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 .
DETAILED ACTION
The action is in response to claims dated 8/22/2023.
Claims pending in the case: 1-20
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 6 claims a mathematical function, however the variables represented by the alphabet place holders used in the equation has not been specified. As such, a person of reasonable skill in the art would not be apprised of the metes and bounds of the invention.
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.
Claim(s) 1-2, 13-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
determining,…, from the electronic density, parameters of a DFT functional that model one or more aspects of the physical system (This determination is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.),
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
receiving, at a density functional theory (DFT) functional module, an electronic density of a physical system (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
wherein the DFT functional module is trained using training data, the training data including classical data generated from a quantum processing module (This high level recitation of training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 12, are similar in scope as claim 1 and therefore rejected under the same rationale. The additional elements of a quantum processing module also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2, 4 pertain to type of data (This appears to be directed to the specification of data and a restriction to a particular type of data. This is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(h).)
Dependent claim 13-14 pertain to a neural network at a high level (This high level recitation of the neural network and qubits is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Dependent claim 15 pertain to using a quantum processor (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
These dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Hence these claims are rejected as being abstract.
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, 3-5, 7-9, 12-17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Umezawa (US 20220207393) in view of Love (Back to the Future: A roadmap for quantum simulation from vintage quantum Chemistry).
Regarding Claim 1, Umezawa teaches, A method comprising:
receiving, at a density functional theory (DFT) functional module, an electronic density of a physical system (Umezawa: [12, 23, 34, 65]: DFT module receiving material properties); and
determining, by the DFT functional module, from the electronic density, parameters of a DFT functional that model one or more aspects of the physical system (Umezawa: [65]: “the trained machine learning model is applied to predict material properties of a target system”),
wherein the DFT functional module is trained using training data (Umezawa: [65]: train model using training data), the training data including classical data generated from a quantum processing module (Umezawa: [4]: train model using training data generated by principles of quantum mechanics);
Umezawa does not recite, data generated from a quantum processing module;
Love teaches, data generated from a quantum processing module (Love Pg. 1 section I [1-2]: quantum computation to generate properties of devices);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Umezawa and Love because the combination would enable using quantum computing to generate the classical data. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve accuracy by using quantum computers over classical machines (see Love Pg. 1 section I [2]).
Regarding claim 3, Umezawa and Love teach the invention as claimed in claim 1 above and, wherein the generating of the classical data by the quantum processing module (Umezawa: [4]: train model using training data generated by principles of quantum mechanics);
constructing a Hamiltonian of the physical system; mapping fermionic operators of the Hamiltonian to qubit operators; constructing, from the qubit operators, a set of unitaries; applying the set of unitaries in accordance with a quantum algorithm onto one or more qubit registers; and generating the classical data (Love: Pg. 2-3, section II, Pg. 6-7 Section A: quantum computing in accordance with a quantum algorithm);
Regarding claim 4, Umezawa and Love teach the invention as claimed in claim 3 above and, wherein the classical data includes one or more of an electronic density function, a total system energy, a classical shadow, and a reduced density matrix (Umezawa: [12, 23, 34, 65]: DFT module receiving material properties).
Regarding claim 5, Umezawa and Love teach the invention as claimed in claim 1 above and, further including approximating the DFT functional using a Kohn-Sham method, wherein the DFT functional is parameterized using a hybrid functional construction (Umezawa: [35-38]: Kohn-Sham equation parameters include exchange-correlation energy).
Regarding claim 7, Umezawa and Love teach the invention as claimed in claim 3 above and, wherein the Hamiltonian is constructed based on one of a first quantization formalism and a second quantization formalism (Love: Pg. 6-7 section A: Hamiltonian construction).
Regarding claim 8, Umezawa and Love teach the invention as claimed in claim 7 above and, wherein the Hamiltonian is constructed based on the second quantization formalism, and the fermionic operator to qubit operator mapping is based on a Jordan-Wigner transformation (Love: Pg. 6-7 section A: Jordan Wigner transformation).
Regarding claim 9, Umezawa and Love teach the invention as claimed in claim 3 above and, wherein the quantum algorithm is any one of quantum phase estimation, variational quantum eigensolver (VQE), adiabatic quantum algorithm, Krylov subspace method, and imaginary-time evolution (Love: Pg. 3 section A: phase estimation algorithm).
Regarding claim 12, Umezawa teaches, A system comprising:
… classical data for training a density functional theory (DFT) functional module (Umezawa: [4]: train model using training data generated by principles of quantum mechanics); and
a classical processing module configured to: generate, by providing an electronic density of a physical system as input to the DFT functional module, parameters of a DFT functional that are used to model one or more aspects of the physical system (Umezawa: [65]: “the trained machine learning model is applied to predict material properties of a target system”; [12, 23, 34, 65]: DFT module receiving material properties);
However, Umezawa does not specifically teach, a quantum processing module configured to generate classical data;
Love teaches, a quantum processing module configured to generate classical data (Love Pg. 1 section I [1-2]: quantum computation to generate properties of devices);
The same motivation to combine stated above applies.
Regarding claim 13, Umezawa and Love teach the invention as claimed in claim 1 above and, wherein the DFT functional module is a trained deep neural network (Umezawa: [28]: neural network).
Regarding claim 14, Umezawa and Love teach the invention as claimed in claim 1 above and, wherein the parameters of the DFT functional are weights of the trained deep neural network (Umezawa: [65]: train neural network as DFT – implies the learnable parameters are of the DFT).
Regarding claim 15, Umezawa and Love teach the invention as claimed in claim 1 above and, wherein the quantum processing module is based on any one of superconducting qubits, photonic qubits, trapped-ion qubits, silicon-based qubits, and neutral-atom-based qubits (Love Pg. 1 section I [1]: trapped ions).
Regarding Claim(s) 16-17, 19-20 this/these claim(s) is/are similar in scope as claim(s) 3-4, 7, 9 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 2, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Umezawa (US 20220207393) and Love (Back to the Future: A roadmap for quantum simulation from vintage quantum Chemistry) in view of Kirkpatrick (US 20240071577).
Regarding claim 2, Umezawa and Love teach the invention as claimed in claim 1 above and,
wherein the training data includes a data pair comprising a training electronic density and a … exchange-correlation energy (Umezawa: [23]: ab initio simulation to get material properties; [25, 35-38]: Kohn-Sham equation parameters include exchange-correlation energy);
Kirkpatrick further teaches, training exchange-correlation energy (Kirkpatrick: [15]: training exchange-correlation energy);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Umezawa, Love and Kirkpatrick because the combination would enable using a model to obtain exchange-correlation energy data. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable using a model to process electron-orbital features of the atomic system to output a predicted exchange-correlation energy of the atomic system, which in the art is typically built or trained using specific approximations. The combination uses a practice for obtaining this parameter common in the art.
Regarding claim 11, Umezawa, Love and Kirkpatrick teach the invention as claimed in claim 2 above and,
wherein the training of the DFT functional module further includes: iteratively performing: sampling the training data, obtaining a predicted exchange-correlation energy based on the training electronic density, calculating a loss value between the predicted exchange-correlation energy and the training exchange-correlation energy, and updating a weight matrix of the DFT functional module based on the loss value; and storing the updated weight matrix of the DFT functional module (Kirkpatrick: [11, 13, 15]: training a neural network for predicting an exchange-correlation energy of an atomic system using loss).
Claim(s) 6, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Umezawa (US 20220207393) and Love (Back to the Future: A roadmap for quantum simulation from vintage quantum Chemistry) in view of Burke (Generalized Gradient Approximation Made Simple).
Regarding claim 6, Umezawa and Love teach the invention as claimed in claim 5 above and, further teach equation used in DFT may be Kohn-Sham equation (Umezawa: [35-36]);
Burke teaches, wherein the hybrid function construction is one of an internal method where the DFT functional, represented by Exc, is expressed as
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(Burke: Pg. 1 col 1 [1-2], Pg. 3 col 2: Approximations for Exc).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Umezawa, Love and Burke because the combination would enable using an approximation of exchange correlation energy based on Kohn-Sham theory.
Regarding Claim(s) 18 this/these claim(s) is/are similar in scope as claim(s) 6. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim Rejections using prior art
Regarding Claim(s), 10 no prior art was found to teach or make obvious all the limitations as claimed. Since the prior arts fail to disclose, suggest or teach all the claimed limitations, prior art rejection has not been presented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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