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 Claims
Claims 1-34 are currently pending and under exam herein.
Claims 1-34 are rejected.
Priority
The instant application claims priority from foreign application CN 202010533480.3 filed on 6/12/2020. Thus, the effective filing date of the instant application is 6/12/2020.
Drawings
The Drawings filed on 10/17/2022 were considered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/17/2022, 9/06/2023, 10/13/2023, 2/05/2024, and 3/07/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1, 2, 4, 5, 9-17, 26-31, 33, 34 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of Claims 1, 2, 6-8, 14-19, 22-27, 29-30 of copending Application No. 18/047,424 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented. The overlapping claims are shown below. The instant application claims are italicized.
Claim 1 of the instant application cites a method for real-time optimization of a molecular level device, wherein the method comprises: (an optimization method for a whole process of molecular-level oil refinery processing, wherein the optimization method comprising - claim 1 of application 18047424) acquiring molecular composition of crude oil (acquiring molecular composition of crude oil - claim 1 of application 18047424); acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil (acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil - claim 1 of application 18047424); respectively inputting, according to a preset feedstock ratio, the corresponding fractions into a pre-trained product prediction model corresponding to a petroleum processing device as petroleum processing feedstocks, to obtain predicted molecular composition of a corresponding predicted product output by the pre-trained product prediction model and predicted molecular content of each single molecule in the predicted molecular composition (respectively inputting, according to a preset feedstock ratio, the corresponding fractions into a product prediction model of a respective petroleum processing device as petroleum processing feedstocks, to obtain molecular composition of a corresponding predicted product and content of each single molecule in the predicted product - claim 1 of application 18047424); acquiring a preset standard set for a preset target product (acquiring a target parameter according to all mixed products - claim 1 of application 18047424); determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition( determining whether the product property of each of the mixed products meets any preset standard in a preset standard set - claim 1 of application 18047424); and if the predicted product does not meet any preset standard for a target product corresponding to the predicted product in the preset standard set, (and if the target parameter does not meet the preset condition, - claim 1 of application 18047424) adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, (adjusting the preset feedstock ratio, a parameter in the product prediction model and a preset rule in the preset rule set, to re-obtain a plurality of mixed products - claim 1 of application 18047424) until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set. (until the product property of each of the mixed products meets any preset standard in the preset standard set and the target parameter meets the preset condition. - claim 1 of application 18047424)
Claim 4 of the instant application adds the limitation of wherein the method further comprises: blending each of the predicted products which is used as a product blending feedstock according to a preset rule set, to obtain molecular composition of a plurality of mixed products and content of each single molecule in each of the mixed products; (blending each of the predicted products which is used as a product blending feedstock according to a preset rule set, to obtain molecular composition of a plurality of mixed products and content of each single molecule in each of the mixed products - claim 1 of application 18047424) and respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products. (respectively calculating a product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products - claim 1 of application 18047424)
Claim 5 of the instant application recites wherein the determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in each of the preset standard set comprises: determining whether the product property of each of the mixed products meets a preset product property of a target mixed product obtained by blending corresponding each target product in the preset standard set;(and determining whether the product property of each of the mixed products meets any preset standard in a preset standard set; - claim 1 of application 18047424) if the preset product property is met, obtaining a target parameter according to all of the mixed products and determining whether the target parameter meets a preset condition( if the product property of each of the mixed products meets any preset standard in the preset standard set, acquiring a target parameter according to all mixed products and determining whether the target parameter meets a preset condition - claim 1 of application 18047424); if the target parameter meets the preset condition, determining that the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set, and outputting the preset feedstock ratio, the pre-trained product prediction model and the preset rule set as a production and processing scheme; and if the target parameter does not meet the preset condition, adjusting the operation parameter in the pre-trained product prediction model and a preset rule in the preset rule set, to re-obtain a plurality of mixed products, until the product property of each of the mixed products meets the preset product property and the target parameters in all of the mixed products meet the preset condition (and if the target parameter does not meet the preset condition, adjusting the preset feedstock ratio, a parameter in the product prediction model and a preset rule in the preset rule set, to re-obtain a plurality of mixed products until the product property of each of the mixed products meets any preset standard in the preset standard set and the target parameter meets the preset condition - claim 1 of application 18047424)
Claim 2 of the instant application recites the method according to claim 1, wherein the method further comprises: (optimization method according to claim 1, wherein the optimization method further comprises: - claim 2 of application 18047424) acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices; (acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices - claim 2 of application 18047424) determining whether each of the input flows meets a preset input flow range of the respective petroleum processing device; (determining whether each of the input flows meets a preset input flow range of the respective petroleum processing device - claim 2 of application 18047424) and adjusting the preset feedstock ratio if any one of the input flows does not meet the preset input flow range of the respective petroleum processing device, ( adjusting the preset feedstock ratio if any one of the input flows does not meet the preset input flow range of the respective petroleum processing device, - claim 2 of application 18047424) and respectively re-inputting, according to the adjusted preset feedstock ratio, the corresponding fractions into the pre-trained product prediction model of the respective petroleum processing device as petroleum processing feedstocks, (respectively re-inputting, according to the adjusted preset feedstock ratio, the corresponding fractions into the product prediction model of the respective petroleum processing device as petroleum processing feedstocks - claim 2 of application 18047424) until each of the input flows meets the preset input flow range of the respective petroleum processing device. (until each of the input flows meets the preset input flow range of the respective petroleum processing device - claim 2 of application 18047424)
Claim 9 of the instant application recites wherein the respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products comprises (wherein the respectively calculating a product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products comprises:- claim 6 of application 18047424): acquiring first molecular composition of each group of the product blending feedstocks and first component content of each single molecule in each group of the product blending feedstocks(acquiring first molecular composition of each group of the product blending feedstocks and first component content of each single molecule in each group of the product blending feedstocks - claim 6 of application 18047424); based on the preset rule set, obtaining second molecular composition of each of mixed products and second component content of each single molecule in each of mixed products according to the first molecular composition of each group of the product blending feedstocks and the first component content of each single molecule in each group of the product blending feedstocks( based on the preset rule set, obtaining second molecular composition of each of mixed products and second component content of each single molecule in each of mixed products according to the first molecular composition of each group of the product blending feedstock and the first component content of each single molecule in each group of the product blending feedstocks- claim 6 of application 18047424); calculating a physical property of each single molecule in each of the mixed products according to the number of groups of each group contained in each single molecule in each of the mixed products and a contribution value of each group to the physical property (calculating a physical property of each single molecule in each of the mixed products according to the number of groups of each group contained in each single molecule in each of the mixed products and a contribution value of each group to the physical property - claim 6 of application 18047424); and calculating a product property of each of the mixed products according to the physical property and the second component content of each single molecule in each of the mixed products (calculating a product property of each of the mixed products according to the physical property and the second component content of each single molecule in each of the mixed products - claim 6 of application 18047424)
Claim 10 of the instant application recites wherein calculation of the physical property of each single molecule comprises (wherein calculation of the physical property of each single molecule comprises- claim 7 of application 18047424): for each single molecule, acquiring the number of groups of each group constituting the single molecule and a contribution value of each group to the physical property (for each single molecule, acquiring the number of groups of each group constituting the single molecule and a contribution value of each group to the physical property- claim 7 of application 18047424); and inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, to acquire the physical property of the single molecule output by the pre-trained property calculation model ( inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, to acquire the physical property of the single molecule outputted by the pre-trained property calculation model. - claim 7 of application 18047424); wherein the pre-trained property calculation model is used to calculate the physical property of the single molecule according to the number of groups of each group contained in a single molecule and a contribution value of each group to the physical property (This is just a particular target of prediction covered by claim 7 of application 18047424)
Claim 11 of the instant application recites wherein, before the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, the method further comprises (wherein, before the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, the optimization method further comprises: - claim 8 of application 18047424): comparing the number of groups of each group constituting the single molecule with molecular information of a template single molecule with known physical properties pre-stored in a database, the molecular information comprising the number of groups of each group constituting the template single molecule; (comparing the number of groups of each group constituting the single molecule with molecular information of a template single molecule with known physical properties pre-stored in a database, the molecular information comprising the number of groups of each group constituting the template single molecule - claim 8 of application 18047424) determining whether there is a same template single molecule as the single molecule (determining whether there is a same template single molecule as the single molecule - claim 8 of application 18047424); if there is a same template single molecule as the single molecule, outputting the physical properties of the template single molecule as a physical property of the single molecule ( if there is a same template single molecule as the single molecule, outputting the physical properties of the template single molecule as a physical property of the single molecule - claim 8 of application 18047424); and if there is not a same template single molecule as the single molecule, then performing the step of the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model (and if there is not a same template single molecule as the single molecule, then performing the step of the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model. - claim 8 of application 18047424)
Claim 12 of the instant application recites wherein a step of training the property calculation model comprises (wherein a step of training the property calculation model comprises: - claim 14 of application 18047424): constructing a property calculation model of a single molecule (constructing a property calculation model of a single molecule - claim 14 of application 18047424); acquiring the number of groups of each group constituting a sample single molecule (acquiring the number of groups of each group constituting a sample single molecule - claim 14 of application 18047424); wherein the physical property of the sample single molecule is known (wherein the physical property of the sample single molecule is known - claim 14 of application 18047424); inputting the number of groups of each group constituting the sample single molecule into the property calculation model; (inputting the number of groups of each group constituting the sample single molecule into the property calculation model - claim 14 of application 18047424) acquiring a predicted physical property of the sample single molecule output by the property calculation model (acquiring a predicted physical property of the sample single molecule outputted by the property calculation model - claim 14 of application 18047424); if a deviation value between the predicted physical property and the physical property which is known is less than a preset deviation threshold, determining that the property calculation model converges, acquiring a contribution value of each group to the physical property in the property calculation model which is converged, and storing the contribution value of the group to the physical property (if a deviation value between the predicted physical property and the physical property which is known is less than a preset deviation threshold, determining that the property calculation model converges, acquiring a contribution value corresponding to each group in the property calculation model which is converged, and storing the contribution value as a contribution value of the group to the physical property - claim 14 of application 18047424); and if the deviation value between the predicted physical property and the physical property which is known is greater than or equal to the deviation threshold, adjusting a contribution value of each group to the physical property in the property calculation model until the property calculation model converges. (and if the deviation value between the predicted physical property and the physical property which is known is greater than or equal to the deviation threshold, adjusting a contribution value corresponding to each group in the property calculation model until the property calculation model converges - claim 14 of application 18047424)
Claim 13 of the instant application recites wherein the property calculation model is established as shown below: f=a+Σni Δf i; where, f is the physical property of the single molecule, ni is the number of groups of the i-th group in the single molecule, Δfi is the contribution value of the i-th group in the single molecule to the physical property, and a is an associated constant (wherein the property calculation model is established as shown below: f=a+Σni Δf i; where, f is the physical property of the single molecule, ni is the number of groups of the i-th group in the single molecule, Δfi is the contribution value of the i-th group in the single molecule to the physical property, and a is an associated constant - claim 15 of application 18047424)
Claim 14 of the instant application recites wherein the acquiring the number of groups of each group constituting a sample single molecule comprises (wherein the acquiring the number of groups of each group constituting a sample single molecule comprises: - claim 16 of application 18047424): determining a primary group, the number of groups of the primary group, a multi-stage group, and the number of groups of the multi-stage group in all groups of the single molecule (determining a primary group, the number of groups of the primary group, a multi- stage group, and the number of groups of the multi-stage group in all groups of the single molecule - claim 16 of application 18047424); taking all groups constituting the single molecule as the primary group (taking all groups constituting the single molecule as the primary group; - claim 16 of application 18047424); and taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group(and taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group. - claim 16 of application 18047424)
Claim 15 of the instant application recites wherein, the property calculation model is established as shown below:
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where, f is the physical property of the single molecule, m1i is the number of groups of the i-th group in the primary group, Δf1i is the contribution value of the i-th group in the primary group to the physical property, m2j is the number of groups of the j-th group in a secondary group, Δf2j is the contribution value of the j-th group in the secondary group to the physical property, mNI is the number of groups of the l-th group in an N-stage group, ΔfNI is the contribution value of the l-th group in the N-stage group to the physical property, a is an associated constant, and N is a positive integer greater than or equal to 2 (Property calculation model is established as shown below:
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where, f is the physical property of the single molecule, m1i is the number of groups of the i-th group in the primary group, Δf1i is the contribution value of the i-th group in the primary group to the physical property, m2j is the number of groups of the j-th group in a secondary group, Δf2j is the contribution value of the j-th group in the secondary group to the physical property, mNl is the number of groups of the l-th group in an N-stage group, ΔfNl is the contribution value of the l-th group in the N-stage group to the physical property, a is an associated constant, and Nis a positive integer greater than or equal to 2. - claim 17 of application 18047424)
Claim 16 of the instant application recites wherein the acquiring the number of groups of each group constituting the single molecule comprises(wherein the acquiring the number of groups of each group constituting the single molecule comprises - claim 18 of application 18047424): determining a primary group, the number of groups of the primary group, a multi-stage group, and the number of groups of the multi-stage group in all groups of the single molecule(determining a primary group, the number of groups of the primary group, a multi- stage group, and the number of groups of the multi-stage group in all groups of the single molecule - claim 18 of application 18047424); taking all groups constituting the single molecule as the primary group (taking all groups constituting the single molecule as the primary group - claim 18 of application 18047424); and taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group (taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group - claim 18 of application 18047424)
Claim 17 of the instant application recites wherein, the physical property of the single molecule comprises a boiling point of a single molecule; the calculating the physical property of the single molecule comprises: calculating the boiling point of the single molecule according to a property calculation model as follows:
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where, T is the boiling point of the single molecule, SOL is a single molecule vector converted according to the number of groups of each group constituting the single molecule, GROUP11 is a first contribution value vector converted according to a contribution value of the primary group to the boiling point, GROUP12 is a second contribution value vector converted according to a contribution value of the secondary group to the boiling point, GROUPIN is an N-th contribution value vector converted according to a contribution value of the N-stage group to the boiling point, Numh is the number of atoms other than the hydrogen atom in the single molecule, d is a first preset constant, b is a second preset constant, c is a third preset constant, and N is a positive integer greater than or equal to 2. (wherein, the physical property of the single molecule comprises a boiling point of a single molecule;
the calculating the physical property of the single molecule comprises: calculating the boiling point of the single molecule according to a property calculation model as follows:
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where, T is the boiling point of the single molecule, SOL is a single molecule vector converted according to the number of groups of each group constituting the single molecule, GROUP11 is a first contribution value vector converted according to a contribution value of the primary group to the boiling point, GROUP12 is a second contribution value vector converted according to a contribution value of the secondary group to the boiling point, GROUPIN is an N-th contribution value vector converted according to a contribution value of the N-stage group to the boiling point, Numh is the number of atoms other than the hydrogen atom in the single molecule, d is a first preset constant, b is a second preset constant, c is a third preset constant, and N is a positive integer greater than or equal to 2. - claim 19 of application 18047424)
Claim 26 of the instant application recites wherein, a step of training the product prediction model comprises (wherein, a step of training the product prediction model comprises - claim 22 of application 18047424): establishing a product prediction model (establishing a product prediction model; - claim 22 of application 18047424); wherein the product prediction model comprises (wherein the product prediction model comprises - claim 22 of application 18047424): a set of reaction rules comprising a plurality of reaction rules and a reaction rate algorithm (a set of reaction rules comprising a plurality of reaction rules and a reaction rate algorithm; - claim 22 of application 18047424); acquiring sample feedstock information for a sample feedstock (acquiring sample feedstock information for a sample feedstock - claim 22 of application 18047424); training the set of reaction rules by using the sample feedstock information, and fixing the set of reaction rules that has been trained (training the set of reaction rules by using the sample feedstock information, and fixing the set of reaction rules that has been trained - claim 22 of application 18047424); and training the reaction rate algorithm by using the sample feedstock information, and fixing the reaction rate algorithm that has been trained, to obtain the product prediction model that has been trained (training the reaction rate algorithm by using the sample feedstock information, and fixing the reaction rate algorithm that has been trained, to obtain the product prediction model that has been trained. - claim 22 of application 18047424)
Claim 27 of the instant application recites wherein the sample feedstock information of the sample feedstock comprises: molecular composition of the sample feedstock, molecular content of each molecule in the sample feedstock, molecular composition of an actual product corresponding to the sample feedstock, and actual content of each molecule in the actual product. (wherein the sample feedstock information of the sample feedstock comprises: molecular composition of the sample feedstock, molecular content of each molecule in the sample feedstock, molecular composition of an actual product corresponding to the sample feedstock, and actual content of each molecule in the actual product. - claim 23 of application 18047424)
Claim 28 of the instant application recites wherein the training the set of reaction rules by using the sample feedstock information comprises: processing the molecular composition of the sample feedstock according to a preset set of reaction rules, to obtain a reaction pathway corresponding to each molecule in the molecular composition of the sample feedstock; obtaining first molecule composition of a device output product comprising the sample feedstock, an intermediate product, and a predicted product according to the reaction path corresponding to each molecule in the molecular composition of the sample feedstock; in the device output product, comprising: the sample feedstock, the intermediate product, and the predicted product; calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the set of reaction rules; and if the first relative deviation does not meet the preset condition, adjusting a reaction rule in the set of reaction rules, and recalculating the first relative deviation according to the adjusted set of reaction rules until the first relative deviation meets the preset condition (wherein the training the set of reaction rules by using the sample feedstock information comprises: processing the molecular composition of the sample feedstock according to a present set of reaction rules, to obtain a reaction pathway corresponding to each molecule in the molecular composition of the sample feedstock; obtaining first molecule composition of a device output product comprising the sample feedstock, an intermediate product, and a predicted product according to the reaction path corresponding to each molecule in the molecular composition of the sample feedstock; in the device output product, comprising: the sample feedstock, the intermediate product, and the predicted product; calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the set of reaction rules; and if the first relative deviation does not meet the preset condition, adjusting a reaction rule in the set of reaction rules, and recalculating the first relative deviation according to the adjusted set of reaction rules until the first relative deviation meets the preset condition. - claim 24 of application 18047424)
Claim 29 of the instant application recites wherein the calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product comprises: acquiring species of single molecules in the first molecule composition, to constitute a first set; acquiring species of single molecules in the second molecule composition, to constitute a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, obtaining a pre-stored relative deviation value that does not meet the preset condition as the first relative deviation; and if the second set is a subset of the first set, calculating the first relative deviation by a calculating formula as follows:
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;
where, x1 is the first relative deviation, M is the first set, M1 is a set of species of single molecules in the molecular composition of the sample feedstock, M2 is a set of species of single molecules in the molecular composition of the intermediate product, M3 is the second set, and card is the number of elements in the sets. (wherein the calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product comprises: acquiring species of single molecules in the first molecule composition, to constitute a first set; acquiring species of single molecules in the second molecule composition, to constitute a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, obtaining a pre-stored relative deviation value that does not meet the preset condition as the first relative deviation; and if the second set is a subset of the first set, calculating the first relative deviation by a calculating formula as follows:
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; where, x1 is the first relative deviation, M is the first set, M1 is a set of species of single molecules in the molecular composition of the sample feedstock, M2 is a set of species of single molecules in the molecular composition of the intermediate product, M3 is the second set, and card represents the number of elements in the sets. - claim 25 of application 18047424)
Claim 30 of the instant application recites wherein the training the reaction rate algorithm by using the sample feedstock information comprises: calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm; obtaining predicted content of each molecule in a predicted product corresponding to the sample feedstock according to molecular content of each molecule in the sample feedstock and the reaction rate of the reaction path corresponding to the molecule; calculating a second relative deviation according to the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting a parameter in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition. (wherein the training the reaction rate algorithm by using the sample feedstock information comprises: calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm; obtaining predicted content of each molecule in a predicted product corresponding to the sample feedstock according to molecular content of each molecule in the sample feedstock and the reaction rate of the reaction path corresponding to the molecule; calculating a second relative deviation according to the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting a parameter in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition. - claim 26 of application 18047424)
Claim 31 of the instant application recites wherein the calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm comprises: calculating a reaction rate of each reaction path according to a reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined according to a calculation formula as follows:
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where, k is the reaction rate constant, kB is the Boltzmann constant, h is the Planck constant, R is an ideal gas constant, E is a temperature value of the environment at which the reaction path is located, exp is an exponential function with base of natural constant, ΔS is an entropy change before and after the reaction corresponding to the reaction rule corresponding to the reaction path, ΔE is a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, φ is a catalyst activity factor, P is a pressure value of the environment at which the reaction path is located, and α is a pressure influencing factor corresponding to the reaction rule corresponding to the reaction path (wherein the calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm comprises: calculating a reaction rate of each reaction path according to a reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined according to a calculation formula as follows:
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where, k is the reaction rate constant, kB is the Boltzmann constant, h is the Planck constant, R is an ideal gas constant, E is a temperature value of the environment at which the reaction path is located, exp is an exponential function with base of natural constant, AS is an entropy change before and after the reaction corresponding to the reaction rule corresponding to the reaction path, AE is a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, is a catalyst activity factor, P is a pressure value of the environment at which the reaction path is located, and a is a pressure influencing factor corresponding to the reaction rule corresponding to the reaction path. - claim 27 of application 18047424)
Claim 33 of the instant application recites a system for real-time optimization of a molecular level device, wherein the system for real-time optimization of a molecular level device comprises a processor and a memory, wherein the processor is configured to execute a real- time optimization program of the molecular level device stored in the memory to implement a method for real-time optimization of the molecular level device of claim 1. (an optimization system for a whole process of molecular-level oil refinery comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory are in communication with each other via the communication bus; the memory is configured to store a computer program; and the processor is configured to carry out the method according to claim 1 when executing the program stored in the memory - claim 29 of application 18047424)
Claim 34 of the instant application recites wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement a method for real-time optimization of the molecular level device of claim 1 (a computer-readable storage medium, wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement the method according to claim 1 - claim 30 of application 18047424)
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-33 are directed to a method and system for optimization of molecular refinement. Claim 34 is directed to a computer readable storage medium and under the broadest reasonable interpretation is ineligible. Therefore, claim 34 is rejected. Examiner will analyze it as a non-transitory computer readable medium for the purpose of compact prosecution.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if
so, then it is determined in Prong Two whether the recited judicial exception is integrated into a
practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception,
examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a
law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition (mental process)
respectively inputting, according to a preset feedstock ratio, the corresponding fractions into a pre-trained product prediction model corresponding to a petroleum processing device as petroleum processing feedstocks, to obtain predicted molecular composition of a corresponding predicted product output by the pre-trained product prediction model and predicted molecular content of each single molecule in the predicted molecular composition (mental process, your mind can look at ratios and make a prediction)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining whether each of the input flows meets a preset input flow range of the respective petroleum processing device; and (mental process)
adjusting the preset feedstock ratio if any one of the input flows does not meet the preset input flow range of the respective petroleum processing device, and respectively re-inputting, according to the adjusted preset feedstock ratio, the corresponding fractions into the pre-trained product prediction model of the respective petroleum processing device as petroleum processing feedstocks, until each of the input flows meets the preset input flow range of the respective petroleum processing device (mental process, contingent limitation, contingent limitation)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
calculating a physical property of each single molecule in the predicted molecular composition according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition (mathematical concept)
calculating a predicted physical property of the predicted product according to the physical property of each single molecule in the predicted molecular composition and the predicted molecular content of each single molecule in the predicted molecular composition; and (mathematical concept)
determining whether the predicted physical property of each of the predicted products meets a preset physical property restriction interval of the corresponding target product in the preset standard set. (mental process)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products. (mathematical concept)
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining whether the product property of each of the mixed products meets a preset product property of a target mixed product obtained by blending corresponding each target product in the preset standard set (mental process)
determining whether the target parameter meets a preset condition (mental process)
if the target parameter meets the preset condition, determining that the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set (mental process, contingent limitation)
Dependent claim 6 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
calculating a product benefit of each of mixed products according to the yield of each of mixed products and the product price of each of mixed products (mathematical concept)
subtracting feedstock prices of all of the petroleum processing feedstocks and operating costs of all of the petroleum processing devices from the cumulative benefit to obtain a comprehensive benefit (mathematical concept)
determining whether the comprehensive benefit reaches a maximum value; (mental process)
determining that the target parameter meets the preset condition if the comprehensive benefit reaches the maximum value; and (mental process)
determining that the target parameter does not meet the preset condition if the comprehensive benefit does not reach the maximum value (mental process)
Dependent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
calculating a physical property of each single molecule in each of the mixed products according to the number of groups of each group contained in each single molecule in each of the mixed products and a contribution value of each group to the physical property; and (mathematical concept)
calculating a product property of each of the mixed products according to the physical property and the second component content of each single molecule in each of the mixed products. (mathematical concept)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein calculation of the physical property of each single molecule comprises: for each single molecule, acquiring the number of groups of each group constituting the single molecule and a contribution value of each group to the physical property; (mental process, mathematical concept, this just limits the type of math of Claim 9 which it depends on)
and inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, to acquire the physical property of the single molecule output by the pre-trained property calculation model (mathematical concept, this just limits the type of math of Claim 9 which it depends on)
wherein the pre-trained property calculation model is used to calculate the physical property of the single molecule according to the number of groups of each group contained in a single molecule and a contribution value of each group to the physical property. (mathematical concept, this just limits the type of math of Claim 9 which it depends on)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, before the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model, the method further comprises: comparing the number of groups of each group constituting the single molecule with molecular information of a template single molecule with known physical properties pre-stored in a database, the molecular information comprising the number of groups of each group constituting the template single molecule; (mental process, mathematical concept, this just limits the type of math of Claim 10 which it depends on)
determining whether there is a same template single molecule as the single molecule; (mental process, mathematical concept, this just limits the type of math of Claim 10 which it depends on)
if there is a same template single molecule as the single molecule, outputting the physical properties of the template single molecule as a physical property of the single molecule; and if there is not a same template single molecule as the single molecule, then performing the step of the inputting the number of groups of each group constituting the single molecule and the contribution value of each group to the physical property into a pre-trained property calculation model (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein a step of training the property calculation model comprises: constructing a property calculation model of a single molecule (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
acquiring the number of groups of each group constituting a sample single molecule (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
wherein the physical property of the sample single molecule is known; inputting the number of groups of each group constituting the sample single molecule into the property calculation model (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
acquiring a predicted physical property of the sample single molecule output by the property calculation model (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
if a deviation value between the predicted physical property and the physical property which is known is less than a preset deviation threshold, determining that the property calculation model converges, acquiring a contribution value of each group to the physical property in the property calculation model which is converged, and storing the contribution value of the group to the physical property (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
if the deviation value between the predicted physical property and the physical property which is known is greater than or equal to the deviation threshold, adjusting a contribution value of each group to the physical property in the property calculation model until the property calculation model converges. (mathematical concept, this just limits the type of math of Claim 10 which it depends on)
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the property calculation model is established as shown below: f=a+Σni Δf i; where, f is the physical property of the single molecule, ni is the number of groups of the i-th group in the single molecule, Δfi is the contribution value of the i-th group in the single molecule to the physical property, and a is an associated constant (mathematical concept)
Dependent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the acquiring the number of groups of each group constituting a sample single molecule comprises: determining a primary group, the number of groups of the primary group, a multi-stage group, and the number of groups of the multi-stage group in all groups of the single molecule; (mental process, mathematical concept, this just limits the type of math of Claim 12 which it depends on)
taking all groups constituting the single molecule as the primary group; (mental process, mathematical concept, this just limits the type of math of Claim 12 which it depends on)
and taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group. (mental process, mathematical concept, this just limits the type of math of Claim 12 which it depends on)
Dependent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, the property calculation model is established as shown below:
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where, f is the physical property of the single molecule, m1i is the number of groups of the i-th group in the primary group, Δf1i is the contribution value of the i-th group in the primary group to the physical property, m2j is the number of groups of the j-th group in a secondary group, Δf2j is the contribution value of the j-th group in the secondary group to the physical property, mNI is the number of groups of the l-th group in an N-stage group, ΔfNI is the contribution value of the l-th group in the N-stage group to the physical property, a is an associated constant, and N is a positive integer greater than or equal to 2. (mathematical concept)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining a primary group, the number of groups of the primary group, a multi-stage group, and the number of groups of the multi-stage group in all groups of the single molecule (mental process)
taking all groups constituting the single molecule as the primary group; and (mental process)
taking various groups which coexist and contribute to a same physical property in common as the multi-stage group, and taking the number of the various groups as a level of the multi-stage group (mental process)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, the physical property of the single molecule comprises a boiling point of a single molecule; the calculating the physical property of the single molecule comprises: calculating the boiling point of the single molecule according to a property calculation model as follows:
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where, T is the boiling point of the single molecule, SOL is a single molecule vector converted according to the number of groups of each group constituting the single molecule, GROUP11 is a first contribution value vector converted according to a contribution value of the primary group to the boiling point, GROUP12 is a second contribution value vector converted according to a contribution value of the secondary group to the boiling point, GROUPIN is an N-th contribution value vector converted according to a contribution value of the N-stage group to the boiling point, Numh is the number of atoms other than the hydrogen atom in the single molecule, d is a first preset constant, b is a second preset constant, c is a third preset constant, and N is a positive integer greater than or equal to 2
Dependent claim 18 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, the physical property of the single molecule comprises a density of a single molecule; the calculating the physical property of the single molecule comprises: calculating the density of the single molecule according to a property calculation model as follows:
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where, D is the density of the single molecule, SOL is a single molecule vector converted according to the number of groups of each group constituting the single molecule, GROUP21 is an N+1-th contribution value vector converted according to a contribution value of the primary group to the density, GROUP22is an N+2-th contribution value vector converted according to a contribution value GROUP of the secondary group to the density, 2N is a 2N-th contribution value vector converted according to a contribution value of the N-stage group to the density, e is the fourth preset constant; and N is a positive integer greater than or equal to 2. (mathematical concept)
Dependent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, the physical property of the single molecule comprises an octane number of a single molecule; the calculating the physical property of the single molecule comprises:calculating the octane number of the single molecule according to a property calculation model as follows:
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where, X is the octane number of the single molecule, SOL is a single molecule vector converted according to the number of groups of each group GROUP constituting the single molecule, 31 is a 2N+1-th contribution value vector converted according to a contribution value of the primary group to the octane GROUP number, 32 is a 2N+2-th contribution value vector converted according to GROUP a contribution value of the secondary group to the octane number, 3N is a 3N-ty contribution value vector converted according to a contribution value of the N-stage group to the octane number; N is a positive integer greater than or equal to 2; and h is the fifth preset constant. (mathematical concept)
Dependent claim 20 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, the product property of the mixed products comprises a density, a cloud point, a pour point, an aniline point, and an octane number. (mental process and mathematical concept, this just limits what is being predicted)
Dependent claim 21 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, when a product property of the mixed product is the density, calculating the product property of each of the mixed products comprises: calculating the density of each of the mixed products according to a calculation formula as follows:
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where, density is the density of the mixed product, Di is the density of the i-th single molecule, and X(i_volume) is second component content of the i- th single molecule (mathematical concept)
Dependent claim 22 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
herein, when a product property of the mixed product is the cloud point, calculating the product property of each of the mixed products comprises: for each mixed product, calculating a cloud point contribution value of each single molecule according to the density and the boiling point of each single molecule in the mixed product; and (mathematical concept)
calculating the cloud point of the mixed product according to cloud point contribution values of all of the single molecules and content of each single molecule in the mixed product. (mathematical concept)
Dependent claim 23 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, when a product property of the mixed product is the pour point, calculating the product property of each of the mixed products comprises: for each mixed product, (mathematical concept)
calculating a pour point contribution value of each single molecule according to the density and molecular weight of each single molecule in the mixed product; and (mathematical concept)
calculating the pour point of the mixed product according to pour point contribution values of all of the single molecules and content of each single molecule in the mixed product. (mathematical concept)
Dependent claim 24 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, when a product property of the mixed product is the aniline point, calculating the product property of each of the mixed products comprises: for each mixed product, calculating an aniline point contribution value of each single molecule according to the density and the boiling point of each single molecule in the mixed product; and (mathematical concept)
calculating the aniline point of the mixed product according to aniline point contribution values of all of the single molecules and content of each single molecule in the mixed product (mathematical concept)
Dependent claim 25 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, when a product property of the mixed product is the octane number, calculating the product property of each of the mixed products comprises: for each mixed product, acquiring the octane number of each single molecule and content of each single molecule in the mixed product; and calculating the octane number of the mixed products according to a calculation formula as follows:
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where, ON is the octane number of the mixed product, HISQFG is a molecular collection, H is a molecular set of n-alkanes, I is a molecular set of isoalkanes, S is a molecular set of cycloalkanes, Q is a molecular set of olefins, F is a molecular set of aromatic hydrocarbons, G is a molecular set of oxygenated compounds, vi is content of each molecule in the mixed product, vH, vI, vS, vQ, vF and vG are total content of n-alkanes, total content of isoalkanes, total content of cycloalkanes, total content of olefins, total content of aromatic hydrocarbons, and total content of a compound of oxygenated compounds in the mixed product, respectively, βi is a regression parameter of each molecule in the mixed product, ONi is an octane number of each molecule in the mixed product, CH is an interaction coefficient of n-alkanes with other molecules, CI is an interaction coefficient of isoalkanes with other molecules; CS is an interaction coefficient of cycloalkanes with other molecules; CQ is an interaction coefficient of olefins with other molecules, CF is an interaction coefficient of aromatic hydrocarbons with other molecules, CG is an interaction coefficient of oxygenated compounds with other molecules, kHI (a) is a first constant coefficient between n-alkanes and isoalkanes, kHS (a) is a first constant coefficient between n-alkanes and cycloalkanes, kHQ (a) is a first constant coefficient between n-alkanes and olefins, kHF (a) is a first constant coefficient between n-alkanes and aromatic hydrocarbons, kHG (a) is a first constant coefficient between n-alkanes and oxygenated compounds, kIS (a) is a first constant coefficient between isoalkanes and cycloalkanes, kIQ (a) is a first constant coefficient between isoalkanes and olefins, kIF (a) is a first constant coefficient between isoalkanes and aromatic hydrocarbons, kIG (a) is a first constant coefficient between isoalkanes and oxygenated compounds, kSQ (a) is a first constant coefficient between cycloalkanes and olefins, kSF (a) is a first constant coefficient between cycloalkanes and aromatic hydrocarbons, kSG (a) is a first constant coefficient between cycloalkanes and oxygenated compounds, kQF (a) is a first constant coefficient between olefins and aromatic hydrocarbons, kQG (a) is a second constant coefficient between olefins and oxygenated compounds, kFG (a) is a first constant coefficient between aromatic hydrocarbons and oxygenated compounds, kHI (a) is a second constant coefficient between n-alkanes and isoalkanes, kHS (a) is a second constant coefficient between n-alkanes and cycloalkanes, kHQ (a) is a second constant coefficient between n-alkanes and olefins, kHF (a) is a second constant coefficient between n-alkanes and aromatic hydrocarbons, kHG (a) is a second constant coefficient between n-alkanes and oxygenated compounds, kIS (a) is a second constant coefficient between isoalkanes and cycloalkanes, kIQ (a) is a second constant coefficient between isoalkanes and olefins, kIF (a) is a second constant coefficient between isoalkanes and aromatic hydrocarbons, kIG (a) is a second constant coefficient between isoalkanes and oxygenated compounds, kSQ (a) is a second constant coefficient between cycloalkanes and olefins, kSF (a) is a second constant coefficient between cycloalkanes and aromatic hydrocarbons, kSG (b) is a second constant coefficient between cycloalkanes and oxygenated compound, kQF (b) is a second constant coefficient between olefins and aromatic hydrocarbons, kQG (b) is a second constant coefficient between olefins and oxygenated compound, and kFG (b) is a second constant coefficient between aromatic hydrocarbons and oxygenated compound; wherein the octane number comprises: a research octane number and a motor octane number (mathematical concept)
Dependent claim 26 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein, a step of training the product prediction model comprises: establishing a product prediction model; wherein the product prediction model comprises: a set of reaction rules comprising a plurality of reaction rules and a reaction rate algorithm (mental process)
training the set of reaction rules by using the sample feedstock information, and fixing the set of reaction rules that has been trained; and (mathematical concept)
training the reaction rate algorithm by using the sample feedstock information, and fixing the reaction rate algorithm that has been trained, to obtain the product prediction model that has been trained. (mathematical concept)
Dependent claim 28 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the training the set of reaction rules by using the sample feedstock information comprises: processing the molecular composition of the sample feedstock according to a preset set of reaction rules, to obtain a reaction pathway corresponding to each molecule in the molecular composition of the sample feedstock (mental process, one can mentally go through a set of rules)
obtaining first molecule composition of a device output product comprising the sample feedstock, an intermediate product, and a predicted product according to the reaction path corresponding to each molecule in the molecular composition of the sample feedstock (mathematical process, as it is obtained through a prediction model)
in the device output product, comprising: the sample feedstock, the intermediate product, and the predicted product; calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product (mathematical concept)
if the first relative deviation meets a preset condition, fixing the set of reaction rules; and if the first relative deviation does not meet the preset condition, adjusting a reaction rule in the set of reaction rules, and recalculating the first relative deviation according to the adjusted set of reaction rules until the first relative deviation meets the preset condition. (mental process)
Dependent claim 29 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the calculating a first relative deviation according to the first molecular composition of the device output product and second molecular composition of the actual product comprises: acquiring species of single molecules in the first molecule composition, to constitute a first set; acquiring species of single molecules in the second molecule composition, to constitute a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, obtaining a pre-stored relative deviation value that does not meet the preset condition as the first relative deviation; and if the second set is a subset of the first set, calculating the first relative deviation by a calculating formula as follows
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; where, x1 is the first relative deviation, M is the first set, M1 is a set of species of single molecules in the molecular composition of the sample feedstock, M2 is a set of species of single molecules in the molecular composition of the intermediate product, M3 is the second set, and card is the number of elements in the sets (mathematical concept)
Dependent claim 30 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the training the reaction rate algorithm by using the sample feedstock information comprises: calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm; (mathematical concept)
obtaining predicted content of each molecule in a predicted product corresponding to the sample feedstock according to molecular content of each molecule in the sample feedstock and the reaction rate of the reaction path corresponding to the molecule; (mathematical concept, this is just output from a mathematical model)
calculating a second relative deviation according to the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting a parameter in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition (mathematical concept)
Dependent claim 31 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the calculating a reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample feedstock, respectively, according to the reaction rate algorithm comprises: calculating a reaction rate of each reaction path according to a reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined according to a calculation formula as follows:
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where, k is the reaction rate constant, kB is the Boltzmann constant, h is the Planck constant, R is an ideal gas constant, E is a temperature value of the environment at which the reaction path is located, exp is an exponential function with base of natural constant, ΔS is an entropy change before and after the reaction corresponding to the reaction rule corresponding to the reaction path, ΔE is a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, φ is a catalyst activity factor, P is a pressure value of the environment at which the reaction path is located, and α is a pressure influencing factor corresponding to the reaction rule corresponding to the reaction path (mathematical concept)
Dependent claim 32 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein types of the petroleum processing devices comprise: a catalytic cracking unit, a delayed coking unit, a residue hydrotreating unit, a hydrocracking unit, a diesel hydro-upgrading unit, a diesel hydro-refining unit, a gasoline hydro-refining unit, a catalytic reforming unit and an alkylation unit; wherein each petroleum processing device corresponds to a set of reaction rules (mental process, mathematical concept, this just limits the prediction model)
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-34 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further
examination is performed that analyzes if the claim recites additional elements that when examined as a
whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that
integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception
in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements
are analyzed to determine if the abstract idea is integrated into a practical application (MPEP
2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract
idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
A method for real-time optimization of a molecular level device, wherein the method comprises: acquiring molecular composition of crude oil; acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil
acquiring a preset standard set for a preset target product
and if the predicted product does not meet any preset standard for a target product corresponding to the predicted product in the preset standard set, adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (contingent limitation)
The additional element in dependent claim 2 includes:
acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices
The additional element in dependent claim 4 includes:
blending each of the predicted products which is used as a product blending feedstock according to a preset rule set, to obtain molecular composition of a plurality of mixed products and content of each single molecule in each of the mixed products (contingent limitation)
The additional element in dependent claim 5 includes:
if the preset product property is met, obtaining a target parameter according to all of the mixed product (contingent limitation)
outputting the preset feedstock ratio, the pre-trained product prediction model and the preset rule set as a production and processing scheme (contingent limitation)
if the target parameter does not meet the preset condition, adjusting the operation parameter in the pre-trained product prediction model and a preset rule in the preset rule set, to re-obtain a plurality of mixed products, until the product property of each of the mixed products meets the preset product property and the target parameters in all of the mixed products meet the preset condition (contingent limitation)
The additional element in dependent claim 6 includes:
wherein the obtaining a target parameter according to all of the mixed products and determining whether the target parameter meets a preset condition comprises: acquiring a product price of each of mixed products and a yield of each of mixed products (contingent limitation)
accumulating the product benefit of each of mixed products to obtain a cumulative benefit
acquiring a feedstock price of each group of the petroleum processing feedstocks and an operating cost of each of the petroleum processing devices (contingent limitation)
serving the comprehensive benefit as the target parameter (contingent limitation)
The additional element in dependent claim 7 includes:
wherein the operation parameter comprises a temperature of an environment where a reaction path in the pre-trained product prediction model is located (contingent limitation)
the adjusting an operation parameter in the pre-trained product prediction model comprises: adjusting a temperature of an environment where a reaction path corresponding to the predicted product in the pre-trained product prediction model is located (contingent limitation)
re-obtaining the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in each of the predicted products according to the adjusted temperature until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (contingent limitation)
The additional element in dependent claim 8 includes:
wherein the operation parameter comprises a pressure of an environment where a reaction path in the pre-trained product prediction model is located; (contingent limitation)
and the adjusting an operation parameter in the pre-trained product prediction model comprises: adjusting a pressure of an environment where a reaction path corresponding to the predicted product in the pre-trained product prediction model is located; (contingent limitation)
and re-obtaining the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in each of the predicted products according to the adjusted pressure until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set. (contingent limitation)
The additional element in dependent claim 9 includes:
wherein the respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products comprises: acquiring first molecular composition of each group of the product blending feedstocks and first component content of each single molecule in each group of the product blending feedstocks; (contingent limitation)
based on the preset rule set, obtaining second molecular composition of each of mixed products and second component content of each single molecule in each of mixed products according to the first molecular composition of each group of the product blending feedstocks and the first component content of each single molecule in each group of the product blending feedstocks; (contingent limitation)
The additional element in dependent claim 26 includes:
acquiring sample feedstock information for a sample feedstock
The additional element in dependent claim 27 includes:
wherein the sample feedstock information of the sample feedstock comprises: molecular composition of the sample feedstock, molecular content of each molecule in the sample feedstock, molecular composition of an actual product corresponding to the sample feedstock, and actual content of each molecule in the actual product. (just limits type of data that I being gathered by claim 26)
The additional element in dependent claim 33 includes:
wherein the system for real-time optimization of a molecular level device comprises a processor and a memory, wherein the processor is configured to execute a real- time optimization program of the molecular level device stored in the memory to implement a method for real-time optimization of the molecular level device of claim 1.
The additional element in dependent claim 34 includes:
wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement a method for real-time optimization of the molecular level device of claim 1.
The additional elements of acquiring molecular composition of crude oil; acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil (Claim 1) acquiring a preset standard set for a preset target product (Claim 1), and if the predicted product does not meet any preset standard for a target product corresponding to the predicted product in the preset standard set, adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (contingent limitation, Claim 1) acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices (Claim 2) acquiring sample feedstock information for a sample feedstock (Claim 26), wherein the sample feedstock information of the sample feedstock comprises: molecular composition of the sample feedstock, molecular content of each molecule in the sample feedstock, molecular composition of an actual product corresponding to the sample feedstock, and actual content of each molecule in the actual product (Claim 27, this just limits type of data that I being gathered by claim 26) are insignificant extra-solution activity and or part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
The additional elements in claims 4-9 are contingent limitations as if the predicted property is equivalent to the target the optimization loop does not need to run. Therefore, the additional elements in claims 4-9 do not integrate a judicial exception into a practical application.
The additional elements of a method for real-time optimization of a molecular level device, wherein the method comprises (Claim 1) wherein the system for real-time optimization of a molecular level device comprises a processor and a memory, wherein the processor is configured to execute a real- time optimization program of the molecular level device stored in the memory to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 33), wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 34) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Claims 3,10-25,28-32 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-34 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-34 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional elements recited in claims 1,2, 4-9, 26-27, 33-34 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of acquiring molecular composition of crude oil; acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil (Claim 1) acquiring a preset standard set for a preset target product (Claim 1), and if the predicted product does not meet any preset standard for a target product corresponding to the predicted product in the preset standard set, adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (contingent limitation, Claim 1) acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices (Claim 2) acquiring sample feedstock information for a sample feedstock (Claim 26), wherein the sample feedstock information of the sample feedstock comprises: molecular composition of the sample feedstock, molecular content of each molecule in the sample feedstock, molecular composition of an actual product corresponding to the sample feedstock, and actual content of each molecule in the actual product (Claim 27, this just limits type of data that I being gathered by claim 26) are insignificant extra-solution activity and or part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). The data gathering steps are also conventional. Evidence for conventionality is that simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). As well as iv. Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone, TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747. As there is no specific mention on how the data gets acquired in the broadest reasonable interpretation it can be understood to be simply receiving data over a network.
The additional elements in claims 4-9 are contingent limitations as if the predicted property is equivalent to the target the optimization loop does not need to run. Therefore, the additional elements in claims 4-9 do not integrate a judicial exception into a practical application.
The additional elements of a method for real-time optimization of a molecular level device, wherein the method comprises (Claim 1) wherein the system for real-time optimization of a molecular level device comprises a processor and a memory, wherein the processor is configured to execute a real- time optimization program of the molecular level device stored in the memory to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 33), wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 34) fail to integrate a judicial exception into a practical application merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)
When taken alone, all additional elements in claims 1-34 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-34 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9 and 32-34 are rejected under 35 U.S.C. 103 as being unpatentable over Quann et al. (Quann et al. Structure-Oriented Lumping: Describing the Chemistry of Complex Hydrocarbon Mixtures. 1992, 31 (11), 2483–2497.) in view of He et al. (He et al. A Kinetic Model for in Situ Coking Denitrification of Heavy Oil with High Nitrogen Content Based on Starch Using a Structure-Oriented Lumping Method. RSC Advances 2018, 8 (57), 32707–32718.) in further view of Qin et al. (Qin et al. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice 2003, 11 (7), 733–764.) in further view of Clayton et al. (Clayton et el. Algorithms for the Self-Optimisation of Chemical Reactions. Reaction Chemistry & Engineering 2019, 4 (9), 1545–1554) in further view of Neiro et al. (Neiro, S. M. S.; Pinto, J. M. A General Modeling Framework for the Operational Planning of Petroleum Supply Chains. Computers & Chemical Engineering 2004, 28 (6-7), 871–896.). The italicized text corresponds to the instant claim limitations.
With respect to the limitations of Claims 1, 33, 34, Quann et al. teaches a method, called structure-oriented lumping (SOL), for describing the composition, reactions, and properties of complex hydrocarbon mixtures has been developed. Structure-oriented lumping represents individual hydrocarbon molecules as a vector of incremental structural features. A mixture of hydrocarbons is represented as a set of these vectors, each with an associated weight percent. The vector representation of molecules provides a convenient framework for constructing reaction networks of arbitrary size and complexity, for developing molecular-based property correlations, and for incorporating existing group contribution methods for the estimation of molecular thermodynamic properties. The SOL approach provides a foundation for molecular-based modeling of all refinery processes. There paper introduces the concepts and principles of structure-oriented lumping (pg. 2483 paragraph 1, acquiring molecular composition of crude oil (Claim 1), wherein the system for real-time optimization of a molecular level device comprises a processor and a memory, wherein the processor is configured to execute a real- time optimization program of the molecular level device stored in the memory to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 33), wherein the computer-readable storage medium has stored therein one or more programs, the one or more programs being executable by one or more processors to implement a method for real-time optimization of the molecular level device of claim 1 (Claim 34)
With respect to the limitations of Claims 1, 3, Quann et al. teaches analytical techniques for distillate and larger hydrocarbons are, except in certain special cases (e.g., normal paraffins, bare ring aromatics, biomarkers), incapable of distinguishing structural isomers. Only molecular mass and number of aromatic rings, from which structural increments can be assigned, are measurable (pg. 2486, col. 2, paragraph 3, acquiring molecular composition of various fractions obtained by distillation of the crude oil according to physical properties of various single molecules in the molecular composition of the crude oil (Claim 1) calculating a physical property of each single molecule in the predicted molecular composition according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition (Claim 3)).
With respect to the limitations of Claims 3, 4, 9, 32, Quann et al. teaches a new approach to model the chemistry of complex mixtures, called structure-oriented lumping (SOL), has been developed over the past several years at Mobil. The basic concept of structure-oriented lumping is that hydrocarbon molecules can be described as a vector, with the elements of the vector representing structural features sufficient to construct any molecule. Each molecule in a mixture is then represented by a structure vector. The approach is still lumping, but at the level of molecular structure. Different molecules with the same set of structural groups, i.e., certain isomers, are lumped and represented by the same vector. The structure vector provides a framework to enable rule-based generation of reaction networks and rate equations involving thousands of components and many thousands of reactions. These techniques are applied in various oil refineries. (pg. 2483 col. 2 paragraph 4 – pg. 2484, col 1, paragraph 1, calculating a physical property of each single molecule in the predicted molecular composition according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition; calculating a predicted physical property of the predicted product according to the physical property of each single molecule in the predicted molecular composition and the predicted molecular content of each single molecule in the predicted molecular composition (Claim 3), blending each of the predicted products which is used as a product blending feedstock according to a preset rule set, to obtain molecular composition of a plurality of mixed products and content of each single molecule in each of the mixed products; and respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products (Claim 4) wherein the respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products comprises: acquiring first molecular composition of each group of the product blending feedstocks and first component content of each single molecule in each group of the product blending feedstocks; based on the preset rule set, obtaining second molecular composition of each of mixed products and second component content of each single molecule in each of mixed products according to the first molecular composition of each group of the product blending feedstocks and the first component content of each single molecule in each group of the product blending feedstocks; calculating a physical property of each single molecule in each of the mixed products according to the number of groups of each group contained in each single molecule in each of the mixed products and a contribution value of each group to the physical property; and calculating a product property of each of the mixed products according to the physical property and the second component content of each single molecule in each of the mixed products (Claim 9), wherein types of the petroleum processing devices comprise: a catalytic cracking unit, a delayed coking unit, a residue hydrotreating unit, a hydrocracking unit, a diesel hydro-upgrading unit, a diesel hydro-refining unit, a gasoline hydro-refining unit, a catalytic reforming unit and an alkylation unit; wherein each petroleum processing device corresponds to a set of reaction rules (Claim 32)
With respect to the limitations of Claims 5, 6, 7,8, Quann et al. teaches the following limitations as they are optional contingent limitations and under the broadest reasonable interpretation do not limit the claim if the original predicted properties match the target properties as no more steps, calculations, or predictions need to be made if the output initially matches the target value.
(wherein the determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in each of the preset standard set comprises: determining whether the product property of each of the mixed products meets a preset product property of a target mixed product obtained by blending corresponding each target product in the preset standard set; if the preset product property is met, obtaining a target parameter according to all of the mixed products and determining whether the target parameter meets a preset condition; if the target parameter meets the preset condition, determining that the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set, and outputting the preset feedstock ratio, the pre-trained product prediction model and the preset rule set as a production and processing scheme; and if the target parameter does not meet the preset condition, adjusting the operation parameter in the pre-trained product prediction model and a preset rule in the preset rule set, to re-obtain a plurality of mixed products, until the product property of each of the mixed products meets the preset product property and the target parameters in all of the mixed products meet the preset condition (Claim 5), wherein the obtaining a target parameter according to all of the mixed products and determining whether the target parameter meets a preset condition comprises: acquiring a product price of each of mixed products and a yield of each of mixed products; calculating a product benefit of each of mixed products according to the yield of each of mixed products and the product price of each of mixed products; accumulating the product benefit of each of mixed products to obtain a cumulative benefit; acquiring a feedstock price of each group of the petroleum processing feedstocks and an operating cost of each of the petroleum processing devices; subtracting feedstock prices of all of the petroleum processing feedstocks and operating costs of all of the petroleum processing devices from the cumulative benefit to obtain a comprehensive benefit; serving the comprehensive benefit as the target parameter; determining whether the comprehensive benefit reaches a maximum value; determining that the target parameter meets the preset condition if the comprehensive benefit reaches the maximum value; and determining that the target parameter does not meet the preset condition if the comprehensive benefit does not reach the maximum value (Claim 6), wherein the operation parameter comprises a temperature of an environment where a reaction path in the pre-trained product prediction model is located; and the adjusting an operation parameter in the pre-trained product prediction model comprises: adjusting a temperature of an environment where a reaction path corresponding to the predicted product in the pre-trained product prediction model is located; and re-obtaining the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in each of the predicted products according to the adjusted temperature until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (Claim 7) wherein the operation parameter comprises a pressure of an environment where a reaction path in the pre-trained product prediction model is located; and the adjusting an operation parameter in the pre-trained product prediction model comprises: adjusting a pressure of an environment where a reaction path corresponding to the predicted product in the pre-trained product prediction model is located; and re-obtaining the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in each of the predicted products according to the adjusted pressure until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (Claim 8) wherein the respectively calculating product property of each of the mixed products according to the molecular composition of each of the mixed products and the content of each single molecule in each of the mixed products comprises:acquiring first molecular composition of each group of the product blending feedstocks and first component content of each single molecule in each group of the product blending feedstocks; based on the preset rule set, obtaining second molecular composition of each of mixed products and second component content of each single molecule in each of mixed products according to the first molecular composition of each group of the product blending feedstocks and the first component content of each single molecule in each group of the product blending feedstocks; calculating a physical property of each single molecule in each of the mixed products according to the number of groups of each group contained in each single molecule in each of the mixed products and a contribution value of each group to the physical property; and calculating a product property of each of the mixed products according to the physical property and the second component content of each single molecule in each of the mixed products (Claim 9))
Quann et al. does not explicitly teach
respectively inputting, according to a preset feedstock ratio, the corresponding fractions into a pre-trained product prediction model corresponding to a petroleum processing device as petroleum processing feedstocks, to obtain predicted molecular composition of a corresponding predicted product output by the pre-trained product prediction model and predicted molecular content of each single molecule in the predicted molecular composition (Claim 1)
acquiring a preset standard set for a preset target product; determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition; (Claim 1)
adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (Claim 1)
re-inputting, according to the adjusted preset feedstock ratio, the corresponding fractions into the pre-trained product prediction model of the respective petroleum processing device as petroleum processing feedstocks, until each of the input flows meets the preset input flow range of the respective petroleum processing device (Claim 2)
A method for real-time optimization of a molecular level device, wherein the method comprises (Claim 1),
acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices (Claim 2)
determining whether each of the input flows meets a preset input flow range of the respective petroleum processing device (Claim 2)
adjusting the preset feedstock ratio if any one of the input flows does not meet the preset input flow range of the respective petroleum processing device (Contingent limitation see MPEP 211.04) (Claim 2)
determining whether the predicted physical property of each of the predicted products meets a preset physical property restriction interval of the corresponding target product in the preset standard set (Claim 3)
With respect to the limitations of Claims 1, He et al. teaches a molecular level process model for in situ coking dentification technology was developed to rationally control the product distribution by the SOL reaction kinetic model, which can be used to predict the influence of starch content on the distribution of products and dentification performance. More importantly, nitrogen compounds in heavy oil is transformed to the chars via in situ directional conversion after the incorporation of starch, which is competitive without other independent dentification processes (such as extraction tower, absorption and so on) involved besides coking compared with other dentification techniques reported. The model takes the amount of starch and heavy oil composition in order to compute (pg. 32717, col. 1, paragraph 9, respectively inputting, according to a preset feedstock ratio, the corresponding fractions into a pre-trained product prediction model corresponding to a petroleum processing device as petroleum processing feedstocks, to obtain predicted molecular composition of a corresponding predicted product output by the pre-trained product prediction model and predicted molecular content of each single molecule in the predicted molecular composition (Claim 1)).
With respect to the limitations of Claims 1, 3, Qin et al. teaches that model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant. At each control interval an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant, and the entire calculation is repeated at subsequent control intervals. Originally developed to meet the specialized control needs of power plants and petroleum refineries, MPC technology can now be found in a wide variety of application areas including chemicals, food processing, automotive, and aerospace applications. These are preset standards for a target product that were previously acquired. (pg. 1, col. 1, paragraph 1) and the MPHC algorithm drives the predicted future output trajectory as closely as possible to a reference trajectory, defined as a first order path from the current output value to the desired setpoint (pg. 5, col. 1, paragraph 4, acquiring a preset standard set for a preset target product; determining whether the predicted product meets a preset standard for a target product corresponding to the predicted product in the preset standard set according to the predicted molecular composition of the predicted product and the predicted molecular content of each single molecule in the predicted molecular composition; (Claim 1) determining whether the predicted physical property of each of the predicted products meets a preset physical property restriction interval of the corresponding target product in the preset standard set (Claim 3)).
With respect to the limitations of Claims 1, 2 Clayton et al. teaches the reaction mixture is analyzed and the responses are supplied to an optimization algorithm. The algorithm then generates the next set of conditions to explore based on the results of the previous experiments, thereby creating a feedback loop. Intelligent analysis of the experimental space reduces the number of experiments required, providing a faster, cheaper and ‘greener’ method for process development. Self-optimizing systems provide an enabling technology for efficient optimization of expensive-to-evaluate chemical systems. As such, algorithms used in self-optimization typically focus on minimizing the number of experiments and material consumed during the optimization process. The parameters are continuingly adjusted until the measured properties are the targets measured properties (pg. 1545, col. 2, paragraph 1, adjusting an operation parameter in the pre-trained product prediction model, to re-obtain predicted molecular composition of the predicted product and predicted molecular content of each single molecule in the predicted molecular composition, until the predicted product meets the preset standard for the target product corresponding to the predicted product in the preset standard set (Claim 1) re-inputting, according to the adjusted preset feedstock ratio, the corresponding fractions into the pre-trained product prediction model of the respective petroleum processing device as petroleum processing feedstocks, until each of the input flows meets the preset input flow range of the respective petroleum processing device (Contingent limitation see MPEP 211.04) (Claim 2))
With respect to the limitations of Claims 1, 2, Neiro et al. teaches the complex topology
is then built by connecting the nodes representing refineries, terminals and pipeline networks. Decision variables include stream flow rates, properties, operational variables, inventory and facilities assignment. The resulting multiperiod model is a large-scale MINLP. The proposed model is applied to a real-world corporation and results show model performance by analyzing different scenarios. The flow of feedstock is acquired in order to make model adjustments (abstract). Moreover, feed flow rate must satisfy CD1 operating capacity: 14 000 ≤ QFCD1,t ≤ 36 000 ∀t ∈ T Production level depends on the feed flow rate, feed properties and on a single operating variable. This shows the maximum and minimum flow rates. The flow rates are adjusted to maximize efficiency in the model. Flow rates will change the feedstock ratio has different amounts of product are included. Additionally a person of ordinary skill in the art would be able to modify Neiro et al. in order to (pg. 882, paragraph 3, A method for real-time optimization of a molecular level device, wherein the method comprises (Claim 1), acquiring an input flow of petroleum processing feedstocks input to each of the petroleum processing devices (Claim 2) determining whether each of the input flows meets a preset input flow range of the respective petroleum processing device (Claim 2); adjusting the preset feedstock ratio if any one of the input flows does not meet the preset input flow range of the respective petroleum processing device (Contingent limitation see MPEP 211.04) (Claim 2))
A person of ordinary skill in the art would be motivated to combine Quann et al. with He et al. with Qin et al. with Neiro et al. as all have to do with optimization of industrial process with a focus on oil refinement. A person of ordinary skill in the art would use literature common in the oil and gas industry. Additionally, a person of ordinary skill in the art would use the optimization method of Clayton et al. as the same principles of optimization apply regardless of scale. There would be a reasonable expectation of success as the instant application is just combining multiple different known methods in the art without modifying how they work. Therefore they are expected to continue to work as they have just been placed in sequential steps.
Art found to be close but was not used is:
Granda et al. (Granda et al. Controlling an Organic Synthesis Robot with Machine Learning to Search for New Reactivity. Nature 2018, 559 (7714), 377–381) teaches an organic synthesis robot that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space. This does live monitoring of reactions and adjusts predictions based on a potential chemical reactivity prediction. This differs from the instant application as its primary purpose is to explore reaction space using chemical reactivity.
Borda et al. (Borda et al. An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone. The Journal of Organic Chemistry 2018, 83 (23), 14286–14299.) teaches a modular autonomous flow reactor combining monitoring technologies with a feedback algorithm is presented for the synthesis of the natural product carpanone. The art uses machine learning HPLC and NMR to optimize yield. It does not predict the physical properties in order to improve the yield which is required in the instant application. The target parameter is also not adjustable as in the instant application.
Christensen et al. (Christensen et al. Future Directions in Modeling the FCC Process: An Emphasis on Product Quality. Chemical Engineering Science 1999, 54 (13-14), 2753–2764.) teaches a new FCC process model has been developed that describes fundamental cracking chemistry of FCC feedstocks using over 3000 molecular species. FCC reaction chemistry is specified using the methods of structure-oriented-lumping (SOL) with over 60 reaction rules. Application of these rules results in a complex network of over 30,000 elementary chemical reactions. Both monomolecular reactions such as cracking, isomerization, and cyclization and bimolecular reactions such as hydrogen transfer, coking, and disproportionation are included. The effects of thermal cracking and metals-catalyzed dehydrogenation are also represented. Reactivity relationships have been developed to reduce the number of parameters required to specify the kinetic model. The kinetic parameters are regressed from data spanning a wide range of FCC process conditions, feed compositions, and catalyst formulations. The art does not use physical property prediction in order to improve oil refineries. Although it does use reaction rules to aid in the refinery process.
Conclusion
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/C.H.B./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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