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 .
Response to Amendment
Claims 1-25 are pending.
Claims 1-9 and 11-12, 14-15, and 17-25 are amended.
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.
Claim(s) 1, 3-8, 10-14, 16-19, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Wilmer (US 20130143768 A1) in view of Development of a General Evaluation Metric for Rapid Screening of Adsorbent Materials for Post combustion CO2 Capture, hence forth NPL1.
In claim 1, Wilmer discloses a computer-implemented method (Par. 9 “computer processor”) for post combustion carbon capture (par. 143 “synthesizing improved materials for applications such as carbon capture”), the computer-implemented method comprising: characterizing materials (Par. 106 “Hypothetical MOF structures generated” Par. 7 “A range of material properties can be predicted for these MOFs…”, “adsorption capability”) with a molecular model workflow (Par. 107-108 Table 1 “modeled” “optimizations”) that generates microscopic figures of merit for the materials by microscopic properties (Par. 111, 128 “methane adsorption isotherms are computationally predicted for the hypothetical MOFs” “nanoscopic” “microporous”, Par. 120 “screening the promising MOFs for high pressure methane storage”); evaluating the materials from the molecular model workflow with a process model workflow, wherein the evaluating of the materials comprises: generating, based on macroscopic figures of merit (Par. 106 “comparing coordinates of the atoms in the MOF structures against the coordinates of the atoms in the experimental and energetically optimized structures”) for process steps of a carbon recovery process (Par. 143); and ranking the materials for applicability as a sorbent material (Par. 131 “rank”) using a combined microscopic performance and macroscopic process feasibility generator (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that ranks the materials according to the microscopic figures of merit for the materials and the macroscopic figures of merit for the process steps (Fig. 11, Par. 131 “rank-ordered”) and performing a chemical separation process using the materials ranked for the applicability as the sorbent material (Par. 143 “subsequently synthesizing improved materials for applications such as carbon capture, hydrogen storage, and chemical separations”).
Wilmer does not explicitly disclose wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof.
NPL1 teaches wherein the evaluating of the materials comprises: simulating, using dynamic process simulation (Page 2 column 2, “a neural network model for 74 adsorbent materials to predict if an adsorbent material could achieve certain purity and recovery requirements/7 and very recently Rajendran and co-workers introduced a simplified process model based on a well-mixed batch adsorber and applied it to 75 adsorbents to identify those that can produce CO2 purity and recovery values meeting specified targets.”), process steps of a carbon recovery process (see title, Page 2, Columns 2, “recovery values”) based on process parameters until steady state of the process steps is reached (Page 4, Column 1, “until the cycle comes to cyclic steady state”); and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process (Page 1, abstract “macroscopic pressure swing adsorption (PSA) modeling”, Column 2 “perform macroscopic process simulation of a PSA process to rank materials based on the cost of CO2 capture”), and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Page 4, Column 2 “maximum CO2 purity that could be obtained while recovering 90% of the CO2. For those 190 MOFs that could achieve 90% CO2 purity, a second optimization was done to minimize the overall cost, with constraints of 90% CO2 purity and 90% recovery of CO2” (emphasis added), Fig. 2 “Minimum energy required”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was made to have wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof, based on the teachings of NPL1 in the material evaluation of Wilmer as analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics (NPL1 page 1 abstract), thus improving the accuracy of the calculations.
In claim 3, Wilmer discloses wherein the microscopic figures of merit include data from an adsorption isotherm for a particular sorbent material (Par. 126 “a complete isotherm was calculated (over a wide range of pressures) for the four MOFs”).
In claim 4, Wilmer discloses wherein the carbon recovery process employs at least one of temperature swing adsorption cycle processes (Par. 149) and pressure swing adsorption cycle processes (Par. 169).
In claim 5, Wilmer discloses selecting highest ranked materials from the ranking for integration as the sorbent material (Par. 131, 134 “best to worst”, “identifying promising candidates”) into at least one of a desorber and an adsorber (Par. 57 “storing and releasing”) of a carbon capture process (Par. 7 “carbon dioxide adsorption capability”) employing at least one of pressure swing adsorption cycles (Par. 169) and temperature swing adsorption cycles (Par. 149); and performing carbon recapture using the at least one of the desorber and adsorber with the sorbent material (Par. 143).
In claim 6, Wilmer discloses wherein the sorbent material is selected from a group consisting of zeolites, metal organic frameworks (MOF), zeolitic imidazolate frameworks (ZIF), porous polymer networks (PPN) and combinations thereof (Par. 3-4, 36, and 124).
In claim 7, Wilmer discloses wherein a combined microscopic performance and macroscopic process feasibility generator comprises of a multi-step and multi-criteria optimizer (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that orders the materials using combined trade-off metrics considering different dimensions of performance enhancement (“allow the user to compare the potential MOFs in order to decide which MOFs to produce”).
In claim 8, Wilmer discloses a system for post combustion carbon capture (Par. 143 “applications such as carbon capture”), the system comprising: a hardware processor (Par. 9 “processor”); and a memory that stores a computer program product (Par. 9 “memory”), which, when executed by the hardware processor, causes the hardware processor to: rank materials for post combustion carbon capture (par. 143 “synthesizing improved materials for applications such as carbon capture”) comprising: characterize materials (Par. 106 “Hypothetical MOF structures generated” Par. 7 “A range of material properties can be predicted for these MOFs…”, “adsorption capability”) with a molecular model workflow (Par. 107-108 Table 1 “modeled” “optimizations”) generating microscopic figures of merit for the materials by microscopic properties (Par. 111, 128 “methane adsorption isotherms are computationally predicted for the hypothetical MOFs” “nanoscopic” “microporous”, Par. 120 “screening the promising MOFs for high pressure methane storage”); evaluate the materials from the molecular model workflow with a process model workflow that generates macroscopic figures of merit (Par. 106 “comparing coordinates of the atoms in the MOF structures against the coordinates of the atoms in the experimental and energetically optimized structures”) for process steps of a carbon recovery process (Par. 143); and rank the materials for applicability as a sorbent material (Par. 131 “rank”) using a combined microscopic performance and macroscopic process feasibility generator (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that ranks the materials according to the microscopic figures of merit for the materials and the macroscopic figures of merit for the process steps (Fig. 11, Par. 131 “rank-ordered”) and performing a chemical separation process using the materials ranked for the applicability as the sorbent material (Par. 143 “subsequently synthesizing improved materials for applications such as carbon capture, hydrogen storage, and chemical separations”).
Wilmer does not explicitly disclose wherein the evaluation of the materials comprises: simulation, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Emphasis added).
NPL1 teaches wherein the evaluating of the materials comprises: simulation, using dynamic process simulation (Page 2 column 2, “a neural network model for 74 adsorbent materials to predict if an adsorbent material could achieve certain purity and recovery requirements/7 and very recently Rajendran and co-workers introduced a simplified process model based on a well-mixed batch adsorber and applied it to 75 adsorbents to identify those that can produce CO2 purity and recovery values meeting specified targets.”), process steps of a carbon recovery process (see title, Page 2, Columns 2, “recovery values”) based on process parameters until steady state of the process steps is reached (Page 4, Column 1, “until the cycle comes to cyclic steady state”); and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process (Page 1, abstract “macroscopic pressure swing adsorption (PSA) modeling”, Column 2 “perform macroscopic process simulation of a PSA process to rank materials based on the cost of CO2 capture”), and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Page 4, Column 2 “maximum CO2 purity that could be obtained while recovering 90% of the CO2. For those 190 MOFs that could achieve 90% CO2 purity, a second optimization was done to minimize the overall cost, with constraints of 90% CO2 purity and 90% recovery of CO2” (emphasis added), Fig. 2 “Minimum energy required”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was made to have wherein the evaluation of the materials comprises: simulation, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof, based on the teachings of NPL1 in the material evaluation of Wilmer as analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics (NPL1 page 1 abstract), thus improving the accuracy of the calculations.
In claim 10, Wilmer discloses wherein the microscopic figures of merit include data from an adsorption isotherm for a particular sorbent material (Par. 126 “a complete isotherm was calculated (over a wide range of pressures) for the four MOFs”).
In claim 11, Wilmer discloses wherein the macroscopic figures of merit for the carbon recovery process employing at least one of temperature swing adsorption cycle processes (Par. 149) and pressure swing adsorption cycle processes (Par. 169) are selected from the group consisting of recovery, purity, production, specific energy and combinations thereof for a product stream of interest (Par. 144 “purity” Par. 57 “release” Par. 179 “volume” Par. 109 “energetic minimum or at a reduced energy”).
In claim 12, Wilmer discloses selecting highest ranked materials from the ranking for integration as sorbent materials (Par. 131, 134 “best to worst”, “identifying promising candidates”) into at least one of a desorber and an adsorber (Par. 57 “storing and releasing”) of a carbon capture process (Par. 7 “carbon dioxide adsorption capability”) employing at least one of pressure swing adsorption cycles (Par. 169) and temperature swing adsorption cycles (Par. 149); and performing carbon recapture using the at least one of the desorber and adsorber with the sorbent materials (Par. 143).
In claim 13, Wilmer discloses wherein a combined microscopic performance and macroscopic process feasibility generator comprises of a multi-step and multi-criteria optimizer (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that orders the materials using combined trade-off metrics considering different dimensions of performance enhancement (“allow the user to compare the potential MOFs in order to decide which MOFs to produce”).
In claim 14, Wilmer discloses a computer program (Par. 9) product post combustion carbon capture (par. 143 “synthesizing improved materials for applications such as carbon capture”) comprising a computer readable storage medium (Par. 9 “memory”) having computer readable program code embodied therewith, the program code executable by a processor to cause the processor to (Par. 9 “processor”): characterize, using the processor, materials (Par. 106 “Hypothetical MOF structures generated” Par. 7 “A range of material properties can be predicted for these MOFs…”, “adsorption capability”) with a molecular model workflow (Par. 107-108 Table 1 “modeled” “optimizations”) that generates microscopic figures of merit for the materials by microscopic properties (Par. 111, 128 “methane adsorption isotherms are computationally predicted for the hypothetical MOFs” “nanoscopic” “microporous”, Par. 120 “screening the promising MOFs for high pressure methane storage”); evaluate, using the processor, the materials from the molecular model workflow with a process model workflow that generates macroscopic figures of merit (Par. 106 “comparing coordinates of the atoms in the MOF structures against the coordinates of the atoms in the experimental and energetically optimized structures”) for process steps of a carbon recovery process (Par. 143); and rank, using the processor, the materials for applicability as a sorbent material (Par. 131 “rank”) using a combined microscopic performance and macroscopic process feasibility generator (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that ranks the materials according to the microscopic figures of merit for the materials and the macroscopic figures of merit for the process steps (Fig. 11, Par. 131 “rank-ordered”) and perform a chemical separation process using the materials ranked for the applicability as the sorbent material (Par. 143 “subsequently synthesizing improved materials for applications such as carbon capture, hydrogen storage, and chemical separations”).
Wilmer does not explicitly disclose wherein the evaluation of the materials comprises: simulation, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Emphasis added).
NPL1 teaches wherein the evaluating of the materials comprises: simulation, using dynamic process simulation (Page 2 column 2, “a neural network model for 74 adsorbent materials to predict if an adsorbent material could achieve certain purity and recovery requirements/7 and very recently Rajendran and co-workers introduced a simplified process model based on a well-mixed batch adsorber and applied it to 75 adsorbents to identify those that can produce CO2 purity and recovery values meeting specified targets.”), process steps of a carbon recovery process (see title, Page 2, Columns 2, “recovery values”) based on process parameters until steady state of the process steps is reached (Page 4, Column 1, “until the cycle comes to cyclic steady state”); and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process (Page 1, abstract “macroscopic pressure swing adsorption (PSA) modeling”, Column 2 “perform macroscopic process simulation of a PSA process to rank materials based on the cost of CO2 capture”), and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Page 4, Column 2 “maximum CO2 purity that could be obtained while recovering 90% of the CO2. For those 190 MOFs that could achieve 90% CO2 purity, a second optimization was done to minimize the overall cost, with constraints of 90% CO2 purity and 90% recovery of CO2” (emphasis added), Fig. 2 “Minimum energy required”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was made to have wherein the evaluation of the materials comprises: simulation, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulation, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof, based on the teachings of NPL1 in the material evaluation of Wilmer as analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics (NPL1 page 1 abstract), thus improving the accuracy of the calculations.
In claim 16, Wilmer discloses wherein the microscopic figures of merit include data from an adsorption isotherm for a particular sorbent material (Par. 126 “a complete isotherm was calculated (over a wide range of pressures) for the four MOFs”).
In claim 17, Wilmer discloses wherein the carbon recovery process employs at least one of temperature swing adsorption cycle processes (Par. 149) and pressure swing adsorption cycle processes (Par. 169).
In claim 18, Wilmer discloses selection, using the processor, of highest ranked materials from the ranks for integration as the sorbent material (Par. 131, 134 “best to worst”, “identifying promising candidates”) into at least one of a desorber and an adsorber (Par. 57 “storing and releasing”) of a carbon capture process (Par. 7 “carbon dioxide adsorption capability”) employing at least one of pressure swing adsorption cycles (Par. 169) and temperature swing adsorption cycles (Par. 149); and execution carbon recapture using the at least one of the desorber and the adsorber with the sorbent material (Par. 143).
In claim 19, Wilmer discloses a computer-implemented method for separation process implementation, the computer-implemented method comprising: characterizing materials (Par. 106 “Hypothetical MOF structures generated” Par. 7 “A range of material properties can be predicted for these MOFs…”, “adsorption capability”) with a molecular model workflow (Par. 107-108 Table 1 “modeled” “optimizations”) that generates microscopic figures of merit for the materials by microscopic properties (Par. 111, 128 “methane adsorption isotherms are computationally predicted for the hypothetical MOFs” “nanoscopic” “microporous”, Par. 120 “screening the promising MOFs for high pressure methane storage”); evaluating the materials from the molecular model workflow with a process model workflow, generating, macroscopic figures of merit (Par. 106 “comparing coordinates of the atoms in the MOF structures against the coordinates of the atoms in the experimental and energetically optimized structures”) for the chemical separation process steps of the carbon recovery process (Par. 143); and ranking the materials for applicability as a sorbent material (Par. 131 “rank”) using a combined microscopic performance and macroscopic process feasibility generator (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that ranks the materials according to the microscopic figures of merit for the materials and the macroscopic figures of merit for the chemical separation process steps (Fig. 11, Par. 131 “rank-ordered”); selecting highest ranked materials from the ranking for integration as sorbent material (Par. 131, 134 “best to worst”, “identifying promising candidates”) into at least one of a desorber and an adsorber (Par. 57 “storing and releasing”) of a chemical separation process (Par. 7 “carbon dioxide adsorption capability”) employing at least one of pressure swing adsorption cycles (Par. 169) and temperature swing adsorption cycles (Par. 149); and performing the chemical separation process using the at least one of the desorber and the adsorber with the sorbent material (Par. 143 “subsequently synthesizing improved materials for applications such as carbon capture, hydrogen storage, and chemical separations”).
Wilmer does not explicitly disclose wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, chemical separation process steps of a carbon recovery process based on process parameters until steady state of the chemical separation process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the chemical separation process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (emphasis added).
NPL1 teaches wherein the evaluating of the materials comprises: simulating, using dynamic process simulation (Page 2 column 2, “a neural network model for 74 adsorbent materials to predict if an adsorbent material could achieve certain purity and recovery requirements/7 and very recently Rajendran and co-workers introduced a simplified process model based on a well-mixed batch adsorber and applied it to 75 adsorbents to identify those that can produce CO2 purity and recovery values meeting specified targets.”), chemical separation process steps of a carbon recovery process (see title, Page 2, Columns 2, “recovery values”) based on process parameters until steady state of the chemical separation process steps is reached (Page 4, Column 1, “until the cycle comes to cyclic steady state”); and generating, based on the simulating, macroscopic figures of merit for the chemical separation process steps of the carbon recovery process (Page 1, abstract “macroscopic pressure swing adsorption (PSA) modeling”, Column 2 “perform macroscopic process simulation of a PSA process to rank materials based on the cost of CO2 capture”), and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Page 4, Column 2 “maximum CO2 purity that could be obtained while recovering 90% of the CO2. For those 190 MOFs that could achieve 90% CO2 purity, a second optimization was done to minimize the overall cost, with constraints of 90% CO2 purity and 90% recovery of CO2” (emphasis added), Fig. 2 “Minimum energy required”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was made to have wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, process steps of a carbon recovery process based on chemical separation process parameters until steady state of the chemical separation process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the chemical separation process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof, based on the teachings of NPL1 in the material evaluation of Wilmer as analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics (NPL1 page 1 abstract), thus improving the accuracy of the calculations.
In claim 21, Wilmer discloses wherein the microscopic figures of merit include data from an adsorption isotherm for a particular sorbent material (Par. 126 “a complete isotherm was calculated (over a wide range of pressures) for the four MOFs”).
In claim 22, Wilmer discloses wherein the macroscopic figures of merit for the chemical separation process employing at least one of temperature swing adsorption cycle processes (Par. 149) and pressure swing adsorption cycle processes (Par. 169) are selected from a group consisting of recovery, purity, production, specific energy and combinations thereof for a product stream of interest (Par. 144 “purity” Par. 57 “release” Par. 179 “volume” Par. 109 “energetic minimum or at a reduced energy”).
In claim 23, Wilmer discloses wherein the sorbent material is selected from a group consisting of zeolites, metal organic frameworks (MOF), zeolitic imidazolate frameworks (ZIF), porous polymer networks (PPN) and combinations thereof (Par. 3-4, 36, and 124).
In claim 24, Wilmer discloses wherein the chemical separation process is selected from a group consisting of carbon recovery, carbon capture, air separation, natural gas separation, hydrogen purification, ammonia separation, N2 purification, 02 purification, H20 removal, bio gas separation and combinations thereof (Par. 110, 124, 151, and 158).
In claim 25, Wilmer discloses a computer-implemented method (Par. 9 “computer processor”) for ranking materials for post combustion carbon capture (par. 143 “synthesizing improved materials for applications such as carbon capture”), the computer-implemented method comprising: characterizing materials (Par. 106 “Hypothetical MOF structures generated” Par. 7 “A range of material properties can be predicted for these MOFs…”, “adsorption capability”) with a molecular model workflow (Par. 107-108 Table 1 “modeled” “optimizations”) that generates microscopic figures of merit for materials by microscopic properties (Par. 111, 128 “methane adsorption isotherms are computationally predicted for the hypothetical MOFs” “nanoscopic” “microporous”, Par. 120 “screening the promising MOFs for high pressure methane storage”); evaluating the materials from the molecular model workflow with a process model workflow generating macroscopic figures of merit (Par. 106 “comparing coordinates of the atoms in the MOF structures against the coordinates of the atoms in the experimental and energetically optimized structures”) for process steps of the carbon recovery process (Par. 143); and ranking the materials for applicability as a sorbent material (Par. 131 “rank”) using a combined microscopic performance and macroscopic process feasibility generator (Par. 115-116 “MOFs that can be created after attempting various combinations of the building blocks to identify those MOFs that are feasible”) that ranks the materials according to the microscopic figures of merit for the materials and the macroscopic figures of merit for the process steps (Fig. 11, Par. 131 “rank-ordered”) and performing a chemical separation process using the materials ranked for the applicability as the sorbent material (Par. 143 “subsequently synthesizing improved materials for applications such as carbon capture, hydrogen storage, and chemical separations”).
Wilmer does not explicitly disclose wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof.
NPL1 teaches wherein the evaluating of the materials comprises: simulating, using dynamic process simulation (Page 2 column 2, “a neural network model for 74 adsorbent materials to predict if an adsorbent material could achieve certain purity and recovery requirements/7 and very recently Rajendran and co-workers introduced a simplified process model based on a well-mixed batch adsorber and applied it to 75 adsorbents to identify those that can produce CO2 purity and recovery values meeting specified targets.”), process steps of a carbon recovery process (see title, Page 2, Columns 2, “recovery values”) based on process parameters until steady state of the process steps is reached (Page 4, Column 1, “until the cycle comes to cyclic steady state”); and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process (Page 1, abstract “macroscopic pressure swing adsorption (PSA) modeling”, Column 2 “perform macroscopic process simulation of a PSA process to rank materials based on the cost of CO2 capture”), and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof (Page 4, Column 2 “maximum CO2 purity that could be obtained while recovering 90% of the CO2. For those 190 MOFs that could achieve 90% CO2 purity, a second optimization was done to minimize the overall cost, with constraints of 90% CO2 purity and 90% recovery of CO2” (emphasis added), Fig. 2 “Minimum energy required”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was made to have wherein the evaluating of the materials comprises: simulating, using dynamic process simulation, process steps of a carbon recovery process based on process parameters until steady state of the process steps is reached; and generating, based on the simulating, macroscopic figures of merit for the process steps of the carbon recovery process, and the macroscopic figures of merit are selected from the group consisting of CO2 recovery, CO2 purity, a productivity associated with CO2, specific energy consumption associated with CO2, and combinations thereof, based on the teachings of NPL1 in the material evaluation of Wilmer as analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics (NPL1 page 1 abstract), thus improving the accuracy of the calculations.
Claim(s) 2, 9, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wilmer in view of NPL1 and in further view of Steingrimsson (US 20200257933 A1) hence forth Stein.
In claim 2, Wilmer discloses wherein the microscopic properties are selected from the group consisting of loading, heat transfer and combinations thereof (Par. 111 “absorption”, “heat of adsorption”).
Wilmer does not explclity disclose the group consisting of heat capacity.
Stein teaches the group consisting of heat capacity (Par. 21 Table 1 “heat capacity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to have the group consisting of heat capacity based on the teaching of Stein to the group in Wilmer as the parameter that impacts the ultimate quality of the finished part (Stein Par. 21) thus improving the modeling and therefore accuracy of Wilmer.
In claim 9, Wilmer discloses wherein the microscopic properties are selected from the group consisting of loading, heat transfer and combinations thereof (Par. 111 “absorption”, “heat of adsorption”).
Wilmer does not explicitly disclose the group consisting of heat capacity.
Stein teaches the group consisting of heat capacity (Par. 21 Table 1 “heat capacity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to have the group consisting of heat capacity based on the teaching of Stein to the group in Wilmer as the parameter that impacts the ultimate quality of the finished part (Stein Par. 21) thus improving the modeling and therefore accuracy of Wilmer.
In claim 15, Wilmer discloses wherein the microscopic properties are selected from the group consisting of loading, heat transfer and combinations thereof (Par. 111 “absorption”, “heat of adsorption”).
Wilmer does not explicitly disclose the group consisting of heat capacity.
Stein teaches the group consisting of heat capacity (Par. 21 Table 1 “heat capacity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to have the group consisting of heat capacity based on the teaching of Stein to the group in Wilmer as the parameter that impacts the ultimate quality of the finished part (Stein Par. 21) thus improving the modeling and therefore accuracy of Wilmer.
In claim 20, Wilmer discloses wherein the microscopic properties are selected from the group consisting of loading, heat transfer and combinations thereof (Par. 111 “absorption”, “heat of adsorption”).
Wilmer does not explclity disclose a group consisting of heat capacity.
Stein teaches the group consisting of heat capacity (Par. 21 Table 1 “heat capacity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to have the group consisting of heat capacity based on the teaching of Stein to the group in Wilmer as the parameter that impacts the ultimate quality of the finished part (Stein Par. 21) thus improving the modeling and therefore accuracy of Wilmer.
Response to Arguments
Applicant's arguments filed 04/13/2026 have been fully considered but they are not persuasive. Regarding applicant’s 102 arguments, they are considered moot in light of the new 103 rejection of the amended claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20130209869 A1, Hybrid Energy Storage Devices Including Support Filaments; and US 20170368533 A1 SEPARATION MATERIAL.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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