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 .
This communication is responsive to amended application filed on 02/05/2026.
Claims 4, 12, and 18 are canceled.
Claims 1-3, 5-11, 13-17, and 19-20 are presented for examination.
Response to Arguments
Applicant's arguments filed 02/05/2026 have been fully considered but they are not persuasive.
Applicants argued:
Claim 1 recites:
A method for ground state inference, comprising: modeling a material state of a selected material utilizing an energy based (EB) deep convolutional neural network (DCNN) (EB DCNN) model; inferring, by the EB DCNN model, an energy function and a ground state of the selected material according to the modeling of the material state; and predicting, by the EB DCNN model, a different, unobserved material state of the selected material by estimating the different, unobserved material state using latent state learning in response to the inferring of the ground state of the material.
As recited highlighted portion of claim 1 is simply fall under mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts. Further, claim 1 recites an additional element of “deep convolutional neural network (DCNN) (EB DCNN) model” which merely indicates a field of use or technological environment in which the judicial exception is performed. Further, the additional element of “using a machine learning model” is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Applicant’s arguments/amendments with respect to claim 17 have been fully considered and are persuasive. The rejection of 35 USC 101 has been withdrawn.
Applicant’s arguments/amendments, see Remarks pgs. 8-9, filed 02/05/2026, with respect to claims 9-11, 13-17 and 19-20 have been fully considered and are persuasive. The rejection of 35 USC 103 has been withdrawn.
Examiner Notes: Examiner would like to suggest to overcome the 35 USC 101 rejection by including some limitations recited in par [0035] for deep convolutional network to have additional elements to integrate the abstract idea into practical application and/or to provide an inventive concept.
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 1-3, 5-11, 13-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-3, 5-8 are directed to a method.
Claims 9-11, and 13-16 are directed to a product.
Claims 17 and 19-20 are directed to a system.
Therefore, claims 1-3, 5-11, 13-17, and 19-20 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method for ground state inference, comprising:
modeling a material state of a selected material [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts];
inferring an energy function and a ground state of the selected material according to the modeling of the material state [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts]; and
predicting a different material state of the selected material in response to the inferring of the ground state of the material [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim recites an additional element of “deep convolutional neural network”. The “deep convolutional neural network” is used to generally apply the abstract idea without placing any limits on how the machine learning model used. The recitation of “deep convolutional neural network” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “deep convolutional neural network” limits the identified judicial exception “inferring of the ground state of the material”, this type of limitation merely confines the use of the abstract idea to a particular technology environment (machine learning) and thus fails to add an inventive concept to the claims.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): as explained above with respect to Step 2A, Prong two, the additional element of “using a machine learning model” is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As per claim 2, the claim falls into [ mathematical concepts].
As per claim 3, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 4, Canceled.
As per claim 5, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 6, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 7, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 8, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per Claims 9-11, 13-17 and 19-20, claims 9-11, 13-17 and 19-20 recite limitations analogous in scope to those of claims 1-3, and 5-8, and as such are similar rejected.
Allowable Subject Matter
Claims 1-3, 5-11, 13-17, and 19-20 would be allowable over prior art and allowed when amended to overcome rejections above.
YOO et al (US Publication No. 2022/0068824 A1) discloses Abstract, Provided are a wiring material for a semiconductor device, the wiring material including a boride-based compound containing boron and at least one metal selected from elements of Groups 2 to 14, a wiring for a semiconductor device including the same, and a semiconductor device including the wiring containing the wiring material; [0081] The new wiring material, according to inventive concepts, provides a boride-based compound containing boron and at least one metal selected from the group consisting of elements of Groups 2 to 14. The boride-based compound, which is a compound of boron and a metal element, generally has properties of an intermetallic compound, a similar appearance to a metal, quite high rigidity, and a high melting point of 1300° C. or greater, for example, 2000° C. or greater, and is chemically inert and superb in view of thermal and electrical conductivities); par [0198] 0198] The electronic state of a material is calculated using the VASP (Vienna Ab initio simulation package) code, which is a first principles DFT code. Then, a candidate material group including two-dimensional electron gas (2DEG) is selected using the Inorganic Crystal Structure Database (ICSD), information about the atomic structure thereof is input, and the energy level for the electrons is then calculated by simulation. Next, for such electrons, an energy density function and a state density function on a k-space are calculated; par [0200] To predict the figure of merit of the boride-based compound as a metal conducting material, a semi-classical Boltzmann transport model may be introduced and the figure of merit of the boride-based compound may be analyzed using Equations 3 to 5 of Table 1.s.
SHMILOVICH et al (US Publication No. 2023/0359929 A1) discloses par [0002] Quantum chemistry workflows can obtain such chemical and physical information by modelling the electronic Schrodinger equation in a chosen basis set of localized atomic orbitals that is then used to derive the ground-state molecular wavefunction.
CHAKRABARTTY et al (US Publication No. 2020/0401876 A1) discloses [0117] Investigating real-time learning algorithms for GT neural network is critical to investigate a dynamical systems approach to adapt Q such that the network can solve a recognition task, as opposed to assuming the synaptic matrix Q to be fixed. In this regard, most learning algorithms are energy-based, developed with the primary objective of minimizing the error in inference and the energy functional captures dependencies among variables, for example, features and class labels. Learning in this case consists of finding an energy function that associates low energies to observed configurations of the variables, and high energies to unobserved ones. The energy function models the average error in prediction made by a network when it has been trained on some input data, so that inference is reduced to the problem of finding a good local minimum in this energy landscape. In the proposed approach, learning would mean optimizing the network for error in addition to energy, which in this case models a different system objective—the one of finding optimal neural responses.
YOO et al, SHMILOVICH et al, CHAKRABARTTY et al and other prior arts do not singularly or in combination disclose the limitations: “predicting, by the EB DCNN model, a different, unobserved material state of the selected material by estimating the different, unobserved material state using latent state learning in response to the inferring of the ground state of the material” as recited in claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 04/20/2026
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