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 Objections
Claims 1-21 are objected to because of the following informalities:
In claim 1, lines 4-5, “one or more material input properties” should be -- one or more material input properties of the material-- to establish proper antecedent basis for “the one or more material input properties of the material” recited in claim 12; and to distinguish itself from “one or more material input properties of the still another material” recited in lines 16-17 of claim 1.
In claim 1, line 12, “the same functional property” should be --a same functional property-- to avoid the issue of lack of antecedent basis.
In claim 3, line 3, “the density functional theory (DFT) calculations” should be --density functional theory (DFT) calculations-- to avoid the issue of lack of antecedent basis.
In claim 8, lines 1-2, “the computed functional property” should be -- the computed functional characteristic-- to be consistent with its antecedent basis.
In claim 9, lines 2-3, “the computed functional property and the experimentally measured functional properties” should be --the computed functional characteristic and the at least one experimental measurement of the same functional property-- to be consistent with their antecedent bases.
In claim 10, lines 1-2, “the computed functional property does not match the experimental measurements” should be --the computed functional characteristic does not match the experimental measurement-- to be consistent with their antecedent bases.
In claim 10, line 4, “the computed functional property” should be --the computed functional characteristic-- to be consistent with its antecedent basis.
In claim 17, line 5, “the unit cell pool” should be -- the pool of unit cell-- to be consistent with its antecedent basis.
In claim 21, line 2, “the surface configuration” should be --a surface configuration-- to avoid the issue of lack of antecedent basis.
Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1-16 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, it recites “repeating the steps (a)-(f), concerning a material surface of at least one other material to generate a dataset comprising the cumulative functional characteristic for each validated material” in lines 13-15. It is unclear how many times are repeated and for how many validated materials. For examination purpose, --repeating the steps (a)-(f), concerning a material surface of at least one other material to generate a dataset comprising the cumulative functional characteristic for a plurality of validated materials-- is assumed.
Regarding claim 11, it recites “wherein configuration for training a machine learning algorithm does not require conducting steps (i)-(e) for the still another material.” It is unclear what steps (i)-(e) are. It is also unclear whether “a machine learning algorithm” is related to “a global machine learning algorithm” recited in claim 1. For examination purpose, --wherein configuration for training the global machine learning algorithm does not require conducting steps (a)-(f) for the still another material-- is assumed.
The other claim(s) not discussed above, or depending on the above claim(s), are rejected for inheriting the issue(s) from their linking claim(s).
Claim 16 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The claim recites “at least one processor executing instructions stored in memory for conducting the method of claim 1.” It is interpreted that the system comprising the at least one processor has an intended purpose, i.e., for conducting the method of claim 1. The claim does not necessarily require or incorporate the method of claim 1. Therefore, it fails to include all the limitations of the claim upon which it depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim is directed to an abstract idea because it recites the limitations beginning from “(a) representing a surface of a material as an ensemble of unit cells” to the end of the claim. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites no elements. Accordingly, no additional element is insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claim 17 is similarly rejected because it recites the limitations beginning from “determining functional characteristics of units cells among a pool of unit cells of a material surface” to the end of the claim. These are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper). There is no additional element to sufficiently integrate the abstract idea into a practical application of, or make the claims significantly more than, the abstract idea.
Dependent claims 2-16 and 18-21 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
Claim Rejections - 35 USC § 102
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 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 17 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pedersen et al. ("High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions" ACS Catal. 2020, 10, 2169-2176; hereinafter “Pedersen”).
Regarding claim 17, Pedersen teaches a method of predicting material structural information in relation to functional characterization (i.e., “By combining density functional theory (DFT) with supervised machine learning, we predict the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surfaces of the disordered CoCuGaNiZn and AgAuCuPdPt HEAs”; see Abstract), the method comprising:
determining functional characteristics (i.e., “DFT calculated adsorption energies”) of units cells (i.e., sites) among a pool of unit cells (i.e., a subset of “corresponding surface microstructures”, “sites”) of a material surface (i.e., “the use of a simple surface microstructure model and supervised machine learning. This allows the almost instantaneous prediction of the adsorption energies of all surface sites using only a subset of known DFT calculated adsorption energies and their corresponding surface microstructures as an input for the machine learning regressor”; see p. 2170, col. 2, ¶ 3);
determining a predicted structure of the material surface (i.e., “HEA compositions”) based on the unit cell pool (i.e., “We present an approach for a probabilistic and unbiased discovery of selective and active catalysts for the carbon dioxide (CO2) and carbon monoxide (CO) reduction reactions on high-entropy alloys (HEAs). By combining density functional theory (DFT) with supervised machine learning, we predict the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surfaces of the disordered CoCuGaNiZn and AgAuCuPdPt HEAs. This allows an optimization for the HEA compositions with increased likelihood for sites with weak hydrogen adsorption to suppress the formation of molecular hydrogen and with strong CO adsorption to favor the reduction of CO”; see Abstract); and
determining a predicted material activity (i.e., “we defined a measure of the CORR activity”; see p. 2172, col. 1, ¶ 2) based on the determined functional characteristics (i.e., absorption energy each site i) and the determined predicted structure (i.e., the activity is per HEA composition during discovery; see equation for “CORR activity” at p. 2172), and outputting a characterization of material structural information of the material surface based on the predicted material activity (i.e., “it is possible to produce a map of the CO2RR/CORR selectivity and CORR activity for every ratio of elements in the HEAs, as shown in Figure 3”; see p. 2172, col. 2, ¶ 2).
Regarding claim 21, Pedersen further teaches:
wherein outputting a characterization includes a dataset for training a machine learning model (i.e., “The data used to train the Gaussian process regressors consist of hundreds of DFT adsorption energy calculations for on-top adsorbed CO and fcc-hollow and hcp-hollow adsorbed H for CoCuGaNiZn and AgAuCuPdPt on randomly populated (111) facets of periodically repeated 2 × 2 x 5 atoms slabs depicted in Figure S1”; see p. 2170, col. 2, ¶ 4; note that the training dataset is necessarily outputted in order to be used for training the machine learning model) for predicting the surface configuration of unit cells for new materials based on material input properties (i.e., “the use of a simple surface microstructure model and supervised machine learning. This allows the almost instantaneous prediction of the adsorption energies of all surface sites using only a subset of known DFT calculated adsorption energies and their corresponding surface microstructures as an input for the machine learning regressor”; see p. 2170, col. 2, ¶ 3).
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.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pedersen.
Regarding claim 18, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Pedersen does not explicitly disclose:
wherein determining the predicted material activity includes determining whether a threshold prediction of material activity is achieved, and re-determining the predicted structure in response to determination that the threshold prediction has not be achieved.
However, since Pedersen is about discovering HEA material composition that meets desired carbon monoxide reduction reaction (CORR) level (i.e., “optimize the catalytic selectivity in the CO2RR and the subsequent CORR needed for the formation of highly reduced carbon products”; see p. 2170, col. 1, ¶ 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pedersen such that determining the predicted material activity includes determining whether a threshold prediction of material activity is achieved, and re-determining the predicted structure in response to determination that the threshold prediction has not be achieved, as claimed. The rationale would be to determining the optimized HEA based on whether the predicted activity is within a desired range (i.e., threshold).
Regarding claim 19, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Pedersen does not explicitly disclose:
wherein the threshold prediction of material activity is determined by comparison of the predicted material activity with experimental results.
However, it is well-known to determine acceptability of products for a particular application by experiments, i.e., testing the material to check the performance. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Pedersen, such that the threshold prediction of material activity is determined by comparison of the predicted material activity with experimental results, as claimed. The rationale would be to find an acceptable activity level as the threshold for selecting the material in an intended application comparable to the experiment.
Regarding claim 20, as a result of modification applied to claim 18 above, Pedersen further teaches:
wherein in response to determination that the threshold prediction is achieved (see discussion in claim 18), outputting is performed (i.e., “discovering new catalyst materials with better properties such as catalytic activity, selectivity, and stability”; see p. 2169, Introduction, ¶ 1).
Notes
Claim 1 distinguishes over the closest prior art of record as discussed below.
Regarding claim 1, the closest prior art of record fails to teach the features: “repeating the steps (a)-(f), concerning a material surface of at least one other material to generate a dataset comprising the cumulative functional characteristic for each validated material, the dataset configured for training a global machine learning algorithm for predicting surface configuration of unit cells for still another material based on one or more material input properties of the still another material,” in combination with the rest of the claim limitations as claimed and defined by the Applicant.
Pedersen teaches substantially the features of claim 1, such as representing a surface of a material as an ensemble of unit cells (i.e., unit sites); determining a pool of unit cells (i.e., subset); computing functional characteristics of the unit cells (i.e., absorption energy per site); computing cumulative functional characteristic for the material (i.e., CORR activity); and validation by experiments.
However, the main difference is that Pedersen uses the computed density functional theory (DFT) data of a site (i.e., unit cell) for training a local machine learning model to learn the DFT data for the site. Pedersen then uses predicted results of the local machine leaning model for plural sites to compute a cumulative CORR activity for the material. It does not teach or suggest computing cumulative functional characteristic for training a global machine learning model for predicting surface configuration of the material. Moreover, Pedersen’s computation of CORR activity (may be viewed as cumulative data of the unit cells) is by summing the local predicted DFT data weighted based on probability Pi that depends on the composition of the HEA material. To use the CORR activity as training data for a global machine learning model (as claimed) would defeat the purposes of per-site prediction by the local machine learning model and the computation of the CORR activity considering the different probability Pi for each site.
Batchelor et al. 2019 ("High-Entropy Alloys as a Discovery Platform for Electrocatalysis" Joule 3, 834-845 March 20, 2019) teaches a combination of DFT for abortion energy calculation with a machine learning to predict remaining absorption energies of a material to optimize the material composition. Batchelor does not cure the deficiency of Pedersen.
None of the closest prior art of record, singly or in combination, teaches or suggests the above indicated features as claimed.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jain et al. ("Computational predictions of energy materials using density functional theory" NATURE REVIEWS | MATERIALS VOLUME 1 | JANUARY 2016) provides a review of predicting performance of a material using DFT.
Fiedler et al. ("Deep dive into machine learning density functional theory for materials science and chemistry" PHYSICAL REVIEW MATERIALS 6, 040301 (2022)) reviews the development of the combination of DFT and machine learning in in silico materials discovery.
Obika et al. (US 20210334655 A1) teaches a method of predicting properties of materials, involving processing predicted embedding of the new material using an experimental prediction neural network to predict one or more properties of the new material.
Hegde (US 20210081834 A1) teaches a method for obtaining learned self-consistent electron density and/or derived physical quantities, involving mapping a first non-self-consistent (NSC) dataset to a first self-consistent (SC) dataset utilizing machine learning algorithm to generate a mapping function; and generating a learned self-consistent data from a new NSC data utilizing the mapping function.
WANG et al. (CN 111651916 A) teaches a material performance prediction method based on deep learning, by establishing a finite element model and a deep learning model, and using the result of finite element model to train the deep learning model.
Ramprasad et al. (US 20210264080 A1) teaches a method for material simulation, involving receiving an input structure for a material; identifying a reference grid point for the material; and determining a predictive local density of states for the reference grid point based on a plurality of approximate energy levels.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857