. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule incorporated in a solid bulk medium, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solid bulk medium to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the solid bulk medium of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solid bulk medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
15. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule incorporated in a medium, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of medium to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the medium of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
1. (Currently amended) A method for simulating a nanoscale device nanoscale device including a liquid system having receiving model parameters for the liquid system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the liquid system; generating a quantum model of the liquid system using the processor partitioning the liquid system into being further partitioned into a plurality of nested shell regions, each nested shell region having a spherical shell shape and encompassing a respective region of the solvent surrounding the device region in the liquid system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green's Function methods under open boundary conditions for the lead region using the processor, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; determining the [[a]] first property of the device region using Non-Equilibrium Green's Function methods using the processor, a Green's function for the device region being determined based on a Green's function for the device-lead interface, the Green's function for the device-lead interface being determined based on the Green's functions for the plurality of nested shell regions of the lead region and
9. (Currently amended) A non-transitory computer readable medium storing a plurality of instructions which are configured to, when executed, cause at least one processor to execute a method for simulating a nanoscale device nanoscale device including a liquid system having receiving model parameters for the liquid system as input to the at least one processor, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the liquid system; generating a quantum model of the liquid system using [[a]] the at least one processor ef having a shape and encompassing the at least one molecule and a portion of the solvent of the liquid system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region having a same shell shape and encompassing a respective region of the solvent surrounding the device region in the liquid system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the at least one processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green's Function methods under open boundary conditions for the lead region using the at least one processor, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; determining the [[a]] first property of the device region using Non-Equilibrium Green's Function methods using the at least one processor, a Green's function for the device region being determined based on a Green's function for the device-lead interface, the Green's function for the device-lead interface being determined based on the Green's functions for the plurality of nested shell regions of the lead region and at least one processor
17. (New) A method for simulating a nanoscale device using a modeling system, the nanoscale device including at least one molecule in a solvent, the method comprising: receiving model parameters for the molecule in the solvent as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the nanoscale device; generating a quantum model of the molecule in the solvent using the processor, the quantum model including a device region and a lead region, the device region having a shape and encompassing the at least one molecule and a portion of the solvent, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region having a same shell shape and encompassing a respective region of the solvent surrounding the device region, the plurality of nested shell regions being arranged in a nested manner starting from a device- lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green's Function methods under open boundary conditions for the lead region using the processor, a recursive Green's function algorithm being applied to the plurality of nested shell regions to determine Green's functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green's Function methods using the processor, a Green's function for the device region being determined based on a Green's function for the device-lead interface, the Green's function for the device-lead interface being determined based on the Green's functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the molecule in the solvent using the processor.
Claims 2, 10, 16
Claims 3, 11, 18
2. The method of claim 1, further comprising: determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian.
10. The method of claim 9, further comprising: determining a Hamiltonian for the system; and determining theGreen’s function for the device with reference to the Hamiltonian.
16. The method of claim 15, further comprising: determining a Hamiltonian for the system; and determining theGreen’s function for the device with reference to the Hamiltonian.
3. (Currently amended) The method of claim 1 [[2]], further comprising: determining a Hamiltonian for the liquid system; and determining the Green's function for the device with reference to the Hamiltonian.
11. (Currently amended) The non-transitory computer readable medium of claim 9 [[10]], wherein the method further comprises: determining a Hamiltonian for the liquid system; and determining the Green's function for the device with reference to the Hamiltonian.
18. (New) The method of claim 17, further comprising: determining a Hamiltonian for the molecule in the solvent; and determining the Green's function for the device with reference to the Hamiltonian.
Claims 3, 11, 17
Claims 4, 12, 19
3. The method of claim 2, wherein the Hamiltonian is determined using a Wannierization procedure.
11. The method of claim 10, wherein the Hamiltonian is determined using a Wannierization procedure.
17. The method of claim 16, wherein the Hamiltonian is determined using a Wannierization procedure.
4. (Original) The method of claim 3, wherein the Hamiltonian is determined using a Wannierization procedure.
12. (Original) The non-transitory computer readable medium of claim 11, wherein the Hamiltonian is determined using a Wannierization procedure.
19. (New) The method of claim 18, wherein the Hamiltonian is determined using a Wannierization procedure.
Claims 4, 12, 18
Claims 7, 15, 20
4. The method of claim 1, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
12. The method of claim 9, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
18. The method of claim 15, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
7. (Currently amended) The method of claim 1 [[6]], wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested Green's functions
15. (Currently amended) The non-transitory computer readable medium of claim 9 [[14]], wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested region, and wherein a number of matrix inversions required to solve Green's functions
20. (New) The method of claim 17, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green's functions for a given region depends in part on the amount of non-locality in the region.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4 and 7 of Application No. 18056857, US Patent # 12154663 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims recite a method of simulating a nanoscale device using a modeling system. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of the reference application that anticipate the claims of the instant application.
App. # 18160578, 01/27/2023
(Instant Application)
App. # 18056857, US Patent # 12154663, 09/28/2024 (Reference Application)
Claims 1, 9, 15
Claim 1
1. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule in a solvent, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the solvent of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solvent surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
9. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule incorporated in a solid bulk medium, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of solid bulk medium to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the solid bulk medium of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the solid bulk medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
15. A method for simulating a nanoscale device using a modeling system, the nanoscale device including a system having at least one molecule incorporated in a medium, the method comprising: receiving model parameters for the system as input to a processor of the modeling system, the model parameters identifying at least one of a type of molecule and a type of medium to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into a device region and a lead region, the device region encompassing the at least one molecule and a portion of the medium of the system, the lead region being further partitioned into a plurality of nested shell regions, each nested shell region encompassing a respective region of the medium surrounding the device region in the system, the plurality of nested shell regions being arranged in a nested manner starting from a device-lead interface and extending outward from the device region, the device-lead interface defining where the device region meets the lead region; and simulating the nanoscale device using the processor based on the quantum model, the simulating including: determining a first property of the lead region using Non-Equilibrium Green’s Function methods under open boundary conditions for the lead region using the processor¸ a recursive Green’s function algorithm being applied to the plurality of nested shell regions to determine Green’s functions for the plurality of nested shell regions of the lead region; determining the first property of the device region using Non-Equilibrium Green’s Function methods using the processor, a Green’s function for the device region being determined based on a Green’s function for the device-lead interface, the Green’s function for the device-lead interface being determined based on the Green’s functions for the plurality of nested shell regions of the lead region; and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the system using the processor.
1. (Previously presented) A method for simulating a nanoscale device using a processor, the nanoscale device including system having at least one molecule in a solvent, the method comprising: receiving model parameters for the system as input to the processor, the model parameters identifying at least one of a type of molecule and a type of solvent to be modeled for the system; generating a quantum model of the system using the processor, the quantum model partitioning the system into including a device region and a lead region, the device region being spherical in shape and encompassing the at least one molecule and a portion of the solvent of the
Claims 2, 10, 16
Claim 3
2. The method of claim 1, further comprising: determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian.
10. The method of claim 9, further comprising: determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian.
16. The method of claim 15, further comprising: determining a Hamiltonian for the system; and determining the Green’s function for the device with reference to the Hamiltonian.
3. (Previously presented) The method of claim 1, further comprising: determining a Hamiltonian for the system; and determining the Green's function for the device region with reference to the Hamiltonian.
Claims 3, 11, 17
Claim 4
3. The method of claim 2, wherein the Hamiltonian is determined using a Wannierization procedure.
11. The method of claim 10, wherein the Hamiltonian is determined using a Wannierization procedure.
17. The method of claim 16, wherein the Hamiltonian is determined using a Wannierization procedure.
4. (Original) The method of claim 3, wherein the Hamiltonian is determined using a Wannierization procedure.
Claims 4, 12, 18
Claim 7
4. The method of claim 1, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
12. The method of claim 9, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
18. The method of claim 15, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green’s functions for a given region depends in part on the amount of non-locality in the region.
7. (Previously presented) The method of claim 1, wherein dephasing increases with distance from the device region which results in the nested shell regions that are farther away from the device region having less non-locality than the nested shell regions that are closer to the device region, and wherein a number of matrix inversions required to solve Green's functions for a given region depends in part on the amount of non-locality in the region.
Claim 5
Claim 2
5. The method of claim 1, wherein the solvent comprises at least one of hydrophobic membranes, organic molecules, inorganic molecules, emulsions, solids, and alloys.
2. (Previously presented) The method of claim 1, wherein the solvent comprises at least one of hydrophobic membranes, organic molecules, inorganic molecules, emulsions, solids, and alloys.
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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