Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-4, 6, and 9-12 are objected to because of the following informalities: The claims utilize bullet points and dashes to separate the elements of the claims. MPEP 608.01(m) states “Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation…There may be plural indentations to further segregate subcombinations or related steps”. It is suggested to utilize line indentations to separate each element or steps. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: In line 2, it is suggested to recite “a parametric coefficient” as “the parametric coefficient”. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: In line 2, it is suggested to recite “a parametric coefficient” as “the parametric coefficient”. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: In line 7, it is suggested to include “and/or” after “concentration or amount”. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: In line 10, it is suggested to recite “EO” in an unabbreviated form to establish the acronym. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: In lines 12-13, it is suggested to recite “CO2” as “CO2” (subscript format). Appropriate correction is required. Note that claim 1 recites CO2 with the subscript format.
Claim 10 is objected to because of the following informalities: In line 2, “an” should read “and”; in lines 3-4, “representing a from the parametric coefficient a genetic programming…” appears to have typographical/grammatical errors. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: In line 10, “g)” appears to be extraneous. It is suggested to delete “g)”. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: In line 1, it is suggested to recite “a combination of a a) to d)” as “a combination of a) to d)”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-5 and 7-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, claim 3 recites “an online analyzer sensors (Gas chromatograph or Mass spectrometer)” in line 6. It is unclear if the limitations within the parentheses are examples of the online analyzer sensors or required by the claim. The limitations within the parenthesis renders the claim indefinite because it is unclear whether the limitations within the parenthesis are part of the claimed invention. For examination purposes, Gas chromatograph or Mass spectrometer are interpreted as being required.
Regarding claims 4, 7, 9, and 13, the claims recite instances of the phrase “in particular”. The phrase " in particular" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Claims 5, 8, and 14-18 are rejected by virtue of their dependency on claims 4, 7, and 13.
Regarding claim 7, claim 7 recites the limitation " the process" in line 4. There is insufficient antecedent basis for this limitation in the claim. Claim 8 is rejected based on its dependency on claim 7.
Regarding claim 10, claim 10 recites the limitation "the step of" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is suggested to recite “the step of” as “a step of” or “steps of”.
Regarding claim 10, claim 10 recites the limitation " the process for ethylene production" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 11, claim 11 recites the limitation "the step of" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is suggested to recite “the step of” as “a step of” or “steps of”.
Regarding claim 11, claim 11 recites the limitation "the current chloride status" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 11, claim 11 recites “ethylene oxide reactor” in line 2. It is unclear if the ethylene oxide reactor of claim 11 is the same or different from “an ethylene oxide reactor” and/or “a reactor” established in claim 1.
Regarding claim 12, claim 12 recites the limitation "the step of" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is suggested to recite “the step of” as “a step of” or “steps of”.
Regarding claim 12, claim 12 recites the limitation "the process for ethylene production" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites the limitations “automatically determining a parametric coefficient…automatically calculating…”.
In accordance with MPEP 2106, the claims are found to recite statutory subject matter (Step 1: YES) and are analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A: Prong 1).
In the instant application, the limitations of “automatically determining a parametric coefficient…automatically calculating…” of claim 1 could be performed mentally or by mathematical calculations. Accordingly, the claims recite abstract ideas (Step 2A: Prong 1: Yes).
This judicial exception is not integrated into a practical application because the claims do not recite any additional elements that reflects an improvement to technology or applies or uses the judicial exception in some other meaningful way (Step 2A, Prong 2: No). In claim 1, after the steps of “automatically determining a parametric coefficient…automatically calculating…”, no further action is performed. Therefore, the claimed limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Additionally, the preceding step of “automatically acquiring process data…” are used for data gathering in the abstract idea; wherein, data gathering to be used in the abstract idea is insignificant extra-solution activity, and not a particular practical application. See MPEP 2106.05(g). Therefore, the claimed limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea that is not integrated into a practical application (Step 2A, Prong 2: No).
The claims 1-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding claim 1, the preceding step of “automatically acquiring process data…” are used for data gathering in the abstract idea; wherein, data gathering to be used in the abstract idea is insignificant extra-solution activity, and not a particular practical application. See MPEP 2106.05(g). Claim 1 and dependent claims 2-18 further recites limitations, however these limitations generally link the judicial exception to a particular field of use (MPEP 2106.05(h)) and are used for data gathering or data outputting, wherein data gathering to be used in the abstract idea is an insignificant extra-solution activity, and not a practical application (see MPEP 2106.05(g)), which alone or in combination do not amount to significantly more. Additionally, claims 4, 6-7, 10, and 11 further include abstract ideas, i.e. mental processes or mathematical concepts. Regarding claims 4-8 and 12, the claims recite AI algorithm, algorithms, and models, wherein the claimed limitations of algorithms and models amount to no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)). Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 9, and 13-14 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Habenschuss et al. (US 20100267975 A1).
Regarding claim 1, Habenschuss teaches a method of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0021] teaches an epoxidation reaction in a reactor to form ethylene oxide, wherein the epoxidation reaction can be characterized in terms of activity, productivity, yield, and selectivity of the epoxidation reaction; [0024] teaches selectivity is synonymous with efficiency; [0102] and [0105] teaches calculating catalyst effectiveness Z*, i.e. selectivity; Fig. 8 teaches reactor 112) comprising the steps:
Automatically acquiring process data of a reactor producing ethylene oxide by means of sensors ([0100] teaches a coolant pressure controller 140 receives coolant data from pressure meter and a reactor temperature controller receives a temperature signal from a reactor thermocouple, i.e. automatically acquire process data by means of a sensor; Fig. 8 and [0101]-[0102],[0105] teaches a controller receiving inputs and data of reactor 112 by means of sensors, such as reactor outlet stream concentration analyzer 148, a reaction mixture concentration analyzer 150, the alkylene feed flow meter 124, an alkylene feed flow controller 152, an alkylene feed analyzer controller 154, a gaseous chlorine-containing promoter species flow controller 156, and a net product flow meter 158; [0021]-[0022] teaches production of alkylene oxide, such as ethylene oxide; therefore, the various controllers shown in Fig. 8 automatically acquire process data of reactor 112 that produces ethylene oxide via sensors/meters),
Automatically determining a parametric coefficient from the acquired process data ([0100] teaches a coolant pressure controller 140 receives coolant data from pressure meter 142, therefore the coolant pressure controller is implied to automatically determine pressure of the reactor from data from the pressure meter; [0100] teaches temperature can be controlled and a reactor temperature controller receives a temperature signal from a reactor thermocouple, therefore the reactor temperature controller is implied to automatically determine temperature from the data received; Fig. 8 and [0101]-[0102],[0105] teaches reactor outlet stream concentration analyzer 148, reaction mixture concentration analyzer 150, alkylene feed flow meter 124, alkylene feed flow controller 152, alkylene feed analyzer controller 154, gaseous chlorine-containing promoter species flow controller 156, and net product flow meter 158, which each operate to determine parameters from acquired data, such as concentration and feed flow; therefore, the various controllers shown in Fig. 8 automatically determine parametric coefficients from the acquired data of reactor 112 via sensors/meters), and
Automatically calculating the selectivity and/or activity of the catalyst from the parametric coefficient ([0102] teaches the controller receives concentration data, i.e. parametric coefficient, to calculate, i.e. automatically calculating, the overall catalyst chloriding effectiveness, i.e. selectivity of the catalyst; [0105] teaches the controller, such as a computer control system, is programmed to calculate, i.e. automatically calculating, the desired overall catalyst chloriding effectiveness, i.e. selectivity of the catalyst, from the slope information calculated from received inputs, i.e. parametric coefficient).
Regarding claim 2, Habenschuss further teaches wherein the process data comprise
- current sensor data ([0100]-[0102],[0105] teaches controllers that receive data from the reactor 112 via sensors/meters, therefore the process data comprise current sensor data of the reactor), and/or
- change in selectivity (interpreted as not required due to the “and/or” phrases) and/or
- inlet moisture (interpreted as not required due to the “and/or” phrases), and/or
- inlet ethylene oxide (interpreted as not required due to the “and/or” phrases)and/or
- inlet ethane ([0101]-[0103] teaches an alkylene feed flow controller 152, alkylene feed analyzer controller 154, and reaction mixture concentration analyzer 150, where concentration of ethylene was determined from received compositional data indicating the amount of alkylene in the reaction mixture 122, i.e. inlet) and/or
- inlet oxygen concentration ([0037] teaches oxygen concentration at desired ranges; [0074] teaches the reaction mixture includes oxygen; [0118] teaches an oxygen feed 118; [0117] teaches the reactor is operated by maintaining an oxygen concentration; therefore, inlet oxygen concentration is implied to be a process data) and/or
- inlet CO2 concentration (interpreted as not required due to the “and/or” phrases ) and/or
- Cycle Gas (CG) pressure (interpreted as not required due to the “and/or” phrases and/or
- Cycle gas(CG) flow rate (interpreted as not required due to the “and/or” phrases ) and/or
- total chloride concentration ([0102] teaches receiving ethyl chloride, vinyl chloride, and ethylene dichloride concentration data) and/or
- parametric coefficients ([0100]-[0102],[0105] teaches controllers that receive data from the reactor 112 via sensors/meters to determine parameters from acquired data, such as concentration and feed flow).
Regarding claim 3, Habenschuss further teaches wherein the process data of the reactor are captured by
- flow sensors ([0099],[0101] teaches flow meters) and/or
- temperature sensors ([0100] teaches a reactor temperature controller 146 receives a temperature signal from a reactor thermocouple, i.e. temperature sensors) and/or
- pressure sensors (interpreted as not required due to the “and/or” phrases ) and/or
- an online analyzer sensors (Gas chromatograph or Mass spectrometer) (interpreted as not required due to the “and/or” phrases).
Regarding claim 9, Habenschuss further teaches wherein the acquired process data comprise: internal reactor data ([0099]-[0102],[0105] teaches receiving data of reactor 112 via meters/sensors), in particular
- an age of a catalyst (interpreted as not required due to the “and/or” phrases), and/or
- a temperature ([0100]) and/or
- a pressure (interpreted as not required due to the “and/or” phrases) and/or
- inlet moisture (interpreted as not required due to the “and/or” phrases) and/or
- inlet ethylene oxide concentration or amount (interpreted as not required due to the “and/or” phrases)
- an ethane concentration (0101]-[0103] teaches an alkylene feed flow controller 152, alkylene feed analyzer controller 154, and reaction mixture concentration analyzer 150, where concentration of ethylene was determined from received compositional data indicating the amount of alkylene in the reaction mixture 122)
and/or external reactor data (interpreted as not required due to the “and/or” phrases), in particular
- EO stripper bottom temperature and pressure (interpreted as not required due to the “and/or” phrases) and/or
- Cycle water system data, in particular cycle water flow and temperature (interpreted as not required due to the “and/or” phrases), and/or
-CO2 regenerator bottom temperature (interpreted as not required due to the “and/or” phrases ) and/or
-CO2 removal system data, in particular carbonate flow, density, temperature (interpreted as not required due to the “and/or” phrases).
Regarding claim 13, Habenschuss further teaches wherein the process data comprise or consist of at least one of:
a) Total inlet chloride moderator concentration (interpreted as not required due to the “at least one of” phrase)
b) Saturated hydrocarbon inlet concentration, in particular ethane inlet concentration ([0101]-[0103] teaches an alkylene feed flow controller 152, alkylene feed analyzer controller 154, and reaction mixture concentration analyzer 150, where concentration of ethane was determined from reaction mixture concentration analyzer 150 at an inlet of the reactor, i.e. inlet)
c) CO2 inlet concentration and/or the oxygen inlet concentration ([0037] teaches oxygen concentration at desired ranges; [0074] teaches the reaction mixture includes oxygen; [0118] teaches an oxygen feed 118; [0117] teaches the reactor is operated by maintaining an oxygen concentration; therefore, inlet oxygen concentration is implied to be a process data), in particular both the CO2 and oxygen inlet concentrations (interpreted as not required due to the “at least one of” phrase)
d) Moisture (H2O) inlet concentration (interpreted as not required due to the “at least one of” phrase)
e) Work Rate (interpreted as not required due to the “at least one of” phrase)
f) C2H4 (ethylene) inlet concentration ([0101]-[0103] teaches an alkylene feed flow controller 152, alkylene feed analyzer controller 154, and reaction mixture concentration analyzer 150, where concentration of ethylene was determined from received compositional data indicating the amount of alkylene in the reaction mixture 122, i.e. inlet) and/or ethylene oxide inlet concentration (interpreted as not required due to the “and/or” phrase).
Regarding claim 14, Habenschuss further teaches wherein the process data are a combination of one or more of input variables a) to f) (see above claim 13).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 4 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss as applied to claims 1 and 13 above, and further in view of Lahiri et al. (US 20140365195 A1).
Regarding claim 4, Habenschuss fails to teach: wherein - Determining the parametric coefficient from the acquired process data involves calculating the parametric coefficient by means of one, in particular two, Al algorithm or algorithms, respectively.
Habenschuss teaches using a program or mathematical equation ([0081]). Habenschuss teaches the computer control system may be programmed to analyze received inputs and use the data to calculate a slope of the efficiency of the reaction to productivity of the reaction ([0105]).
Lahiri teaches a method (abstract and [0002] teaches systems and methods for monitoring a process and diagnosing an operating condition of the process) of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0058]-[0059] teaches monitoring the process of producing ethylene oxide, i.e. EO, in an EO reactor; [0073] teaches monitoring catalyst selectivity and activity). Lahiri teaches performance of the reaction is measured by selectivity which is calculated by the percentage of ethylene used to produce EO as compared to total ethylene used to produce EO and CO2; wherein selectivity has a profound effect of the efficiency and hence the overall economics of the glycol plant ([0060]). Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1). Lahiri teaches studying an EO reaction process and collection of real time data for real time fault diagnosis and interpretation of ANN and PCA outputs (Fig. 10). Lahiri teaches building an ANN model to construct process outputs, including catalyst selectivity ([0096]-[0097]). Lahiri teaches analysis can be performed automatically suing a programmed computer ([0171]). Lahiri teaches statistical methods are used to find correlation coefficients between catalyst selectivity and other parameters ([0130]). Lahiri teaches a PC loaded with PCA and ANN software, i.e. at least two algorithms, which allow for receiving real time process parameter values from plant from sensors and calculate values in real time, therefore updating plots with fresh real time data ([0168]). Lahiri teaches ANN are computer algorithms, i.e. at least two algorithms, ([0007]), which have emerged as useful tools for modeling, is capable of adapting to a changing environment, and capable of dealing with uncertainties, noisy data, and non-linear relationships ([0008]). Lahiri teaches ANN modeling methods have various advantageous and attractive characteristics for dynamic process modeling ([0009]-[0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Habenschuss to incorporate Habenschuss’s teachings of using a program or equation ([0081]) and a computer programmed to analyze received inputs for calculation ([0105]) and Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using algorithms such as ANN and PCA to allow for real time processing and calculation of process parameters to provide: wherein - Determining the parametric coefficient from the acquired process data involves calculating the parametric coefficient by means of one, in particular two, Al algorithm or algorithms, respectively. Doing so would have a reasonable expectation of successfully improving real time processing and calculation of process parameters of the ethylene oxide reactor using algorithms that have shown advantageous for dynamic process modeling (Lahiri, [0008]-[0015]).
Regarding claim 15, Habenschuss further teaches the process data includes b) (see above claim 13). Habenschuss fails to teach: wherein the process data are a combination of a) and b).
Lahiri teaches a method (abstract and [0002] teaches systems and methods for monitoring a process and diagnosing an operating condition of the process) of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0058]-[0059] teaches monitoring the process of producing ethylene oxide, i.e. EO, in an EO reactor; [0073] teaches monitoring catalyst selectivity and activity). Lahiri teaches performance of the reaction is measured by selectivity which is calculated by the percentage of ethylene used to produce EO as compared to total ethylene used to produce EO and CO2; wherein selectivity has a profound effect of the efficiency and hence the overall economics of the glycol plant ([0060]). Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1), wherein input variables include: a) Total inlet chloride moderator concentration ([0074], table 1, “Total chloride concentration”), and b) ethane inlet concentration ([0064] teaches gas inlet compositions including ethane).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process data of Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using measured independent variables including total inlet chloride moderator concentration and ethane inlet concentration to provide: wherein the process data are a combination of a) and b). Doing so would have a reasonable expectation of successfully improving real time processing and calculation of known process parameters of the ethylene oxide reactor to improve monitoring and diagnosing an operating condition of the reactor (Lahiri, [0008]-[0015]).
Regarding claim 16, Habenschuss further teaches the process data are a combination of b) to c) (see above claim 13). Habenschuss fails to teach wherein the process data are a combination of a) to c).
Lahiri teaches a method (abstract and [0002] teaches systems and methods for monitoring a process and diagnosing an operating condition of the process) of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0058]-[0059] teaches monitoring the process of producing ethylene oxide, i.e. EO, in an EO reactor; [0073] teaches monitoring catalyst selectivity and activity). Lahiri teaches performance of the reaction is measured by selectivity which is calculated by the percentage of ethylene used to produce EO as compared to total ethylene used to produce EO and CO2; wherein selectivity has a profound effect of the efficiency and hence the overall economics of the glycol plant ([0060]). Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1), wherein input variables include: a) Total inlet chloride moderator concentration ([0074], table 1, “Total chloride concentration”), b) ethane inlet concentration ([0064] teaches gas inlet compositions including ethane), and c) CO2 inlet concentration and the oxygen inlet concentration ([0064] teaches measuring gas inlet compositions including oxygen and carbon-dioxide)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process data of Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using measured independent variables a combination of a) to c) to provide: wherein the process data are a combination of a) to c). Doing so would have a reasonable expectation of successfully improving real time processing and calculation of known process parameters of the ethylene oxide reactor to improve monitoring and diagnosing an operating condition of the reactor (Lahiri, [0008]-[0015]).
Regarding claim 17, Habenschuss further teaches the process data includes b) and c) (see above claim 13). Habenschuss fails to teach wherein the process data are a combination of a a) to d).
Lahiri teaches a method (abstract and [0002] teaches systems and methods for monitoring a process and diagnosing an operating condition of the process) of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0058]-[0059] teaches monitoring the process of producing ethylene oxide, i.e. EO, in an EO reactor; [0073] teaches monitoring catalyst selectivity and activity). Lahiri teaches performance of the reaction is measured by selectivity which is calculated by the percentage of ethylene used to produce EO as compared to total ethylene used to produce EO and CO2; wherein selectivity has a profound effect of the efficiency and hence the overall economics of the glycol plant ([0060]). Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1), wherein input variables include: a) Total inlet chloride moderator concentration ([0074], table 1, “Total chloride concentration”), b) ethane inlet concentration ([0064] teaches gas inlet compositions including ethane), c) CO2 inlet concentration and the oxygen inlet concentration ([0064] teaches measuring gas inlet compositions including oxygen and carbon-dioxide), and d) Moisture (H2O) inlet concentration ([0064] teaches measuring gas inlet compositions including water, i.e. moisture).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process data of Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using measured independent variables including a combination of a) to d) to provide: wherein the process data are a combination of a a) to d). Doing so would have a reasonable expectation of successfully improving real time processing and calculation of known process parameters of the ethylene oxide reactor to improve monitoring and diagnosing an operating condition of the reactor (Lahiri, [0008]-[0015]).
Regarding claim 18, Habenschuss further teaches the process data includes b) and c) (see above claim 13). Habenschuss fails to teach wherein the process data are a combination of a) to e).
Lahiri teaches a method (abstract and [0002] teaches systems and methods for monitoring a process and diagnosing an operating condition of the process) of estimating a selectivity and/or activity of a catalyst in an ethylene oxide reactor ([0058]-[0059] teaches monitoring the process of producing ethylene oxide, i.e. EO, in an EO reactor; [0073] teaches monitoring catalyst selectivity and activity). Lahiri teaches performance of the reaction is measured by selectivity which is calculated by the percentage of ethylene used to produce EO as compared to total ethylene used to produce EO and CO2; wherein selectivity has a profound effect of the efficiency and hence the overall economics of the glycol plant ([0060]). Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1), wherein input variables include: a) Total inlet chloride moderator concentration ([0074], table 1, “Total chloride concentration”), b) ethane inlet concentration ([0064] teaches gas inlet compositions including ethane), c) CO2 inlet concentration and the oxygen inlet concentration ([0064] teaches measuring gas inlet compositions including oxygen and carbon-dioxide), d) Moisture (H2O) inlet concentration ([0064] teaches measuring gas inlet compositions including water, i.e. moisture), and e) work rate ([0074], table 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process data of Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using measured independent variables including a combination of a) to e) to provide: wherein the process data are a combination of a) to e). Doing so would have a reasonable expectation of successfully improving real time processing and calculation of known process parameters of the ethylene oxide reactor to improve monitoring and diagnosing an operating condition of the reactor (Lahiri, [0008]-[0015]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss in view of Lahiri as applied to claim 4 above, and further in view of Lin et al. (US 20220180029 A1; effectively filed 12/04/2020).
Regarding claim 5, modified Habenschuss fails to teach: wherein the Al algorithm or algorithms, respectively, comprises a combination of artificial neural network and genetic programming.
Lahiri measuring independent variables of the reactor ([0064]) and input and output parameters of ANN based EO reactor model ([0074], table 1). Lahiri teaches studying an EO reaction process and collection of real time data for real time fault diagnosis and interpretation of ANN and PCA outputs (Fig. 10). Lahiri teaches building an ANN model to construct process outputs, including catalyst selectivity ([0096]-[0097]). Lahiri teaches analysis can be performed automatically suing a programmed computer ([0171]). Lahiri teaches statistical methods are used to find correlation coefficients between catalyst selectivity and other parameters ([0130]). Lahiri teaches a PC loaded with PCA and ANN software, i.e. at least two algorithms, which allow for receiving real time process parameter values from plant from sensors and calculate values in real time, therefore updating plots with fresh real time data ([0168]).Lahiri teaches ANN are computer algorithms, i.e. at least two algorithms, ([0007]), which have emerged as useful tools for modeling, is capable of adapting to a changing environment, and capable of dealing with uncertainties, noisy data, and non-linear relationships ([0008]). Lahiri teaches ANN modeling methods have various advantageous and attractive characteristics for dynamic process modeling ([0009]-[0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using algorithms such as ANN and PCA to allow for real time processing and calculation of process parameters to provide: wherein the Al algorithm or algorithms, respectively, comprises an artificial neural network. Doing so would have a reasonable expectation of successfully improving real time processing and calculation of process parameters of the ethylene oxide reactor using algorithms that have shown advantageous for dynamic process modeling (Lahiri, [0008]-[0015]).
Modified Habenschuss fails to teach: wherein the Al algorithm or algorithms, respectively, comprises a combination of artificial neural network and genetic programming.
Lin teaches embodiments of generating values for property parameters, including obtaining values for samples, generating at least one model using property parameters, and using the at least one model for generating a value for another property parameter (abstract). Lin teaches generating the at least one model includes using a machine learning algorithm to search for at least one best fit model including genetic algorithm and genetic programming ([0070]). Lin teaches generating the at least one model comprises using a machine learning algorithm to perform regression, including neural network ([0071]). Lin teaches methods and systems may use one or more machine learning algorithms ([0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the algorithms of modified Habenschuss to incorporate Lin’s teachings of known machine learning algorithms including neural network and genetic programming to provide: wherein the Al algorithm or algorithms, respectively, comprises a combination of artificial neural network and genetic programming. Doing so would have a reasonable expectation of successfully improving modeling, processing, and analysis of parameters of the reactor.
Claims 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss as applied to claim 1 above, and further in view of Lin et al. (US 20220180029 A1; effectively filed 12/04/2020).
Regarding claim 6, Habenschuss fails to teach: wherein - Determining a parametric coefficient from the acquired process data is performed by applying the acquired process data to a genetic programming model and/or a kinetic based detail phenomenological model.
Lin teaches embodiments of generating values for property parameters, including obtaining values for samples, generating at least one model using property parameters, and using the at least one model for generating a value for another property parameter (abstract). Lin teaches generating the at least one model includes using a machine learning algorithm to search for at least one best fit model including genetic algorithm and genetic programming ([0070]). Lin teaches generating the at least one model comprises using a machine learning algorithm to perform regression, including neural network ([0071]). Lin teaches methods and systems may use one or more machine learning algorithms ([0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Habenschuss to incorporate Lin’s teachings of known machine learning algorithms including neural network and genetic programming to provide: wherein - Determining a parametric coefficient from the acquired process data is performed by applying the acquired process data to a genetic programming model. Doing so would have a reasonable expectation of successfully improving modeling, processing, and analysis of parameters of the reactor.
Regarding claim 10, Habenschuss further teaches the method according to claim 1 further comprising the step of
- comparing the selectivity and/or activity of the catalyst to a predefined reference ([0114] teaches table 2 provides comparison when the reaction is operated at maximum efficiency to the preferred operating condition of Fig. 5, i.e. predefined reference).
Habenschuss fails to teach:
- automatically generating a signal representing a from the parametric coefficient a genetic programming by using sensor data of the process for ethylene production.
Lin teaches embodiments of generating values for property parameters, including obtaining values for samples, generating at least one model using property parameters, and using the at least one model for generating a value for another property parameter (abstract). Lin teaches generating the at least one model includes using a machine learning algorithm to search for at least one best fit model including genetic algorithm and genetic programming ([0070]). Lin teaches generating the at least one model comprises using a machine learning algorithm to perform regression, including neural network ([0071]). Lin teaches methods and systems may use one or more machine learning algorithms ([0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Habenschuss to incorporate Lin’s teachings of known machine learning algorithms including neural network and genetic programming to provide: automatically generating a signal representing a from the parametric coefficient a genetic programming by using sensor data of the process for ethylene production. Doing so would have a reasonable expectation of successfully improving modeling, processing, and analysis of the parametric coefficient of the reactor.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss as applied to claim 1 above, and further in view of Lahiri et al. (LAHIRI, S. et al., "Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution", International Journal of Chemical Reactor Engineering, January 2010, pp. 1-28, vol. 8, no. 1; cited in the IDS filed 12/16/2024; Herein, “Lahiri 2010”) and Broekhuis et al. (US 20230097182 A1; effectively filed 03/09/2020).
Regarding claim 7, Habenschuss fails to teach: wherein the Determining a parametric coefficient from the acquired process data is performed by applying the acquired process data to a combination of a genetic programming model and a second, in particular a first principle-based kinetic, model of the process.
Lahiri 2010 teaches a combination of models of a new hybrid artificial neural network and differential evolution technique, i.e. genetic programming model, for efficient tuning of ANN meta parameters for modeling of commercial ethylene oxide reactor (abstract). Lahiri 2010 teaches differential evolution is an improved version of genetic algorithm that is significantly faster and robust at numerical optimization (page 10, second paragraph). Lahiri 2010 teaches a comprehensive reactor model is expected take into account the various subjects, such as chemistry, chemical reaction and kinetics, catalysis and physics which consequently become very complex (page 16, third full paragraph).
Broekhuis teaches an ethylene oxide production process including a first reactor system (abstract). Broekhuis teaches a mathematical model used reaction kinetics considerations, i.e. first principle-based kinetic model ([0108]). Broekhuis teaches net ethylene selectivity was about 97% ([0108]). Broekhuis teaches a reactor model used reaction kinetics and calculated profiles of temperature, conversion, and selectivity are shown in FIG. 4 ([0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Habenschuss to incorporate Lahiri 2010’s teachings of hybrid models including genetic programming (abstract; 10, second paragraph) and a model taking into account of kinetics and physics (page 16, third full paragraph) and Broekhuis’s teachings of an ethylene oxide production process (abstract) and using mathematical model that used reaction kinetics considerations for calculations ([0108]-[0109]) to provide: wherein the Determining a parametric coefficient from the acquired process data is performed by applying the acquired process data to a combination of a genetic programming model and a second, in particular a first principle-based kinetic, model of the process. Doing so would have a reasonable expectation of successfully improving and tuning modeling of the ethylene oxide production.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss and Lahiri 2010 and Broekhuis as applied to claim 7 above, and further in view of Agrafiotis et al. (US 20030014191 A1).
Regarding claim 8, modified Habenschuss further teaches wherein the model is a first principle-based kinetic model (see above claim 7; modified Habenschuss teaches a first principle-based kinetic, model). Modified Habenschuss fails to teach: a prediction error of the model is minimized by an artificial intelligence-based data driven model.
Lahiri 2010 teaches a properly trained model possesses excellent generalization ability owing to which it can accurately predict outputs for a new input data set (page 1, second paragraph). Lahiri 2010 teaches it becomes necessary to employ a heuristic procedure involving multiple training runs; and parameters including error minimization algorithm (page 2, first paragraph).
Agrafiotis teaches an automatic system, method, and computer program for generating chemical entities (abstract). Agrafiotis teaches process of minimizing the Prediction Error shall hereafter be referred to as Training ([0142]). Agrafiotis teaches one embodiment including a neural network model structure, which includes layers of the neural network so that the overall prediction error is minimized; wherein many variants of such training algorithms have been reported, and are well known to those skilled in the art ([0161]). Agrafiotis teaches a process is repeated until the prediction error for an entire training set is minimized ([0172]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Habenschuss to incorporate Lahiri 2010’s teachings of properly training models and an error minimization algorithm (page 1, second paragraph; 2, first paragraph) and Agrafiotis’s teachings of training to minimize prediction error using neural networks ([0142],[0161],[0172]) to provide: a prediction error of the model is minimized by an artificial intelligence-based data driven model. Doing so would have a reasonable expectation of successfully improving prediction and calculation of outputs and minimization of errors.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss as applied to claim 1 above, and further in view of Lahiri et al. (US 20140365195 A1) and Lahiri et al. (LAHIRI, S. et al., "Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution", International Journal of Chemical Reactor Engineering, January 2010, pp. 1-28, vol. 8, no. 1; cited in the IDS filed 12/16/2024; Herein, “Lahiri 2010”).
Regarding claim 11, Habenschuss fails to teach the method according to claim 1 further comprising the step of
- Validating the current chloride status of ethylene oxide reactor by a plant operation experience based heuristic rules along with genetic programming calculations.
Lahiri teaches early detection and diagnosis of an abnormal event in an operating plant is very important for ensuring plant safety and for maintaining plant quality; and measurements bring in useful signatures about the status of the plant operation ([0004]). Lahiri teaches it is very important to operate the chloride level in optimum chloride zone ([0125]).
Lahiri 2010 teaches a combination of models of a new hybrid artificial neural network and differential evolution technique, i.e. genetic programming model, for efficient tuning of ANN meta parameters for modeling of commercial ethylene oxide reactor (abstract). Lahiri 2010 teaches differential evolution is an improved version of genetic algorithm that is significantly faster and robust at numerical optimization (page 10, second paragraph). Lahiri 2010 teaches a comprehensive reactor model is expected take into account the various subjects, such as chemistry, chemical reaction and kinetics, catalysis and physics which consequently become very complex (page 16, third full paragraph). Lahiri teaches ANN performance is assessed by validation (page 11, first paragraph; page 16, last paragraph). Lahiri 2010 teaches a properly trained model possesses excellent generalization ability owing to which it can accurately predict outputs for a new input data set (page 1, second paragraph). Lahiri 2010 teaches it becomes necessary to employ a heuristic procedure involving multiple training runs to obtain an optimal ANN model whose parameters corresponds to the global or the deepest local minimum of the error function (page 2, first paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Habenschuss to incorporate Lahiri’s teachings of early detection and diagnosis of the status of a plant operation ([0004]) and importance of operating at a chloride level in an optimum chloride zone ([0125]) and Lahiri 2010’s teachings of genetic programming (abstract; page 10, second paragraph) and employing a heuristic procedure involving multiple training runs to obtain an optimal ANN model whose parameters corresponds to the global or the deepest local minimum of the error function (page 2, first paragraph) to provide: the method according to claim 1 further comprising the step of - Validating the current chloride status of ethylene oxide reactor by a plant operation experience based heuristic rules along with genetic programming calculations. Doing so would have a reasonable expectation of successfully improving training and validation of the method, therefore improving ensuring plant safety and for maintaining plant quality.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Habenschuss as applied to claim 1 above, and further in view of Lahiri et al. (US 20140365195 A1) and Lin et al. (US 20220180029 A1; effectively filed 12/04/2020).
Regarding claim 12, Habenschuss fails to teach the method according to claim 1 further comprising the step of
- training a genetic programming by using sensor data of the process for ethylene production.
Lahiri teaches studying an EO reaction process and collection of real time data for real time fault diagnosis and interpretation of ANN and PCA outputs (Fig. 10). Lahiri teaches building an ANN model to construct process outputs, including catalyst selectivity ([0096]-[0097]). Lahiri teaches analysis can be performed automatically suing a programmed computer ([0171]). Lahiri teaches statistical methods are used to find correlation coefficients between catalyst selectivity and other parameters ([0130]). Lahiri teaches a PC loaded with PCA and ANN software, i.e. at least two algorithms, which allow for receiving real time process parameter values from plant from sensors and calculate values in real time, therefore updating plots with fresh real time data ([0168]). Lahiri teaches ANN are computer algorithms, i.e. at least two algorithms, ([0007]), which have emerged as useful tools for modeling, is capable of adapting to a changing environment, and capable of dealing with uncertainties, noisy data, and non-linear relationships ([0008]). Lahiri teaches ANN modeling methods have various advantageous and attractive characteristics for dynamic process modeling ([0009]-[0015]), specifically a properly trained model can be generalized easily due to its capability to accurately predict outputs for a new input data set ([0013]). Lahiri teaches ANN models are trained with steady state hourly average data when plant is running normal and smooth ([0140]).
Lin teaches embodiments of generating values for property parameters, including obtaining values for samples, generating at least one model using property parameters, and using the at least one model for generating a value for another property parameter (abstract). Lin teaches generating the at least one model includes using a machine learning algorithm to search for at least one best fit model including genetic algorithm and genetic programming ([0070]). Lin teaches generating the at least one model comprises using a machine learning algorithm to perform regression, including neural network ([0071]). Lin teaches methods and systems may use one or more machine learning algorithms ([0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Habenschuss to incorporate Lahri’s teachings of monitoring and diagnosing a process of ethylene oxide production using algorithms such as ANN and PCA to allow for real time processing and calculation of process parameters and training models and Lin’s teachings of known machine learning algorithms including neural network and genetic programming to provide: the method according to claim 1 further comprising the step of - training a genetic programming by using sensor data of the process for ethylene production. Doing so would have a reasonable expectation of successfully improving real time processing and calculation of process parameters of the ethylene oxide reactor using algorithms that have shown advantageous for dynamic process modeling (Lahiri, [0008]-[0015]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kent et al. (US 20200074307 A1) teaches systems and methods for performing assessing, optimizing, and controlling performance of an anaerobic digestion plant (abstract). Kent teaches the simulation engine is configured to use a genetic algorithm ([0015]). Kent teaches using a genetic algorithm for optimization ([0060]). Kent teaches the predicted operational results of the neural network model can then be compared to the actual real-time data obtained from a given AD plant to tune the neural network model and improve its performance ([0067]).
Johnson et al. (US 20220208297 A1; effectively filed 03/29/2019) teaches a computational method of modeling a bioreactor combines mechanistic models of kinetics to predict cell culture performance (abstract).
Terrazas-Moreno et al. (US 20220366360 A1; effectively filed 04/30/2021) teaches embodiments that control industrial supply chains, including models (abstract). Terrazas-Moreno teaches using a given input-output model and fitting parameters to a first-principles engineering model, and (iii) processing a given input-output model using at least one of statistics, machine learning, and artificial intelligence ([0007]). Terrazas-Moreno teaches using a given input-output model and fitting parameters to a first-principles engineering model (e.g., kinetic equations, and heat and mass transfer equations in a chemical reactor) ([0031]).
Bruckhaus et al. (US 8417715 B1) teaches methods and systems for generating and delivering analytical results (abstract). Bruckhaus teaches with respect to model derivation, each model is derived from a precursor model by adding or deleting inputs, adjusting algorithm parameters, changing data that was used for training and validation, and the method that was used to select these specific changes, such as applying a genetic algorithm, performing feature selection, randomly varying model parameters, specific heuristics for selecting inputs and algorithms, and other similar methods for evolving models (column 35, line 64 – Column 36, line 5).
Nandi et al. (Nandi et al., “Reaction modeling and optimization using neural networks and genetic algorithms: Case study involving TS-1-catalyzed hydroxylation of benzene”, Ind. Eng. Chem. Res. 2002, 41, 2159-2169) teaches a hybrid process modeling and optimization formalism integrating artificial neural networks (ANNs) and genetic algorithms (GAs) that allows process modeling and optimization exclusively on the basis of process input-output data (abstract). Nandi teaches: this model has the inputs describing process operating parameters and variables (reactant/ catalyst concentration, temperature, pressure, etc.) and its outputs representing process output variables (con version, selectivity, etc.); In the second step of the ANN-GA procedure, the input space of the ANN model is optimized using a GA such that the optimized process inputs result in the enhanced values of the output variables page 2160, right column, first full paragraph).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H NGUYEN whose telephone number is (571)272-2338. The examiner can normally be reached M-F 7:30A-5:00P.
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/HENRY H NGUYEN/Primary Examiner, Art Unit 1758
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