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 .
Status of the Application
Claims 1-20 are currently pending in this application.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 08/10/2023. It is noted, however, that applicant has not filed a certified copy of the JP 2023-131534 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/06/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 2, 4, 6, and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 5, and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Neumann Barros Ferreira et al. (Hereafter, “Neumann”) [US 2023/0043363 A1].
Regarding claim 1, Neumann discloses a learning device ([0034] FIG. 5 focuses on the training of an AI machine-learning model and its later application to predict adsorption properties.) comprising: a processor ([0004] A system that includes one or more processors operable to perform one or more methods described herein also may be provided.); and a memory storing program instructions that cause the processor ([0005] A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided. [0080] The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.) to: generate a learned model ([0003] generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs) by performing machine learning using a training dataset in which a descriptor of a molecular structure of an adsorbate ([0025] In some embodiments, the user 201 starts the interaction with the system by defining a subset of materials of interest to be analysed 202. This subset 203 could, for instance, be defined by a certain class of nanoporous materials (i.e., zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, covalent organic framework or porous polymer networks, etc). Each material of interest is defined as a Crystallographic Information File (CIF). [0026] a complete CIF is one that includes, at least: a) crystal cell lengths and angles; b) symmetry group, symmetry number or list of symmetry operations; c) atom type and fractional coordinates) to be adsorbed to an adsorbent is associated with an interaction index of one or more intermolecular bonds of interest between the adsorbate and the adsorbent ([0022] The process includes step S1 of generating input features for each material of a subset of materials to be screened and step S2 of generating target properties for each material of the subset of materials. Next, the process includes step S3 of inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and step S4 of training the material screening artificial intelligence model based on the inputs. [0023] As shown in more detail in the flow diagram of FIG. 2, in one embodiment of the material screening process, a user 101 compiles a subset of candidate materials 102 for creating a training subset of material definitions 103. The training subset of candidate material 103 may be a small set of materials that are part of a much larger set of materials to be screened. The training material subset 103 is subjected to a series of calculations 105 and 106 to be described below that extract the input features 108 and target properties 109. The screening conditions 107, input features 108 and target properties 109 are used to train an artificial intelligence (AI) model 110. The trained AI model 111 can then be used as part of a Rapid AI-based screening workflow 113 to process the much larger full material dataset 112 and rank those materials in terms of their target properties 114. [0031] Depending on the user input 211 the simulation will comprise an adsorption isotherm (222), an adsorption isobar (224) or an adsorption simulation for a single pressure and temperature value 226. In any case, the resulting isotherm 223, isobar 225 or single pressure-temperature (P,T) adsorption metric 227 is stored via the REST API 208 into the NoSQL database 209.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the different embodiments of Neumann to include the additional features for the system [Official Notice].
Claim 3 lists all the same elements of claim 1, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to claim 3.
Regarding claim 5, Neumann discloses a screening device ([Abstract] material screening process [0001] This disclosure is directed to computers, and computer applications, and more particularly to computer-implemented methods and systems for material screening for target properties.) comprising: a processor ([0004] A system that includes one or more processors operable to perform one or more methods described herein also may be provided.); and a memory storing program instructions that cause the processor to ([0005] A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.): acquire a prediction target adsorbent and a descriptor of a molecular structure of a prediction target adsorbate to be adsorbed to the prediction target adsorbent ([0025] In some embodiments, the user 201 starts the interaction with the system by defining a subset of materials of interest to be analysed 202. This subset 203 could, for instance, be defined by a certain class of nanoporous materials (i.e., zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, covalent organic framework or porous polymer networks, etc). Each material of interest is defined as a Crystallographic Information File (CIF). [0026] a complete CIF is one that includes, at least: a) crystal cell lengths and angles; b) symmetry group, symmetry number or list of symmetry operations; c) atom type and fractional coordinates [0028] The system then launches a virtual experiment 212 that will retrieve from the database 209 a set of CIFs 210 representing the unit cell of the adsorbent materials under study. [0030] In some embodiments, each supercell CIF 216 is processed to compute their adsorption in steps 217-227, topological properties in steps 228-229 and geometric properties in steps 230-231. In one embodiment, the computation of the adsorption properties of each material proceeds first with charges being assigned to each atom in the supercell 217 using methods such as Qeq, EQeq, DDEC, among others.); and predict an interaction index of one or more intermolecular bonds of interest between the prediction target adsorbate and the prediction target adsorbent ([0031] Depending on the user input 211 the simulation will comprise an adsorption isotherm (222), an adsorption isobar (224) or an adsorption simulation for a single pressure and temperature value 226. In any case, the resulting isotherm 223, isobar 225 or single pressure-temperature (P,T) adsorption metric 227 is stored via the REST API 208 into the NoSQL database 209. [0032] The adsorption properties of these materials can be simulated using, in one example, the Grand Canonical Monte Carlo simulation method as implemented by various open-source programs such as Cassandra, DL Monte, and others.), using a learned model generated using a training dataset in which a descriptor of a molecular structure of an adsorbate to be adsorbed to an adsorbent is associated with an interaction index of one or more intermolecular bonds of interest between the adsorbate and the adsorbent ([0003] Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials may include the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening. [0022] The process includes step S1 of generating input features for each material of a subset of materials to be screened and step S2 of generating target properties for each material of the subset of materials. Next, the process includes step S3 of inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and step S4 of training the material screening artificial intelligence model based on the inputs. [0023] As shown in more detail in the flow diagram of FIG. 2, in one embodiment of the material screening process, a user 101 compiles a subset of candidate materials 102 for creating a training subset of material definitions 103. The training subset of candidate material 103 may be a small set of materials that are part of a much larger set of materials to be screened. The training material subset 103 is subjected to a series of calculations 105 and 106 to be described below that extract the input features 108 and target properties 109. The screening conditions 107, input features 108 and target properties 109 are used to train an artificial intelligence (AI) model 110. The trained AI model 111 can then be used as part of a Rapid AI-based screening workflow 113 to process the much larger full material dataset 112 and rank those materials in terms of their target properties 114. [0031] Depending on the user input 211 the simulation will comprise an adsorption isotherm (222), an adsorption isobar (224) or an adsorption simulation for a single pressure and temperature value 226. In any case, the resulting isotherm 223, isobar 225 or single pressure-temperature (P,T) adsorption metric 227 is stored via the REST API 208 into the NoSQL database 209.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the different embodiments of Neumann to include the additional features for the system [Official Notice].
Regarding claim 12, the limitations of claim 5 have been addressed. Neumann discloses wherein either the adsorbent or the adsorbate is a molecular crystal ([0025] FIG. 3 focuses on the compilation of a collection of crystal structure files that serve as input for the screening method. In some embodiments, the user 201 starts the interaction with the system by defining a subset of materials of interest to be analysed 202. This subset 203 could, for instance, be defined by a certain class of nanoporous materials (i.e., zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, covalent organic framework or porous polymer networks, etc). Once the subset of interest 203 is defined, a collection of web scrappers 204 extract data from known sources of information for that particular class of materials. In some embodiment, the sources can either be proprietary (internal databases) or open-source repositories, with licenses such as creative common copyright licenses. Each material of interest is defined as a Crystallographic Information File (CIF).).
Regarding claim 13, the limitations of claim 5 have been addressed. Neumann discloses wherein the adsorbent is a molecular crystal and the adsorbate is a molecule ([0025] FIG. 3 focuses on the compilation of a collection of crystal structure files that serve as input for the screening method. In some embodiments, the user 201 starts the interaction with the system by defining a subset of materials of interest to be analysed 202. This subset 203 could, for instance, be defined by a certain class of nanoporous materials (i.e., zeolites, metal-organic frameworks, zeolitic imidazolate frameworks, covalent organic framework or porous polymer networks, etc). Once the subset of interest 203 is defined, a collection of web scrappers 204 extract data from known sources of information for that particular class of materials. In some embodiment, the sources can either be proprietary (internal databases) or open-source repositories, with licenses such as creative common copyright licenses. Each material of interest is defined as a Crystallographic Information File (CIF). [0034] set of adsorbate gas composition).
Claim 14 lists all the same elements of claim 5, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 5 applies equally as well to claim 14.
Claim(s) 7-11 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of “Looking beyond Adsorption Energies to Understand Interactions at Surface using Machine Learning,” Agarwal et al. (Hereafter, “Agarwal”).
Regarding claim 7, the limitations of claim 5 have been addressed. Neumann fails to explicitly disclose wherein the program instructions further cause the processor to: calculate energy EA of the adsorbent; calculate energy EB of the adsorbate; and calculate energy EA+B of a surface adsorption structure in which the adsorbate is adsorbed to the adsorbent.
Agarwal discloses wherein the program instructions further cause the processor to: calculate energy EA of the adsorbent; calculate energy EB of the adsorbate; and calculate energy EA+B of a surface adsorption structure in which the adsorbate is adsorbed to the adsorbent ([Page 5] Esystem is the energy of metal surface + adsorbate).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teachings of Neumann with the known calculation of the energy of the surface plus the energy of the absorbate as taught by Agarwal in order to improve performance of the system [See Agarwal].
Regarding claim 8, the limitations of claim 7 have been addressed. Neumann fails to explicitly disclose wherein the program instructions further cause the processor to calculate adsorption energy Eads of the adsorbate from the energy EA of the adsorbent, the energy EB of the adsorbate, and the energy EA+B of the surface adsorption structure.
Agarwal discloses wherein the program instructions further cause the processor to calculate adsorption energy Eads of the adsorbate from the energy EA of the adsorbent, the energy EB of the adsorbate, and the energy EA+B of the surface adsorption structure ([Page 5] The following formula was used to calculate the adsorption energies: Eads = Esystem − (EM +Emolecule) where, Esystem is the energy of metal surface + adsorbate, EM is the energy of the bare metal surface, and Emolecule is the energy of the bare adsorbate.).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teachings of Neumann with the known calculation of the adsorption energies as taught by Agarwal in order to improve performance of the system [See Agarwal].
Regarding claim 9, the limitations of claim 8 have been addressed. Neumann discloses wherein the program instructions further cause the processor to calculate the interaction index ([0031] Depending on the user input 211 the simulation will comprise an adsorption isotherm (222), an adsorption isobar (224) or an adsorption simulation for a single pressure and temperature value 226. In any case, the resulting isotherm 223, isobar 225 or single pressure-temperature (P,T) adsorption metric 227 is stored via the REST API 208 into the NoSQL database 209.).
Regarding claim 10, the limitations of claim 9 have been addressed. Neumann fails to explicitly disclose wherein the program instructions further cause the processor to generate the learned model by using the descriptor of the molecular structure of the adsorbate used to calculate the energy EB and the calculated interaction index.
Agarwal discloses wherein the program instructions further cause the processor to generate the learned model by using the descriptor of the molecular structure of the adsorbate used to calculate the energy EB and the calculated interaction index ([Pages 5-6] The data collected from DFT calculations was then used to train ML models. Data points for only adsorption (and not dissociation) were used while training and testing the model. [Page 8] Correlation between bondlength activation of adsorbate and their adsorption energies. Two parameters viz. adsorption energies (Eads) and the amount of activation that adsorbate bonds undergo upon interaction (BLact) were carefully analyzed. [Pages 12-13 descriptors for ML).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teachings of Neumann with the teachings of Agarwal in order to improve performance of the system [See Agarwal].
Regarding claim 11, the limitations of claim 7 have been addressed. Neumann fails to explicitly disclose wherein at least one of the calculating of the energy EA, the calculating of the energy EB, or the calculating of the energy EA+B is performed using a machine learning potential.
Agarwal discloses wherein at least one of the calculating of the energy EA, the calculating of the energy EB, or the calculating of the energy EA+B is performed using a machine learning potential [Pages 5-6].
It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teachings of Neumann with the teachings of Agarwal in order to improve performance of the system [See Agarwal].
Claim 16 lists all the same elements of claim 7, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 7 applies equally as well to claim 16.
Claim 17 lists all the same elements of claim 8, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 8 applies equally as well to claim 17.
Claim 18 lists all the same elements of claim 9, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 9 applies equally as well to claim 18.
Claim 19 lists all the same elements of claim 10, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 10 applies equally as well to claim 19.
Claim 20 lists all the same elements of claim 11, but in method form rather than device form. Therefore, the supporting rationale of the rejection to claim 11 applies equally as well to claim 20.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kaitlin A Retallick whose telephone number is (571)270-3841. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at (571) 272-7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KAITLIN A RETALLICK/Primary Examiner, Art Unit 2482
