DETAILED ACTION
This is a first Office Action (“Action”) on the merits to the application filed June 30, 2023. Claims 1-20 were original filed, and by preliminary amendment filed and entered on April 7, 2026, claims 1-18 and 20 are pending, and claim 19 is canceled. All references in this Action to the specification as filed of the current application will be made to the published version U.S. Patent Application Publication No. 2024/0071577 (“PgPub”).
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 .
Effective Filing Date of the Claimed Invention
The effective filing date of a claimed invention is “the earliest of: (1) the actual filing date of the patent or the application for the patent containing the claimed invention; or (2) the filing date of the earliest application for which the patent or application is entitled, as to such invention, to a right of priority or the benefit of an earlier filing date under 35 U.S.C. 119, 120, 121, 365, or 386. See 35 U.S.C. 100(i)(1).” MPEP § 2152.01. However, any claim to an earlier patent application must still satisfy 35 U.S.C. 112(a) (pre-AIA , section 112, first paragraph). MPEP § 706.02.VI.
The current application claims domestic benefit to U.S. provisional application number 63/135,223 filed Jan. 8, 2021. After review of the U.S. provisional application, there is adequate section 112(a) support for claims 1-18 and 20, and thus, the pending claims have an effective filing date of Jan. 8, 2021. However, Applicant is advised that not all of the description in the current application appears to have support in the provisional application. For example, Fig. 1 and its corresponding description do not at all appear in the filings of the provisional application. As a result, claim amendments filed in subsequent responses may result in a change of the corresponding claim’s effective filing date depending on whether there is adequate support in the provisional application, which may lead to new rejections over prior art not available at this time.
Information Disclosure Statement
The information disclosure statements submitted on Nov. 21, 2024 and March 20, 2026 are in compliance with the provisions of 37 C.F.R. §§ 1.97 and 1.98 and have been considered.
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.
Claim 10 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Claim 10 depends from claim 1 and recites the limitation “the plurality of contribution terms”. There is insufficient antecedent basis for this limitation in the claim because neither claim 10 nor claim 1 previously recite “a plurality of contribution terms.”
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 1-11, 13-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
To determine whether claimed subject matter is patent eligible section 2106 of the MPEP requires specific evaluation of the limitations recited. In step 1, a determination is made as to whether a claim is directed to a statutory category (i.e., a process, machine, manufacture, and composition of matter). If so, then a determination is made as to whether the claim is directed to patent ineligible subject matter, such as an abstract idea, using a three part test: First, in step 2A, prong 1 the abstract idea is identified; Second, in step 2A, prong 2 the abstract idea is determined to be integrated into a practical application or not; and Third, in step 2B the additional claim limitations are evaluated individually and as a whole to determine if they amount to an inventive concept (i.e., determining whether the limitations are significantly more than the abstract idea itself).
While the claims fall within the statutory categories of patent eligible subject matter (i.e., step 1 is satisfied because the claims are directed to a process (claims 1-17) and an apparatus (claims 18 and 20), they are nonetheless patent ineligible for being directed to an abstract idea without reciting significantly more.
Initially, the following explanation is based not only on the guidance in the MPEP, but also the “2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence,” published on July 17, 2024 (89 FR 58128) (AI-SME Update). Moreover, the Office has provided examples of patent ineligible subject matter that are relevant to the claimed subject matter of this application. In particular, examples 47 and 481, are relevant to the discussion below of claims 1-11, 13-18, and 20 and should be reviewed for further guidance and support of the determination that claims 1-11, 13-18, and 20 are directed to patent ineligible subject matter.
Step 2A, Prong 1: Independent Claims 1, 13, and 18
Claims 1 and 18 are virtually identical in scope except for the statutory categories and some of the more minor aspects of the claim limitations. Claim 13 is of a different scope than claims 1 and 18. Even so, claims 1, 13, and 18 all recite limitations directed to an abstract idea, specifically the recitation of mathematical concepts and mental processes. See MPEP 2106.04(a)(2), subsections I and III.
Taking claim 1 as a representative claim of claims 1 and 18, the limitations comprising the abstract idea are:
[1.A] generating, for each of the plurality of grid points, a respective input feature vector for the electron-orbital features at the grid point; and
[1.B] … generate a predicted exchange-correlation energy of the atomic system.
Under a broadest reasonable interpretation, and consistent with the specification as filed, limitation [1.A] is understood as follows. The limitation is recited at a high level of generality, thus, the “generating” is not defined. The term “input feature vector” is defined by a mathematical equation and doing so over a “grid point” is also mathematical. See PgPub, ¶¶45, 48. Thus, generation of “a respective input feature vector … at a grid point” is mathematical and can be done in the human mind or on paper. See MPEP §§ 2106.04(a), subsections I and III.
Similarly, limitation [1.B] is understood as follows. The limitation is recited at a high level of generality, thus, the “generate” step is not defined. Additionally, an “exchange-correlation energy” is defined by a mathematical equation. See PgPub, ¶58. Thus, generating “a predicted exchange-correlation energy of the atomic system” is mathematical and can be done in the human mind or on paper. See MPEP §§ 2106.04(a), subsections I and III.
Since limitations [1.A] and [1.B] fall within the meaning of mental processes and mathematical concepts, they are taken together as a single abstract idea, to which the claims are directed. Thus, prong one of step 2A is satisfied and the analysis continues below.
Claim 13 recites the following limitations comprising the abstract idea:
[13.A] obtaining a plurality of training examples, each training example including electron-orbital features and corresponding ground-truth energy label of an atomic system, the plurality of training examples including a first subset of training examples that correspond to atomic systems that have electron-orbital features and energy levels satisfying one or more mathematical constraint conditions;
[13.B] for each training example … generate a predicted exchange-correlation energy for the training example;
[13.C] determining a gradient with respect to the parameters of a training loss, the training loss including a regression loss that measures, for each training example, an error between the predicted exchange-correlation energy for the training example and the ground-truth energy label in the training example.
Under a broadest reasonable interpretation consistent with the specification, limitation [13.A], along with being merely data gathering (see discussion below), is also mathematical in nature. The limitation obtains “a first subset of training examples” by “satisfying one or more mathematical constraint conditions.” This is essential filtering out the subset based on filter conditions, which is mathematical and could be performed in the human mind or on paper. See MPEP §§ 2106.04(a), subsections I and III.
Limitation [13.B] incorporates the features already addressed in limitations [1.A] and [1.B] above. The limitation is recited at a high level of generality, thus, the “generate” step is not defined. Additionally, an “exchange-correlation energy” is defined by a mathematical equation. See PgPub, ¶58. Thus, generating “a predicted exchange-correlation energy for the training example” is mathematical and can be done in the human mind or on paper. See MPEP §§ 2106.04(a), subsections I and III.
Lastly, limitation [13.C] is mathematical in nature. The “gradient,” “training loss,” and “regression loss” are defined by mathematical equations or concepts. PgPub, ¶¶17, 81-82, 86, 114. Additionally, “an error” is also a mathematical function comparing at least two values. As a result, “determining a gradient” is mathematical and can be done in the human mind or on paper. See MPEP §§ 2106.04(a), subsections I and III.
For the reasons set forth above, claims 1, 13, and 18 are directed to abstract ideas and satisfy step 2A, prong 1 of the analysis.
Step 2A, Prong 2: Independent Claims 1, 13, and 18
The descried invention states in PgPub, ¶22:
Density functional theory (DFT) revolutionized quantum chemistry as the only computational method to promise high accuracy and to scale to large chemical systems. Key to its success is the efficient description of the quantum-mechanical interaction between electrons by approximation of the exact exchange-correlation functional, E. In trying to satisfy the accuracy and broad applicability demanded in diverse scientific fields, a large range of such approximations has evolved. However, fundamental failings persist in modern general-purpose functionals, which impact broad areas of chemistry and are even evident on molecules as simple as H2+. Failures can often be traced back to the violation of mathematical properties of the exact functional, for example, the violation of exact conditions for systems exhibiting behaviors of fractional electrons.
As further described in PgPub, ¶¶23-25, with emphasis:
The described techniques use deep learning to produce functionals (e, g., the exchange-correlation functional, Exc) that achieve state-of-the-art performance on large general chemistry databases, and that also allow incorporation of exact constraints in a data-driven manner. Notably, the described techniques produce accurate functionals that solve the problem of simultaneously obeying the known constraints for systems with fractional electron charge and spin. The functionals produced by the described techniques can be used to accurately predict the energy of atomic systems in the DFT framework. The predicted energies of the atomic systems can be used to predict important chemical properties, such as the ionization energies, the vibrational properties, the enthalpies of formation, and the reaction barriers of the corresponding atomic systems.
The described techniques allow behavior learned from training on these fictional atomic systems to generalize to larger real molecules. The constraints are demonstrated to contribute to superior approximations of the exchange-correlation energies of a broad range of atomic systems, and thus surpassing state-of-the-art for predicting properties of atomic systems using DFT.
Based on the predicted exchange-correlation energy of the atomic system, e.g., a reactant for a chemical reaction, the system can obtain properties such as the ionization energy, the vibrational properties, the enthalpies of formation, and the reaction barriers of the reactant. Based on these properties, the system can make a decision for producing a chemical product (e.g., a drug molecule) using the reactant. For example, the system can generate an output that indicates whether the chemical reaction can take place under a specified condition to produce a chemical product, or an output that specifies the optimal reaction parameters for producing the chemical product. The system can transmit the output to a fabrication apparatus operative to implement the instruction to produce the chemical product.
None of claims 1, 13, or 18 link the claimed abstract idea to any of the practical applications discussed in the specification as filed. While there are additional limitations recited in claims 1, 13, and 18 (see discussion below), these do not recite any features or elements linking the abstract idea to the noted practical applications.
Claims 1 and 18 also require the step of, taking claim 1 as a representative example, “processing the respective input feature vectors for the plurality of grid points using a neural network” to generate the “predicted exchange-correlation energy.” However, this limitation is understood as nothing more than applying the abstract idea by way of a “neural network.” See MPEP §§ 2106.04(d), subsection I, and 2106.05(f), subsection I. There are no details claimed of the “neural network” itself or how the “input feature vectors” are processed using the “neural network.” As a result, this is nothing more than merely applying the abstract idea without more, where the claims do not specify any use or action taken with the processed “input feature vectors” or the resulting “predicted exchange-correlation energy” to show integration into a practical application.
While claim 13 additionally recites, “updating the current values of the parameters [of the neural network] using the gradient,” this is recited at such a high level of generality so as to be understood as merely apply the abstract idea without anything more. Moreover, as noted above, there is no claimed feature within this limitation linking the abstract idea to an integrated practical application.
There is also no improvement to the functioning of a computer or technological environment based on the abstract idea. See MPEP §§ 2106.04(d), subsection I, 2106.04(d)(1). Similar to what has been stated above, there is no claimed feature or limitation linking any type of improvement in the area of quantum chemistry computing, as noted in the specification. Claim 18 also requires the abstract idea to be carried out by “one or more computers; and one or more storage devices storing instructions that when executed by the one or more computers, cause the one or more computers to perform operations.” However, the “one or more computers” and “one or more storage devices” are claimed at a high level of generality and are merely a tool to apply the abstract idea, which does not result in a practical application of the abstract idea itself, especially since the abstract idea does not necessarily result in an improvement in functionality of the “computers” or “storage devices.” See MPEP § 2106.05(f).
Lastly, even taking the limitations together or in combination, there is nothing that would show an integrated practical application of the abstract idea. For example, there are no additional limitations that explain what is done with the “predicted exchange-correlation energy” to show any type of practical result. Since there are no other limitations to show integration into a practical application, prong 2 of step 2A is satisfied and the analysis continues below.
Step 2B: Independent Claims 1, 13, and 18
The abstract idea is embodied in the limitations recited in independent claims 1, 13, and 18 identified above. Claims 1 and 18, taking claim 1 as a representative claim, recite, “obtaining respective electron-orbital features of the atomic system at each of a plurality of grid points.” There are no further details claimed detailing how the “features” are obtained, thus, this limitation is nothing more than mere data gathering, which is insignificant extra-solution activity that is not significantly more to make the claims patent eligible. See MPEP § 2106.05(g).
Additionally, as also noted above, claim 18 recites carrying out the method on “one or more computers” and “one or more storage devices,” which are claimed at a high level of generality and are merely a tool to apply the abstract idea. As a result, these do not result in significantly more than insignificantly extra-solution activity and applying the abstract on a generic computer. See MPEP §§ 2106.05(b),(f),(g).
Limitation [13.A] of claim 13 is also just mere data gathering since there is nothing more detailed on the way in which the “plurality of training examples” are obtained. Claim 13 additionally recites:
… processing the electron-orbital features in the training example using the neural network and in accordance with current values of the parameters to generate a predicted exchange-correlation energy for the training example; and
updating the current values of the parameters using the gradient.
None of these limitations amount to significantly more than the abstract idea itself. There is no detail in the claim on how the “neural network” is used for “processing the electron-orbital features in the training example.” Additionally, the claim does not define the “neural network” except at a high level of generality. The last limitation, “updating the current values of the parameters using the gradient,” is nothing more than updating data at a high level of generality. Doing so is both insignificant extra-solution activity and well-known, routine, and conventional, and as such, does not amount to significantly more than the abstract idea itself. See MPEP §§ 2106.05(d), subsection II, and 2106.05(g) (citing Ultramercial, Inc. v. Hulu, LLC , 772 F.3d 709, 716 (Fed. Cir. 2014)). Moreover, even when considering all limitations together, the claims merely recite instructions to implement an abstract idea or other exception on a generic computer, and/or insignificant extra-solution activity, which do not provide an inventive concept.
Based on the analysis in steps 2A and 2B as explained above, claims 1, 13, and 18 recite an abstract idea without significantly more and are directed to patent ineligible subject matter.
Dependent Claims 2-11, 14-17, and 20
Claims 2-11, 14-17, and 20 merely recite additional mathematical or mental process concepts, or recite limitations that are merely linking the abstract idea to well-known, routine, and conventional mathematical constraints or algorithms without anything more. As a result, these claims too are directed to an abstract idea without significantly more and are directed to patent ineligible subject matter.
Claims 2 and 20 recites virtually identical limitations that further define the “neural network “ as being trained by “training examples”. These limitations, however, are virtually identical to limitation [13.A] discussed above with respect to claim 13. As a result, these limitations, for the same reasons above, do not make the claimed invention patent eligible.
Claims 3-11 recites additional features that merely require another mathematical function or human executed step to be performed, such as “satisfying one or more mathematical constraint conditions,” performing “real world” measurements, defining a “real-space quadrature grid,” defining features as “distributions,” converting “a linear scale to a logarithm scale” and “concatenating” features, defining the “neural network” as including “a multilayer perceptron (MLP)” and “numerical quadrature layer”, and using known “contribution terms.” These additional limitations and features are either mathematical concepts or actions that can be performed in the human mind. For example, claim 9, while defining the “neural network” as including the “MLP” and “numerical quadrature layer,” these are nothing more than additional mathematically defined features. See PgPub, ¶55; and claim 9 defines the “numerical quadrature layer” as “integrat[ing] a weighted sum of the plurality of contribution terms scaled by the enhancement factors.” Moreover, the results of these mathematical concepts are not used in any practical way to show either integration into a practical application or significantly more than the abstract idea itself.
As a result, none of dependent claims 2-11, 14-17, and 20 recite significantly more than the abstract idea recited in independent claims 1, 13, and 18, from which they all ultimately depend.
For the reasons explained above, none of claims 1-11, 13-18, and 20 are directed to patent eligible subject matter under section 101, and are thus, rejected.
With respect to claim 12, while depending from claim 1, there are recited additional limitations that are considered to limiting the abstract idea to an integrated practical application, thus, claim 12 is patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4-7, 11, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmidt2.
Claim 1
Claim 1 recites:
A method performed by one or more computers and for predicting an exchange-correlation energy of an atomic system, the method comprising:
obtaining respective electron-orbital features of the atomic system at each of a plurality of grid points;
generating, for each of the plurality of grid points, a respective input feature vector for the electron-orbital features at the grid point; and
processing the respective input feature vectors for the plurality of grid points using a neural network to generate a predicted exchange-correlation energy of the atomic system.
Schmidt discloses the entirety of claim 1 for the following reasons. Schmidt in the paragraph spanning pages 2 and 3 states:
We sampled 20 000 systems and calculated their exact ground-state energy and ground-state electronic density. We used a grid spacing of 0.1 au, and a box size of 20 au, leading to a grid with 201 points. The nuclei positions ai in eq 4 were normally distributed with zero mean and variance of 4 au[.] We then solved the corresponding inverse Kohn−Sham problem in OCTOPUS [(Andrade, X.; et al. Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems. Phys. Chem. Chem. Phys. 2015, 17, 31371)] to find the exact exchange−correlation energy and potential.
A plurality of grid points (e.g., “201”) are determined and their “nuclei positions ai” are also determined, which discloses the “obtaining respective electron-orbital features of the atomic system at each of a plurality of grid points,” where “electron-orbital features” are understood to be the relative positions (see PgPub, ¶46). Based on these grid points, nuclei positions, and density, a vector is generated as determined by equation 4, and thus, Schmidt discloses “generating, for each of the plurality of grid points, a respective input feature vector for the electron-orbital features at the grid point.” Lastly, the problem based on the above determined data is solved using OCTOPUS, which is considered a “neural network,” resulting in “the exact exchange−correlation energy and potential,” and thus, Schmidt finally discloses “processing the respective input feature vectors for the plurality of grid points using a neural network to generate a predicted exchange-correlation energy of the atomic system.”
Claim 18
Claim 18 recites a “system comprising: one or more computers; and one or more storage devices storing instructions that when executed by the one or more computers, cause the one or more computers to perform operations comprising” the same steps as recited in the method of claim 1. Schmidt discloses on page 1, the last paragraph, and page 2, the right column, using “machine learning” and using “Octopus code” for solving the noted problem, which means there must be at least one processor (i.e., “computer”) to execute the code, where the code is “instructions” that must also be stored on a device for compiling and execution. Additionally, Schmidt discloses the method of claim 1, as explained above. As a result, claim 18 is anticipated by Schmidt and rejected under section 102(a)(1).
Claims 2 and 20
Claim 2 recites the “method of claim 1, wherein: the neural network is trained on training data that includes a plurality of training examples, the training examples each corresponding to a respective atomic system, and the training examples including a first subset of training examples that correspond to atomic systems that have electron-orbital features and energy levels satisfying one or more mathematical constraint conditions.” Claim 20 recites the “system of claim 18, wherein the neural network is trained on training data that includes a plurality of training examples, the training examples each corresponding to a respective atomic system, and the training examples including a first subset of training examples that correspond to atomic systems that have electron-orbital features and energy levels satisfying one or more mathematical constraint conditions.” Schmidt discloses these limitations at the paragraph spanning pages 2 and 3, which states, “[w]e used up to 12800 of these systems for training, 6400 for validation during the training, and 2000 Systems for the test set. Furthermore, training was considerably improved when removing outliers with Exc > −0.55 au from the training set,” where Exc is an energy level. Thus, claims 2 and 20 are anticipated by Schmidt.
Claim 4
Claim 4 recites and Schmidt discloses the “method of claim 2, wherein: the atomic systems corresponding to the plurality of training examples include synthetically generated virtual atomic systems with electron-orbital features and energy levels satisfying the one or more mathematical constraint conditions.” Schmidt, paragraph spanning pages 2-3, where since the calculations are based on a model and model data (see also the description of “Data” in the right column on page 2), the atomic systems are “synthetic” and generated as described to solve the problem and train the model.
Claim 5
Claim 5 recites and Schmidt discloses the “method of claim 2, wherein the training examples are associated with data describing physical properties of the corresponding atomic system, the data for a plurality of the training examples having been obtained by measurements performed in the real world on the corresponding atomic systems.” Schmidt, page 6, right column, second-to-last paragraph, “Training three-dimensional systems will have to be accomplished by using data obtained with coupled-cluster, full configuration-interaction, or quantum Monte Carlo. While sufficient data to train a universal functional still has to be created, exchange−correlation energies and potentials for a few small molecules already exists and can provide a good starting point,” in other words, some of the training data is based on “real world” atomic systems. (Emphasis added.)
Claim 6
Claim 6 recites and Schmidt discloses the “method of claim 1, wherein: the plurality of grid points include grid points on a real-space quadrature grid.” Schmidt, page 2, right column, “Qualitatively close to real 3D systems, this 1D model is known as a theoretical laboratory for studying strong correlation and developing exchange−correlation density functionals.”
Claim 7
Claim 7 recites and Schmidt discloses the “method of claim 1, wherein: the electron-orbital features obtained for the atomic system include one or more of: an electron density distribution, an electron density gradient norm distribution, a kinetic energy density distribution, a local Hartree-Fock (HF) exchange distribution, or a range-separated form of the local HF exchange distribution of the atomic system.” Schmidt, page 2, right column, “The nuclei positions ai in eq 4 were normally distributed with zero mean and variance of 4 au[.]”
Claim 11
Claim 11 recites and Schmidt discloses the “method of claim 1, wherein: one or more of the electron-orbital features of the atomic system are obtained based on real-world measurement data.” Schmidt, page 6, right column, second-to-last paragraph, “Training three-dimensional systems will have to be accomplished by using data obtained with coupled-cluster, full configuration-interaction, or quantum Monte Carlo. While sufficient data to train a universal functional still has to be created, exchange−correlation energies and potentials for a few small molecules already exists and can provide a good starting point,” in other words, some of the training data is based on “real world” atomic systems. (Emphasis added.)
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Krueger3, which is in the same field of manufacturing chemical compound based on a mathematical model as the claimed invention.
Claim 3
Claim 3 recites that “the one or more mathematical constraint conditions include: a uniform electron gas (UEG) constraint condition, a fractional charge (FC) constraint condition, or a fractional spin (FS) constraint condition,” which Schmidt does not necessarily disclose. Krueger remedies this and teaches that the UEG condition may be used as a mathematical condition. See Krueger, ¶51. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a UEG condition, as in Krueger, with the mathematical model in Schmidt because this provides “good approximation” of the exchange-correlation energy. See id.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of CN ‘8364, which is in the same field of manufacturing chemical compound based on a mathematical model as the claimed invention.
Claim 10
Claim 10 recites that “the plurality of contribution terms include one or more of: a local-density approximation (LDA) exchange term, an HF term, or a range-separated HF term,” which Schmidt does not necessarily disclose. CN ‘836 remedies this and teaches that the LDA term may be used as a mathematical condition when solving the problem. See CN ‘836, ¶43. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a LDA term, as in CN ‘836, with the mathematical model in Schmidt because this provides a “reasonable” result of the exchange-correlation energy. See id.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Teter5, which is in the same field of manufacturing chemical compound based on a mathematical model as the claimed invention.
Claim 12
Claim 12 recites that the “method of claim 1, further comprising: determining based on the predicted exchange-correlation energy of the atomic system, whether to perform a fabrication process of a chemical product which comprises the atomic system, and, if the determination is positive, causing the fabrication to be performed,” which Schmidt does not necessarily teach. Even so, Teter remedies this and teaches that the mathematical equations can be used to predict the physical and chemical properties of materials, which is a determination of a positive result that allows for “identifying reaction conditions and/or material compositions which enhance the properties and/or yield of a finished product.” Teter 13:22-26. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine using the result of the prediction, as in Teter, with the calculation of the prediction, as in Schmidt, to produce a product with enhanced properties. See id.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following article, James Kirkpatrick et al., “Pushing the frontiers of density functionals by solving the fractional electron problem.” Science 374, 1385-1389 (2021). DOI:10.1126/science.abj6511, is virtually identical to the U.S. provisional filings (i.e., the appendix) to which this application claims a benefit as noted above. There are overlapping authors with the inventors of this application, however, there are more authors listed in the article than inventors listed on this application. Applicant is advised and reminded that inventorship should be carefully evaluated in light of the claimed invention and updated as necessary. See MPEP § 602.01.
U.S. Patent Application Publication No. 2016/0181516 describes using the findings of the described invention to “open up the possibility of manufacturing ultra-thin, flexible 2D phase change electronic devices with potential for higher energy efficiency than conventional electronic devices.” Id. at ¶30.
U.S. Patent Application Publication No. 2022/0165364 describes a system of molecular simulation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA KADING whose telephone number is (571) 270-3413. The examiner can normally be reached Monday-Friday, 8:00 AM to 5:00 PM Eastern Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Eileen Lillis can be reached at 571-272-6928. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA KADING/ Primary Patent Examiner, Art Unit 3993
1 https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf.
2 Schmidt et al., "Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory," CoRR, Submitted on October 2019, 1908.06198v2, 8 pages (“Schmidt”). Applicant-cited in the IDS filed Nov. 21, 2024.
3 U.S. Patent Application Publication No. 2018/0057756, to Krueger et al. (“Krueger”).
4 Chinese patent document CN 108121836 (“CN ‘836”). All citations will be to the English translation attached to the CN ‘836 document.
5 U.S. Patent No. 6,106,562, to Teter et al. (“Teter”).
Prosecution Insights