DETAILED ACTION
This action is filed in response to the application filed on 12/08/2023.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 12/08/2023, 9/24/2024, 3/17/2025, and 11/07/2025. These IDS have been considered.
Claim Rejections - 35 USC § 101
Claims 1-21 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claims 20-21 recite the following abstract concepts in BOLD of:
An information processing device comprising one or more processors configured to: optimize, for a specific elementary reaction in a reaction using a catalyst including a plurality of elementary reactions, an arrangement of a promoter element in the catalyst based on activation energy acquired using a trained model; and
search for the promoter element based on the activation energy acquired using the trained model for each type of the promoter element.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 teaches a device, claim 20 teaches a method, and claim 21 teaches a non-transitory computer readable medium.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The steps of optimizing an arrangement and searching for an element are recited so broadly that they can be interpreted as a mental process that can be performed in the human mind under the broadest reasonable interpretation.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations teaching an information processing device comprising one or more processors discloses generic computer elements that are not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-19 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2-4,8-9,12, and 17 further limit the abstract mental processes of Claim 1 without significantly more.
Claims 5, 13, and 18, further disclose routine data gathering and do not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claims 6, 16, and 19 recite generic computing components that are not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 7, 10, and 11 further disclose the arrangement of the device which does not integrate the abstract ideas into a practical application.
Claim 14 recites performing a simulation in a model which can be considered instructions to apply the abstract idea to a computer, which is not significantly more than the abstract ideas. See MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer.”
Claim 15 teaches the additional abstract idea of performing mathematics which does not integrate the abstract ideas of claim 1 into a practical application.
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 1-4, 6, 11, 13, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (JP2002210375 A) in view of Ham (KR20200096452 A).
Regarding Claims 1, 20, and 21, Matsumoto discloses an information processing device configured to: optimize, for a specific elementary reaction in a reaction using a catalyst including a plurality of elementary reactions, an arrangement of a promoter element in the catalyst (e.g. see [pg. 2 paragraph 5] “the object of the present invention is to determine a model catalyst containing a main catalyst element and at least one other element, to calculate the most stable structure of the model catalyst and the internal energy of the most stable structure. Means for calculating the internal energy of a reaction molecule which promotes a reaction (i.e. promoter element) by the model catalyst, means for determining the adsorption structure between the reaction molecule and the model catalyst, and the most stable structure of the adsorption structure and the most stable structure Means for calculating the internal energy of the reaction molecule”); and
search for the promoter element based on the activation energy acquired for each type of the promoter element (e.g. see [pg. 2 paragraph 6] “therefore, in the method of the present invention, for many possible catalyst structures, the energies of each of the catalyst, the reactive molecule, and the system including the catalyst and the reactive molecule are obtained by precise calculation based on quantum mechanical calculation”).
Matsumoto does not explicitly disclose an information processing device comprising one or more processors configured to optimize, for a specific elementary reaction in a reaction using a catalyst including a plurality of elementary reactions, an arrangement of a promoter element in the catalyst based on activation energy acquired using a trained model; and search[ing] for the promoter element based on the activation energy acquired using the trained model.
In the same field of endeavor, Ham teaches an information processing device comprising one or more processors (e.g. see [pg. 6 paragraph 9] “A storage medium storing instructions according to another aspect of the present invention, wherein the instructions are configured to cause the at least one processor to perform at least one step when executed by at least one processor, wherein the at least one step Is, determining a learning model through learning using an artificial neural network, inputting catalyst structure and adsorbate information into the learning model, and predicting from the catalyst structure”) configured to optimize, for a specific elementary reaction in a reaction using a catalyst including a plurality of elementary reactions, an arrangement of a promoter element in the catalyst based on activation energy acquired using a trained model (e.g. see [pg. 6 paragraphs 4-5] “an input module for inputting information of a catalyst structure and adsorbent into the learning module, and input to the learning module A catalyst design system is provided, comprising an output module for outputting an oxygen reduction reaction catalytic activity predicted from information of the catalytic structure and adsorbed material. In one implementation, the artificial neural network may include a feedforward neural network (FNN)”); and
search[ing] for the promoter element based on the activation energy acquired using the trained model (e.g. see [pg. 5 last paragraph-pg. 6 paragraph 2] “Next, the input training data input information value and the training data output information value corresponding thereto may be acquired through calculation of the density function theory based on the first principle (S120). Next, a learning model may be obtained by adjusting weights and biases for the acquired learning data input information value and output information value (S130). Specifically, in order to utilize the constructed artificial neural network for catalyst search, weights and biases of nodes constituting the artificial neural network must be determined using input information of learning data and output information of corresponding training data. In addition, in one embodiment, the step of determining the learning model may further include supervised learning (not shown), and the supervised learning is output information of learning data corresponding to the output oxygen reduction reaction catalytic activity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the catalyst information processing method of Matsumoto with the processors and trained model of Ham for the purpose of determining which elementary reaction accomplishes the desired yield with the advantage of a trained model to enhance the accuracy and efficiency of the determination.
Regarding Claim 2, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto further discloses specify[ing] an elementary reaction related to an adsorbed molecule being an elementary reaction affecting a target property in the reaction using the catalyst including the plurality of elementary reactions (e.g. see [pg. 5 paragraph 3] “In the same manner, such calculation is also performed on constituent atoms and / or constituent atom groups of the generated molecules to be generated by the target reaction to determine the adsorption structure with the model catalyst, and the adsorption structure The adsorption energy when the constituent atoms and / or a group of constituent atoms are adsorbed on the model catalyst is calculated using a means including a means for calculating the most stable structure of the above and the internal energy of the most stable structure”); and optimize the arrangement of the promoter element for the elementary reaction specified (e.g. see [pg. 7 paragraph 7] “This adsorption structure was assumed because it is considered as an adsorption structure in which the extremely characteristic properties of gold are maximized as described above”).
Regarding Claim 3, Matsumoto and Ham teach the limitations of Claim 2. Matsumoto further discloses search[ing] for, based on the acquired activation energy for each of a plurality of types of the promoter elements, the promoter element lower or higher in the activation energy than the other promoter elements (e.g. see [pg. 2 paragraph 6] “The fact that the reaction molecule is easily adsorbed on the catalyst should correspond to the low adsorption energy when the reaction molecule is adsorbed on the catalyst. Therefore, in the method of the present invention, for many possible catalyst structures, the energies of each of the catalyst, the reactive molecule, and the system including the catalyst and the reactive molecule are obtained by precise calculation based on quantum mechanical calculation,” and [pg. 3 paragraph 3] “By comparing the adsorption energies of the reactive molecules and the dissociative adsorption energies of the generated molecules for a number of possible catalyst structures, we predict the catalyst structures that are likely to adsorb the reactive molecules and desorb the generated molecules”).
Regarding Claim 4, Matsumoto and Ham teach the limitations of Claim 3. Matsumoto further discloses repeat[ing] a reaction path search a plurality of times with the arrangement of one or a plurality of promoter elements designated in the catalyst; and optimize the arrangement of the promoter element lower in the activation energy than the other arrangements (e.g. see [pg. 4 paragraph 7] “Similarly, setting of a condition in which a displacement is added to the distance between molecules and calculation of the internal energy under the condition are repeated until a constant convergence level is reached. By this repetitive calculation, the minimum internal energy is calculated, and at the same time, the distance between the corresponding constituent atoms and / or constituent atom groups, that is, the most stable structure of the catalyst can be grasped,” and [pg. 5 paragraph 3] “the entire system in which the model catalyst and the reaction molecule are combined is obtained. Find the most stable structure and internal energy. Also here, it is preferable to perform repetitive calculations in consideration of all possible spin states for the entire system”).
Matsumoto does not explicitly disclose repeat[ing] a reaction path search using the trained model a plurality of times. In the same field of endeavor, Ham teaches repeat[ing] a reaction path search using the trained model a plurality of times (e.g. see [pg. 6 paragraph 4] “a learning module including a neural network, an input module for inputting information of a catalyst structure and adsorbent into the learning module, and input to the learning module A catalyst design system is provided, comprising an output module for outputting an oxygen reduction reaction catalytic activity predicted from information of the catalytic structure and adsorbed material”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the repetitive catalyst information processing method of Matsumoto with the trained model of Ham for the purpose of determining which elementary reaction accomplishes the desired yield with the advantage of a trained model to enhance the accuracy and efficiency of the determination.
Regarding Claim 6, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose wherein the trained model is a neural network model trained by a plurality of elements.
In the same field of endeavor, Ham teaches wherein the trained model is a neural network model trained by a plurality of elements (e.g. see [pg. 6 paragraph 4] “a learning module including a neural network, an input module for inputting information of a catalyst structure and adsorbent into the learning module, and input to the learning module”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the repetitive catalyst information processing method of Matsumoto with the trained model of Ham for the purpose of determining which elementary reaction accomplishes the desired yield with the advantage of a trained model to enhance the accuracy and efficiency of the determination.
Regarding Claim 11, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto further discloses arrange the promoter element with some of atoms of the catalyst replaced with the promoter element and/or the promoter element added to the atoms of the catalyst (e.g. see [pg. 5 paragraph 4] “the entire system in which the model catalyst and the reaction molecule are combined is obtained. Find the most stable structure and internal energy. Also here, it is preferable to perform repetitive calculations in consideration of all possible spin states for the entire system,” and [pg. 5 paragraphs 5-6] “Then, from the internal energy (E .sub.M + R ) of the whole system, the adsorption energy (E .sub.a ) when the reactive molecule is adsorbed on the model catalyst is calculated by the following equation. E .sub.a = E .sub.M + R − (E .sub.M + E .sub.R ). In the same manner, such calculation is also performed on constituent atoms and / or constituent atom groups of the generated molecules to be generated by the target reaction to determine the adsorption structure with the model catalyst”).
Regarding Claim 13, Matsumoto and Ham teach the limitations of Claim 2. Matsumoto further discloses wherein the one or more processors are configured to: specify one or a plurality of elementary reactions (e.g. see [pg. 3 paragraph 4] “Here, as the “main catalyst element”, an element that has been found to be most effective for the intended catalytic reaction can be selected.,” and [pg. 4 paragraph 8] “further, the method of the present invention uses a means for calculating the internal energy of the reaction molecule to be examined for the reaction with the model catalyst. The reaction molecules, for example, if the model catalyst catalyst for purifying automotive exhaust gases, NO .sub.x, HC (hydrocarbons) to purification reaction is promoted, it is possible to select a CO, or catalyst poisons Can select the SO .sub.x in question. If the electrode catalyst of the fuel cell is used as a model catalyst, the reaction molecule can select H .sub.2 , CH .sub.4, etc., for which the reaction is to be promoted, or CO 2 which is a problem of catalyst poisons. Can be selected”).
Claims 5, 8, 14-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (JP2002210375 A) in view of Ham (KR20200096452 A) and in further view of Cheng (CN111128311 A).
Regarding Claim 5, Matsumoto and Ham teach the limitations of Claim 4. Matsumoto does not explicitly disclose a device configured to acquire the activation energy using the trained model in a state where a calculation initial structure of the adsorbed molecule to be used for the reaction path search is arranged at the same position.
In the same field of endeavor, Cheng teaches disclose a device configured to acquire the activation energy using the trained model in a state where a calculation initial structure of the adsorbed molecule to be used for the reaction path search is arranged at the same position (e.g. see [pg. 7 paragraph 2] “searching each adsorption steady reaction path (i.e. between elementary reaction), and obtaining the corresponding transition-state structure, and other information and activation energy barrier. micro-dynamics analysis process is as follows: by the Arrhenius equation, the activation energy barrier, partition function based on comentropy vibration calculating the reaction rate constant of elementary reaction, according to the adsorption energy of the reaction species, steady state reaction rate constant and reaction species surface coverage of elementary reaction approximately assumed in calculating the reaction network coverage of each adsorption species, and the reaction rate of each elementary reaction,” and [pg. 9 paragraph 7] “using first principles-based software Intigrity Espresso executing quantum chemical calculation, based on the reaction path network of the direct synthesis of hydrogen peroxide reaction, the palladium-based alloy surface model to calculate the simulation, obtaining reactant on the surface model, the activation barrier for reaction intermediate, the adsorption energy of the final product”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the trained model of Matsumoto as modified by Ham with the activation energy acquisition of Cheng for the purpose of optimizing a catalyst arrangement with the advantage of additional data that enhances the accuracy of the arrangement determinations.
Regarding Claim 8, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose wherein the one or more processors are configured to: optimize the arrangement of the one promoter element using a grid search.
In the same field of endeavor, teaches wherein the one or more processors are configured to: optimize the arrangement of the one promoter element using a grid search (e.g. see [pg. 10 paragraph 4] “network to the catalyst model based on depth forward extends a single atom of-adsorption structure constructing the performance model using a Adam algorithm and 10-fold cross validation training neural network. hyperparameter of the neural network obtained by the grid search”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the trained model of Matsumoto as modified by Ham with the grid search of Cheng for the purpose of optimizing a catalyst arrangement with the advantage of a simplistic search method that also enhances the efficiency of the search.
Regarding Claim 14, Matsumoto and Ham teach the limitations of Claim 2. Matsumoto further discloses specify the elementary reaction affecting the target property based on the effect on the target property (e.g. see [pg. 6 paragraphs 5-6] “the following calculation was performed to predict a catalyst structure that promotes the following reaction. NO .fwdarw. 1 / 2N .sub.2 + 1 / 2O .sub.2 Gold is selected as the main catalyst element, and the additional element is selected from the elements of the fourth to sixth periods of the periodic table. The quantum mechanical calculation described below was performed on a model catalyst in which two atoms were arranged in space. The reason for using gold as the main catalyst element is as follows. Among the noble metals that have been found to be effective for NO .sub.x purification, the gold element has extremely high dissociation and adsorption energy of oxygen molecules, that is, it has extremely characteristic properties such as easy desorption of oxygen molecules. This property is considered extremely important property in promoting .sub.the NO .sub.x purification. On the other hand, gold element has a relatively high adsorption energy of NO .sub.x”).
Matsumoto does not explicitly disclose execute a kinetic simulation while changing a reaction rate constant of the plurality of elementary reactions; acquire effects of the elementary reactions changed in the reaction rate constant on the target property.
In the same field of endeavor, Cheng teaches execute a kinetic simulation (e.g. see [pg. 7 paragraph 2] “forming theory reaction kinetics is specifically as follows: The adsorption species reaction contained in the network, calculating the reactant on the different surface model, intermediate, the adsorption energy of the final product. searching each adsorption steady reaction path (i.e. between elementary reaction), and obtaining the corresponding transition-state structure, and other information and activation energy barrier.,”) while changing a reaction rate constant of the plurality of elementary reactions [pg. 10 last paragraph] “using quasi-Newton algorithm to speed and coordinates of the atom model of palladium-based alloy particles in the model to force balance optimization calculation to obtain new palladium-based alloy grain model of the structure after local optimization”); and
acquire effects of the elementary reactions changed in the reaction rate constant on the target property (e.g. see [pg. 7 paragraphs 8-9] “The target function screening the catalytic material target of excellent catalytic performance, guidance experiment for high throughput preparation and high-flux catalytic performance test. S15, comparing the catalytic performance prediction result and the experiment result of the catalyst structure-activity relationship model. If the deviation is within an acceptable range, then the successful screening the catalytic performance of the catalytic material to reach the target”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the target property and elementary reaction of Matsumoto with the kinetic simulation and changed reaction rate of Cheng for the purpose of optimizing a catalyst arrangement with the advantage of utilizing a model to evaluate the effects of different arrangements on target properties.
Regarding Claim 15, Matsumoto, Ham, and Cheng teach the limitations of Claim 14. Matsumoto does not explicitly disclose wherein the one or more processors are configured to: calculate a parameter of the kinetic simulation by the trained model.
In the same field of endeavor, Cheng teaches wherein the one or more processors are configured to: calculate a parameter of the kinetic simulation by the trained model (e.g. see [pg. 10 paragraph 4] “establishing a first neural network structure feature-can be associated with the model. Specifically, adsorption established based on palladium-based alloy surface structure characteristic of the energy prediction model. network to the catalyst model based on depth forward extends a single atom of-adsorption structure constructing the performance model using a Adam algorithm and 10-fold cross validation training neural network”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the target property and elementary reaction of Matsumoto with the kinetic simulation and parameters of Cheng for the purpose of optimizing a catalyst arrangement with the advantage of utilizing a model to evaluate the effects of different arrangements on target properties.
Regarding Claim 16, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose wherein the processor is configured to: train a model which predicts activation energy by active learning.
In the same field of endeavor, Cheng teaches wherein the processor is configured to: train a model (e.g. see [pg. 8 paragraph 6] “based on the machine learning algorithm, training to obtain the corrected model of catalytic performance theoretical value”) which predicts activation energy by active learning (e.g. see [pg. 7 paragraphs 1-2] “using the reaction kinetics evaluation method obtaining the theoretical catalytic performance of the catalyst model structure.. method wherein, forming theory reaction kinetics is specifically as follows: The adsorption species reaction contained in the network, calculating the reactant on the different surface model, intermediate, the adsorption energy of the final product. searching each adsorption steady reaction path (i.e. between elementary reaction), and obtaining the corresponding transition-state structure, and other information and activation energy barrier”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the catalyst structure evaluation of Matsumoto with the trained predictive model of Cheng for the purpose of determining the most effective catalyst arrangement with the advantage of predicting the effects of the catalyst arrangement to better evaluate its usefulness.
Regarding Claim 19, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose wherein the trained model is a neural network model to be used for NNP (Neural Network Potential).
In the same field of endeavor, Ham teaches wherein the trained model is a neural network model (e.g. see [pg. 4 paragraph 11] “2 is a schematic diagram illustrating an FNN neural network as an artificial neural network according to Embodiment 1 of the present invention”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the catalyst information processing method of Matsumoto with the trained model of Ham for the purpose of determining which elementary reaction accomplishes the desired yield with the advantage of a trained model to enhance the accuracy and efficiency of the determination.
Matsumoto as modified by Ham does not explicitly disclose wherein the trained model is a neural network model to be used for NNP (Neural Network Potential).
In the same field of endeavor, Cheng teaches wherein the trained model is a neural network model to be used for NNP (Neural Network Potential) (e.g. see [pg. 10 paragraph 4] “establishing a first neural network structure feature-can be associated with the model. Specifically, the adsorption established based on palladium-based alloy surface structure characteristic of the energy prediction model”).
It would have been obvious to combine the trained model of Matsumoto as modified by Ham with the Neural network potential of Cheng for the purpose of optimizing a catalyst arrangement with the advantage of utilizing a model to evaluate the effects of different arrangements on target properties.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (JP2002210375 A) in view of Ham (KR20200096452 A) and in further view of Oshikiri (JP2008055302A).
Regarding Claim 7, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose set the arrangement of the promoter element to within 5A from a position of the adsorbed molecule.
In the same field of endeavor, Oshikiri teaches set the arrangement of the promoter element to within 5A from a position of the adsorbed molecule (e.g. see [pg. 3 paragraph 5] “The metal oxide catalyst of the invention 11 is characterized in that, in the invention 6, 7, 8, 9, or 10, the Co, Ni, Cu, or the like is located 3-10 angstroms away from the metal ion M .sup.5+ or M .sup.6+ contained therein”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the promoter arrangement of Matsumoto with the spacing of Oshikiri for the purpose and advantage of the promoter being in close proximity to the adsorbed molecule.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (JP2002210375 A) in view of Ham (KR20200096452 A) and in further view of Asep Sugih Nugraha, Guillaume Lambard, et al., Mesoporous trimetallic PtPdAu alloy films toward enhanced electrocatalytic activity in methanol oxidation: unexpected chemical compositions discovered by Bayesian optimization. J. Mater. Chem. A 2020; 8 (27): 13532–13540 (hereinafter “Asep”).
Regarding Claim 9, Matsumoto and Ham teach the limitations of Claim 1. Matsumoto does not explicitly disclose herein the one or more processors are configured to: optimize the arrangement of the plurality of promoter elements using at least one of Bayesian optimization and a random search.
In the same field of endeavor, Asep teaches herein the one or more processors are configured to: optimize the arrangement of the plurality of promoter elements using at least one of Bayesian optimization and a random search (e.g. see [pg. 1 last paragraph- pg. 2 first paragraph] “However, the possible compositions for ternary alloys are countless, making the exploratory search for the optimal compositions prohibitively experimentally expensive… therefore, we apply Bayesian optimization to suggest the synthetic conditions likely resulting in the alloys with higher electrocatalytic activity”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the optimization method of Matsumoto with the Bayesian optimization of Asep for the purpose of optimizing catalyst structure with the advantage of an inexpensive method to create catalyst alloys with higher electrocatalytic activity.
Allowable Subject Matter
Claims 10, 12, and 17-18 contain allowable subject matter. The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 10, None of the prior art discloses or renders obvious a device as claimed wherein “the one or more processors are configured to: arrange less than 10% of atoms in number except for an atom constituting the adsorbed molecule in an atomic structure input into the trained model, as the promoter elements.”
Regarding Claim 12, None of the prior art discloses or renders obvious a device as claimed comprising “optimiz[ing] a ratio of the plurality of types of promoter elements and the arrangement of each of the promoter elements.”
Regarding Claim 17, None of the prior art discloses or renders obvious a device as claimed comprising, “optimiz[ing] the arrangement of the promoter element lower in the physical property value than the other arrangements.”
Regarding Claim 18, the subject matter of claim 18 would be allowable based on its dependence on Claim 17.
Conclusion
Examiner notes while there are no prior art rejections for Claims 10,12, 17, and 18, Examiner is unable to comment on the allowability of the claims until the 35 U.S.C. 101 Rejections are addressed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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