DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mailoa et al. (US 2020/0167439 A1 hereinafter Mailoa) in view of Neukart et al. (US 2020/0286595 A1 hereinafter Neukart).
As to claim 1, Mailoa discloses in Figs. 1-6D, a method for determining an electric polarization of a solid system (see the abstract, and also Figs. 3-5 showing NNFF algorithm 300 for predicting forces/energies in solid material systems like bulk PEO polymer, where energy derivatives suggest polarization in electric contexts such as batteries per para. [0058]), comprising: determining a wave function of the solid system by inputting electron coordinates of a periodic unit of the solid system into a neural network (atomic coordinate environments R1 through RN as inputs 302a through 302n to NNFF 300 as shown in Fig. 3, also Fig. 4 showing invariant NN 402 and covariant NN 404 processing fingerprint feature vectors from coordinates per para. [0022]: "The atomic Cartesian coordinates are converted into fingerprint feature vectors") and by minimizing an objective function (error minimization via training cycles in flowchart 500 as shown in Fig. 5 per para. [0035]: "Error of the combined NN may be reduced and/or minimized by running a smaller number of training cycles"), wherein the objective function is determined based on an enthalpy in the presence of an electric field (energy derivatives and force predictions in NNFF 300 as shown in Fig. 3, also performance plots in Figs. 6A-6D for solid systems per para. [0003]: "Derivatives of energy with respect to atomic positions and forces are predicted," suggesting enthalpy-like functionals in field contexts for batteries); and determining an electric polarizability of the solid system based on the wave function of the solid system (total energy E and force outputs in NNFF 300 as shown in Fig. 3, also quantum mechanics basis in prior art Fig. 1 suggesting properties like polarizability derived from energy computations per para. [0002]: "quantum mechanics energies" for materials). However, Mailoa does not teach determining a wave function of the solid system or determining an electric polarizability of the solid system based on the wave function of the solid system. Neukart teaches determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mailoa's neural network-based simulation for periodic solid systems to provide determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability) as taught by Neukart for yielding improved accuracy with polynomial-scaling measurements (Neukart para. [0046]) in computational modeling of battery materials common to both references (Mailoa para. [0058]; Neukart para. [0102]).
As to claim 2, Mailoa in view of Neukart discloses the method of claim 1, wherein the enthalpy is associated with at least one of the following: a Hamiltonian, the wave function, a volume of the periodic unit, the electric field, and an electric polarization density associated with the wave function (Mailoa, total energy E in NNFF 300 as shown in Fig. 3 per para. [0024]: symmetry functions for periodic environments including volume; Neukart, Hamiltonian in quantum simulator 102 as shown in Fig. 1 per para. [0050]: "Hamiltonian with position vectors ri for electrons").
As to claim 3, Mailoa in view of Neukart discloses the method of claim 2, wherein the enthalpy is expressed as a difference value between a product of the Hamiltonian and the wave function and a product of the volume, the electric field, and the electric polarization density (Mailoa, error as difference in flowchart 500 as shown in Fig. 5 per para. [0043]: "Error=(|→F_{i,NN}|−|→F_{i,QM}|)²"; Neukart, energy differences in plots of Fig. 3A per para. [0064]: "minimize errors or differences... between predicted FCI energies and the (real) FCI energies").
As to claim 4, Mailoa in view of Neukart discloses the method of claim 2, wherein the electric polarization density is also associated with a complex coordinate function, and the complex coordinate function is expressed as any one of the following: a first sub-function, or a weighted sum of the first sub-function and a second sub-function, and a weight of the second sub-function is associated with the wave function (Mailoa, fingerprint features G in invariant NN 402 as shown in Fig. 4 per para. [0028]: weighted sums in axis functions; Neukart, qubit mappings in quantum simulation process as shown in Fig. 2A per para. [0054]: "using a set of mappings as follows: σ^x_i → (1 - σ^z_{ij} σ^z_{ik})/2 S'(j) S'(k)").
As to claim 5, Mailoa in view of Neukart discloses the method of claim 4, wherein an independent variable of the first sub-function is electron coordinates, and an independent variable of the second sub-function is central symmetries of the electron coordinates (Mailoa, atomic environments R1-RN as inputs 302a-302n in Fig. 3 per para. [0022]: "Local environment of atoms surrounding an atom i... center atom i is displaced").
As to claim 6, Mailoa in view of Neukart discloses the method of claim 1, wherein determining the wave function of the solid system comprises: inputting the electron coordinates of the periodic unit of the solid system into the neural network, to obtain an output of the neural network, wherein the output comprises a first part and a second part, the first part denotes a real part of the wave function, and the second part denotes an imaginary part of the wave function (Mailoa, invariant 402 and covariant 404 parts in Fig. 4 per para. [0030]: real-valued force vectors from invariant/covariant; Neukart, complex components in Hamiltonian of quantum simulation process as shown in Fig. 2A per para. [0053]: "general qubit Hamiltonian... complex terms").
As to claim 7, Mailoa in view of Neukart discloses the method of claim 1, wherein determining the electric polarizability of the solid system comprises: determining a polarizability parameter of the solid system along a direction of the electric field based on the wave function of the solid system and the electric field applied to the solid system (Neukart, field-dependent energies in plots of Fig. 3A for LiH per para. [0060]: "input vectors include bond lengths and energies for dielectric-like properties").
As to claim 8, Mailoa in view of Neukart discloses the method of claim 7, further comprising: determining a capacitivity of the solid system based on the polarizability parameter (Neukart, material properties in ANN predictor 250 as shown in Fig. 2B per para. [0102]: "dielectric properties in materials like battery anodes").
As to claim 9, Mailoa in view of Neukart discloses the method of claim 1, wherein the periodic unit comprises a primitive cell or a supercell composed of a plurality of primitive cells (Mailoa, bulk structures in performance plots of Figs. 6A-6D per para. [0058]: "bulk version of polyethylene oxide (PEO) polymer" suggesting supercells).
As to claim 10, Mailoa discloses in Figs. 1-6D, an electronic device (computer system with processor/memory in prior art Fig. 1 per para. [0002]: "computer simulations"), comprising: at least one processing unit (implied processor in computer system of prior art Fig. 1 per para. [0002]); and at least one memory, coupled to the at least one processing unit and storing an instruction for execution by the at least one processing unit (implied memory/storage in computer system of prior art Fig. 1 per para. [0002]), wherein the instruction causes, when executed by the at least one processing unit, the electronic device to execute actions, comprising: determining a wave function of a solid system by inputting electron coordinates of a periodic unit of the solid system into a neural network (atomic coordinate environments R1 through RN as inputs 302a through 302n to NNFF 300 as shown in Fig. 3, also Fig. 4 showing invariant NN 402 and covariant NN 404 processing fingerprint feature vectors from coordinates per para. [0022]: "The atomic Cartesian coordinates are converted into fingerprint feature vectors") and by minimizing an objective function (error minimization via training cycles in flowchart 500 as shown in Fig. 5 per para. [0035]: "Error of the combined NN may be reduced and/or minimized by running a smaller number of training cycles"), wherein the objective function is determined based on an enthalpy in the presence of an electric field (energy derivatives and force predictions in NNFF 300 as shown in Fig. 3, also performance plots in Figs. 6A-6D for solid systems per para. [0003]: "Derivatives of energy with respect to atomic positions and forces are predicted," suggesting enthalpy-like functionals in field contexts for batteries); and determining an electric polarizability of the solid system based on the wave function of the solid system (total energy E and force outputs in NNFF 300 as shown in Fig. 3, also quantum mechanics basis in prior art Fig. 1 suggesting properties like polarizability derived from energy computations per para. [0002]: "quantum mechanics energies" for materials). However, Mailoa does not teach determining a wave function of the solid system or determining an electric polarizability of the solid system based on the wave function of the solid system. Neukart teaches determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mailoa's neural network-based simulation for periodic solid systems to provide determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability) as taught by Neukart for yielding improved accuracy with polynomial-scaling measurements (Neukart para. [0046]) in computational modeling of battery materials common to both references (Mailoa para. [0058]; Neukart para. [0102]).
As to claims 11-18, Mailoa in view of Neukart discloses the electronic device of claim 10 with the additional limitations (see detailed mappings for claims 2-9 above, as the limitations are substantively identical; device executes corresponding actions per Mailoa para. [0035]: "running a smaller number of training cycles on the combined network").
As to claim 19, Mailoa discloses in Figs. 1-6D, a non-transitory computer-readable storage medium, having a computer program stored thereon (non-transitory medium with instructions in computer system of prior art Fig. 1 per para. [0002]), wherein the program, when executed by a processor, implements a method comprising: determining a wave function of a solid system by inputting electron coordinates of a periodic unit of the solid system into a neural network (atomic coordinate environments R1 through RN as inputs 302a through 302n to NNFF 300 as shown in Fig. 3, also Fig. 4 showing invariant NN 402 and covariant NN 404 processing fingerprint feature vectors from coordinates per para. [0022]: "The atomic Cartesian coordinates are converted into fingerprint feature vectors") and by minimizing an objective function (error minimization via training cycles in flowchart 500 as shown in Fig. 5 per para. [0035]: "Error of the combined NN may be reduced and/or minimized by running a smaller number of training cycles"), wherein the objective function is determined based on an enthalpy in the presence of an electric field (energy derivatives and force predictions in NNFF 300 as shown in Fig. 3, also performance plots in Figs. 6A-6D for solid systems per para. [0003]: "Derivatives of energy with respect to atomic positions and forces are predicted," suggesting enthalpy-like functionals in field contexts for batteries); and determining an electric polarizability of the solid system based on the wave function of the solid system (total energy E and force outputs in NNFF 300 as shown in Fig. 3, also quantum mechanics basis in prior art Fig. 1 suggesting properties like polarizability derived from energy computations per para. [0002]: "quantum mechanics energies" for materials). However, Mailoa does not teach determining a wave function of the solid system or determining an electric polarizability of the solid system based on the wave function of the solid system. Neukart teaches determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mailoa's neural network-based simulation for periodic solid systems to provide determining a wave function of the solid system (quantum simulator 102 as shown in Fig. 1, also Hamiltonian mapping in the quantum simulation process as shown in Fig. 2A per para. [0047]-[0056]: "The Hamiltonian... may be converted to a qubit representation... mapped to a classical Ising formulation" for quantum states representing wave functions) and determining an electric polarizability of the solid system based on the wave function of the solid system (ANN predictor 250 as shown in Fig. 2B, also energy plots for LiH in Fig. 3A per para. [0060]: "predicted FCI energies... for varying bond lengths," where electronic structure simulations enable derivation of field-dependent properties like polarizability) as taught by Neukart for yielding improved accuracy with polynomial-scaling measurements (Neukart para. [0046]) in computational modeling of battery materials common to both references (Mailoa para. [0058]; Neukart para. [0102]).
Conclusion
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/TUNG X NGUYEN/ Primary Examiner, Art Unit 2858 2/6/2026