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 Status
Claims 1-17 are pending and examined.
Information Disclosure Statement
The information disclosure statement (IDS) received on 12/16/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
The specification is replete with grammatical and syntax errors which make the content therein difficult to understand. Examples include:
Pg. 1, Lns. 15-16 of the instant Specification recites, “Business environment has encountered a drastic transformation in this decade due to stiff global competition”, which should read, “Business environments have encountered a drastic transformation in this decade due to stiff global competition” to be grammatically correct.
Pg. 1, Lns. 24-25 of the instant Specification recites, “In search of innovative ways to generate more profit, chemical reactor attracts the focus of plant managers and researchers”, which should read, “In search of innovative ways to generate more profit, chemical reactors attract the focus of plant managers and researchers” to be grammatically correct.
Pg. 2 Ln. 4 of the instant Specification recites, “Data is considered as new oil in current time”. However, it is unclear what this expression means.
Pg. 2 Lns. 29-31 of the instant Specification recites, “To tackle the above challenges US 9,892,238 B2 found a technique to detecting abnormal events in an ethylene oxide reaction process using multivariate statistical techniques and artificial neural network”, which should read, “To tackle the above challenges US 9,892,238 B2 found a technique to detect abnormal events in an ethylene oxide reaction process using multivariate statistical techniques and artificial neural network” to be grammatically correct.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “digital data storage means” in claim 5.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-17 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.
Claim 1 recites the limitation "the process property" in the Last Ln. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-17 are rejected at least for depending on claim 1.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 3 recites the broad recitation “an observable reaction of the running process”, and the claim also recites “in particular a change in internal reactor data, in particular -an age of a catalyst…and/or a change in external reactor data, in particular – in a scrubber…” which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 4 is similarly rejected for reciting “an input action for controlling the reactor, in particular – heat input…”.
Claim 6 is similarly rejected for reciting “wherein the history is recorded, and in particular evaluated, over a time span…” and “of at least one week, preferably at least one month and particularly preferably at least one year…”.
Regarding claim 9, the claim recites, “wherein the…applying the acquired process data…”. There is insufficient antecedent basis for this limitation in the claim, as claim 1 does not recite applying the acquired process data.
Claim 12 is similarly rejected for reciting “Saturated hydrocarbon inlet concentration, in particular ethane inlet concentration…” and “CO2 inlet concentration and/or the oxygen inlet concentration, in particular both the CO2 and oxygen inlet concentrations”.
Claims 13-17 are rejected for depending on a rejected claim.
Claim 3 Ln. 1 recites, “the process data change”. However, it is unclear if this process data change is referring to the process data change recited in the preamble, the history representing process data changes recited in the “Recording a history” clause, or the “a first process data change” recited in the “Analyzing the history” clause of claim 1. Further clarification is needed.
Claim 4 Ln. 1 recites, “the parameter change”. However, it is unclear if this parameter change is referring to the parameter change recited in the preamble, the history representing process data changes in connection with parameter changes recited in the “Recording a history clause”, or one of the “a first parameter change” and/or “a first and a second parameter change” recited in the “Analyzing the history” clause of claim 1. Further clarification is needed.
Claim Rejections - 35 USC § 101
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The subject matter eligibility test for the claims is shown below:
Step 1: Claim 1 is directed toward a method, which is a statutory category.
Step 2A, Prong One: Identify the law of nature/natural phenomenon/abstract idea.
Claim 1 recites analyzing a history of process data changes in connection with parameter changes regarding a delay of a first process data change with respect to a first parameter change and/or a delay of the first process data change with respect to a first and second parameter data change, and determining a future point in time based on the analysis and acquired process data when a change of a parameter of a process will have a certain impact on a property of the process. These acts are a determination/evaluation-type mental process that can be practically performed in the human mind, particularly as the steps are performed at a high level of generality, and do not recite a specialized computer for performing the mental processes. Evaluation/determination-type mental processes are abstract ideas.
Step 2A, Prong Two: Has the abstract idea been integrated into a particular practical application?
No. The claim additionally recites recording a history representing process data changes in connection with parameter changes of a real ethylene oxide reactor over time. This is mere data-gathering that is necessary order to perform the remainder of the claimed method steps, and is therefore insignificant extra-solution activity. The claim further recites acquiring process data of a reactor producing ethylene oxide. This is also mere data-gathering that is necessary to perform the remainder of the claims method steps, and is therefore insignificant extra-solution activity. See MPEP 2106.05(g). The claim further recites that the analyzing of the history is performed using an artificial neural network model. A model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitation “an artificial neural network model” is recited at a high level of generality, and amounts to a generic computer, where the computer is used as a tool to perform the concept of analyzing the history, i.e. the mental process-type abstract idea. See MPEP 2106.05(f).
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
As previously stated in Step 2A Prong Two, Claim 1 additionally recites recording a history representing process data changes in connection with parameter changes of a real ethylene oxide reactor over time, and acquiring process data of a reactor producing ethylene oxide. The steps of recording a history and acquiring process data are mere data-gathering that is necessary in order to perform the remainder of the claimed method steps, and are therefore insignificant extra-solution activity. See MPEP 2106.05(g). Further, the claim recites an artificial neural network model to perform the analyzing step (a mental process-type abstract idea), where the artificial neural network is used as a generic computer to apply the abstract idea. See MPEP 2016.05(f). Claim 1 recites the additional element of an artificial neural network model, and a reactor producing ethylene oxide. However, the reactor producing ethylene oxide amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Further, both an artificial neural network and a process for ethylene oxide production are well-understood, routine, and conventional activity that is previously known to the industry. See MPEP 2106.05(d).
Further, with regards to the generically recited artificial neural network model and reactor producing ethylene oxide, the following prior art is relied upon to show that the above element is well-understood, routine, and conventional:
Wells et al. (US Pub. No. 2024/0150307; hereinafter Wells) teaches a reactor producing ethylene oxide ([0034]).
Lee et al. (US Pub. No. 2024/0279193; hereinafter Lee) teaches a reactor producing ethylene oxide ([0001], [0063]).
Lahiri et al., “Model of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution”, 2010, International Journal of Chemical Reactor Engineering, Vol. 8, Pgs. 1-28 (hereinafter Lahiri; already of record on the IDS received 12/16/2024) teaches a reactor producing ethylene oxide (see Pgs. 4-6 at 1. Introduction, Pgs. 12-13 at 3. Differential Evolution (DE) at a glance, and Pgs. 15-22 at 5. Case study: Study of industrial ethylene oxide reactor) and an artificial neural network model (Pg. 13 at 1st Para., Pg. 19 at 2nd to Last Para., the algorithm uses a combination of an artificial neural network and differential evolution).
Yang et al., “A novel convolutional neural network based approach to predictions of process dynamic time delay sequences”, 2018, Chemometrics and Intelligent Laboratory Systems, Vol. 174, Pgs. 56-61 (hereinafter Yang) teaches an artificial neural network model (Pg. 56 at Abstract).
Claim 2 recites the specifics of how the determination of the future point in time is performed, by an artificial neural network model. A model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitation “an artificial neural network model” is recited at a high level of generality, and amounts to a generic computer, where the computer is used as a tool to perform the concept of predicting the future value, i.e. the mental process-type abstract idea.
Claims 3 and 4 relate to the specifics of the process data change and the parameter change. The process data change and parameter change are either related to data-gathering (if the process data change and parameter change or part of the recorded history), and are insignificant extra-solution activity, or further define the abstract idea judicial exception (if the process data change and parameter change are related to analyzing the history).
Claims 5-6 relate to how the history is recorded, and are drawn to data-gathering, which is insignificant extra-solution activity.
Claims 7-9 are drawn to the specifics of models that may be used to analyze the history and/or determine the future point in time, or apply the acquired process data. A model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitations “partial correlation co-efficient methodology”, “a combination of an artificial neural network and genetic programming”, “two artificial neural network models”, “an artificial neural network model and a kinetic based detail phenomenological model”, and “a first principle-based kinetic model of the process” are recited at a high level of generality, and amount to a generic computer, where the computer is used as a tool to perform the concept of analyze the history and/or determine the future point in time, or apply the acquired process data, i.e. the mental process-type abstract idea. Further, the step of applying the acquired process data amounts to the term “apply it”, and does not integrate the judicial exception into a practical application or recite significantly more than the judicial exception. See MPEP 2106.05(f).
Claim 10 recites training a genetic programming by using sensor data. A model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitation “a genetic programming” is recited at a high level of generality, and amounts to a generic computer. Further, the genetic programming that is trained does not tie into the remainder of the method, and therefore does not amount to a particular practical application of the judicial exception, or significantly more than the judicial exception.
Claim 11 recites further details on the artificial neural network model used to analyze the history. A model is just an algorithm or a series of mental steps that are performed by a computer. A claim that requires a computer may still recite a mental process, particularly as the recitation “a first principle-based kinetic model and a prediction error of the model is minimized by an artificial intelligence-based data driven model” is recited at a high level of generality, and amounts to a generic computer, where the computer is used as a tool to perform the concept of predicting the future value, i.e. the mental process-type abstract idea.
Claims 12-17 further define what the process data comprises, and merely further describe the data-gathering, which is insignificant extra-solution activity.
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 1-7, 10, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Lahiri in view of Yang.
Regarding claim 1, Lahiri discloses a method of modelling a process of producing ethylene oxide in a reactor (see Pgs. 4-6 at 1. Introduction, Pgs. 12-13 at 3. Differential Evolution (DE) at a glance, and Pgs. 15-22 at 5. Case study: Study of industrial ethylene oxide reactor). The method comprises the steps:
- Recording a history representing process data changes in connection with parameter changes of a real ethylene oxide reactor over time (Pg. 19 2nd to Last Para., Pg. 20 Last Para., see Pg. 21 at Table 2).
-Analyzing the history by means of an artificial neural network model (Pg. 13 at 1st Para., Pg. 19 2nd to Last Para., the algorithm uses a combination of an artificial neural network and differential evolution, Pg. 20 Last Para., see Pg. 21 at Table 2).
-Acquiring process data of a reactor producing ethylene oxide (Pg. 19 2nd to Last Para., Pg. 20 Last Para., see Pg. 21 at Table 2).
Lahiri fails to explicitly disclose:
That the method is of estimating a delay of a process data change with respect to a parameter change of the process of producing ethylene oxide in the reactor;
The history is analyzed regarding a delay of a first process data change with respect to a first parameter change and/or a delay of the first process data change with respect to a first and a second parameter change; and
Determining a future point in time based on the analysis and the acquired process data, when a change of a parameter of the running process will have a certain impact on the process property.
Yang is in the analogous field of process systems (Yang; Pg. 56 at Abstract, Pg. 57 Col. 1 3rd Para.). Yang teaches a method of estimating a delay of a process data change with respect to a parameter change of a reactor process (Yang; Pg. 56 at Abstract, see Pg. 57 at Fig. 1, Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.), analyzing history regarding a delay of a first process data change with respect to a first parameter change (Yang; Pg. 56 at Abstract, see Pg. 57 at Fig. 1, Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.), and determining a future point in time based on the analysis and acquired process data, when a change of a parameter on a running process will have a certain impact on a process property (Yang; Pg. 56 at Abstract, see Pg. 57 at Fig. 1, Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.). The determination of the future point in time is performed by means of an artificial neural network model (Yang; Pg. 56 at Abstract, see Pg. 57 at Fig. 1, Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Lahiri with the teachings of Yang so that the method is of estimating a delay of a process data change with respect to a parameter change of the process of producing ethylene oxide in the reactor; the history is analyzed regarding a delay of a first process data change with respect to a first parameter change and/or a delay of the first process data change with respect to a first and a second parameter change; and determining a future point in time based on the analysis and the acquired process data, when a change of a parameter of the running process will have a certain impact on the process property, in order to obtain highly accurate time lag predictions for a chemical process in real time (Yang; Pg. 56 Col. 2 1st Para., Pg. 60 Col. 2 Last Para-Pg. 61 Col. 1 1st Para.), thereby allowing process variables to be tracked more accurately in an industrial process. Further, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Lahiri with the teachings of Yang so that the determination of the future point in time is performed by means of an artificial neural network model, as Yang teaches that an artificial neural network model can be used to predict a time delay sequence (Yang Pg. 56 at Abstract).
Regarding claim 2, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses that the determination of the future point in time is performed by means of an artificial neural network model (see Claim 1 above at Yang teaching the artificial neural network model in Pg. 56 at Abstract, see Pg. 57 at Fig. 1, Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.).
Regarding claim 3, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses that the process data change represents an observable reaction of the running process, in particular a change in internal reactor data, in particular
- an age of a catalyst and/or
- a selectivity of a catalyst and/or
- a temperature and/or
- a pressure and/or
- inlet moisture and/or
- inlet ethylene oxide (EO) concentration and/or
- an ethane concentration and/or
a change in external reactor data, in particular
- in a scrubber and/or
- a CO2-Removal Unit (Lahiri; Pg. 19 2nd to Last Para., Pg. 20 Last Para., see Pg. 21 at Table 2).
Further, even if Lahiri is not considered to teach that the process data change represents an observable reaction of the running process as claimed, Yang further teaches that the process data change represents an observable reaction of the running process as claimed (see Yang Pg. 60 at Table 1). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the further teachings of Yang so that the process data change represents an observable reaction of the running process as claimed, in order to more accurately track the claimed variables throughout a chemical production process, in order to obtain highly accurate time lag predictions for a chemical process in real time (Yang; Pg. 56 Col. 2 1st Para., Pg. 60 Col. 2 Last Para-Pg. 61 Col. 1 1st Para.), thereby allowing process variables to be tracked more accurately in an industrial process.
Regarding claim 4, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses that the parameter change represents an input action for controlling the reactor, in particular - heat input and/or - addition of a component and/or - a flow rate of a component or educt and/or - a ratio of a component or educt and/or - concentration of a component or educt (Lahiri; Pg. 19 2nd to Last Para., Pg. 20 Last Para., see Pg. 21 at Table 2).
Further, even if Lahiri is not considered to teach that the parameter change represents an input action for controlling the reactor, in particular - heat input and/or - addition of a component and/or - a flow rate of a component or educt and/or - a ratio of a component or educt and/or - concentration of a component or educt, Yang further teaches that the parameter change represents an input action for controlling the reactor, in particular - heat input and/or - addition of a component and/or - a flow rate of a component or educt and/or - a ratio of a component or educt and/or - concentration of a component or educt (see Yang Pg. 60 at Table 1). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the further teachings of Yang so that parameter change represents an input action for controlling the reactor, in particular - heat input and/or - addition of a component and/or - a flow rate of a component or educt and/or - a ratio of a component or educt and/or - concentration of a component or educt, in order to more accurately track how variables are affected by changes in the claimed parameters throughout a chemical production process, in order to obtain highly accurate time lag predictions for a chemical process in real time (Yang; Pg. 56 Col. 2 1st Para., Pg. 60 Col. 2 Last Para-Pg. 61 Col. 1 1st Para.), thereby allowing process variables to be tracked more accurately in an industrial process.
Regarding claim 5, modified Lahiri discloses the method according to claim 1.
Modified Lahiri fails to explicitly disclose that the history is automatically recorded in a digital data storage means.
However, Lahiri teaches storing data (Lahiri Pg. 19 2nd to Last Para.). it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to automatically record the history in a digital data storage. The motivation would have been to ensure that the history is safely recorded in an easily accessible format for speedy processing.
Regarding claim 6, modified Lahiri discloses the method according to claim 1.
Modified Lahiri fails to explicitly disclose that the history is recorded, and in particular evaluated, over a time span - of at least one week, preferably at least one month and particularly preferably at least one year, and/or - since a first ethylene oxide process performed by the reactor or since a most recent structural modification of the reactor or since a hardware update of the reactor.
However, Lahiri teaches recording/evaluating history over an extended time period (Lahiri Pg. 19 2nd to Last Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention record/evaluate the history over a time span of at least one week, in order to ensure that the amount of data is over a broad enough period of time to reduce the potential for outlier data, e.g. periods of abnormal operation within the reactor, by including data from a broad range of time.
Regarding claim 7, modified Lahiri discloses the method according to claim 1.
Modified Lahiri further discloses that the step of analyzing the history and/or the step of determining the future point in time comprises using
- partial correlation co-efficient methodology and/or
- a combination of an artificial neural network and genetic programming and/or
- two artificial neural network models on the acquired process data (Lahiri; Pg. 13 at 1st Para., Pg. 19 2nd to Last Para., the algorithm uses a combination of an artificial neural network and differential evolution, Pg. 20 Last Para., see Pg. 21 at Table 2).
Regarding claim 10, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses training an artificial neural network model by using sensor data of the process for ethylene production (Lahiri; Pg. 19 at 2nd to Last to Last Paras., the algorithm uses a combination of an artificial neural network and differential evolution, see also Pg. 21 at Table 2).
Regarding claim 12, modified Lahiri discloses the method according claim 1. Modified Lahiri further discloses that the process data comprise or consist of at least one of:
a) Total inlet chloride moderator concentration
b) Saturated hydrocarbon inlet concentration, in particular ethane inlet concentration
c) CO2 inlet concentration and/or the oxygen inlet concentration, in particular both the CO2 and oxygen inlet concentrations
d) Moisture (H2O) inlet concentration
e) Work Rate
f) C2H4 (ethylene) inlet concentration and/or ethylene oxide inlet concentration (see Lahiri Pg. 21 at Table 2, which lists ethane inlet concentration, carbon dioxide concentration, and inlet concentration as input variables for the ANN).
Regarding claim 13, modified Lahiri discloses the method according to claim 12, wherein the process data are a combination of one or more of input variables a) to f) (see Lahiri Pg. 21 at Table 2, which lists ethane inlet concentration, carbon dioxide concentration, and inlet concentration as input variables for the ANN).
Claims 8-9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lahiri in view of Yang as applied to claims 1-7, 10, and 12-13 above, and further in view of Wells.
Regarding claim 8, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses that the analysis of the history of process data changes is performed by an artificial neural network model (Lahiri; Pg. 13 at 1st Para., Pg. 19 2nd to Last Para., the algorithm uses a combination of an artificial neural network and differential evolution, Pg. 20 Last Para., see Pg. 21 at Table 2).
Modified Lahiri fails to explicitly disclose that the analysis of the history of process data changes is performed by a kinetic based detail phenomenological model.
Wells is in the analogous field of ethylene oxide production (Wells [0034]). Wells teaches a kinetic based detail phenomenological model (Wells [0140]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Wells so that the analysis of the history of process data changes is performed by a kinetic based detail phenomenological model, as Wells teaches that a kinetic model can be used to model the performance of an ethylene oxide production system (Wells [0140]), and would benefit from the error minimizing taught by Lahiri.
Regarding claim 9, modified Lahiri discloses the method according to claim 1, and all limitations recited therein.
Modified Lahiri fails to explicitly disclose that the analysis of the history of process data changes and/or applying the acquired process data involves a first principle-based kinetic model of the process.
Wells teaches applying acquired process data using a first principle-based kinetic model of a process (Wells [0140]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Wells so that the applying the acquired process data involves a first principle-based kinetic model of the process, as Wells teaches that a kinetic model can be used to model the performance of an ethylene oxide production system (Wells [0140]), and would benefit from the error minimizing taught by Lahiri.
Regarding claim 11, modified Lahiri discloses the method according to claim 1. Modified Lahiri further discloses that a prediction error of the model is minimized by an artificial intelligence-based data driven model (Lahiri Pg. 25 1st Para.).
Further, even if Lahiri is not considered to teach that a prediction error of the model is minimized by an artificial intelligence-based data driven model, Yang further teaches that a prediction error of a model is minimized by an artificial intelligence-based data driven model (Yang Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the further teachings of Yang so that the prediction error of the model is minimized by an artificial intelligence-based data driven model, in order to effectively improve the prediction accuracy (Yang Pg. 59 Col. 2 Last Para.-Pg. 60 Col. 2 1st Para.).
Modified Lahiri fails to explicitly disclose that the model is a first principle-based kinetic model.
Wells teaches a first principle-based kinetic model (Wells [0140]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Wells so that the model is a first principle-based kinetic model, as Wells teaches that a kinetic model can be used to model the performance of an ethylene oxide production system (Wells [0140]), and would benefit from the error minimizing taught by Lahiri/Yang.
Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lahiri in view of Yang as applied to claims 1-7, 10, and 12-13 above, and further in view of Lee.
Regarding claim 14, modified Lahiri discloses the method according to claim 12.
Modified Lahiri fails to explicitly disclose that the process data are a combination of a) and b).
Lee is in the analogous field of ethylene oxide production (Lee [0001]). Lee teaches inlet chloride moderator concentration (Lee [0010]), ethane inlet concentration (Lee [0064]), carbon dioxide and oxygen inlet concentrations (Lee; [0073], [0075]), moisture inlet concentration (Lee [0075]), work rate (Lee [0130]), and ethylene inlet concentration (Lee [0074]) as variables for optimizing ethylene oxide production. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Lee so that the process data are a combination of a) to f), which thereby includes a) and b), as these variables can be used to optimize ethylene oxide production (Lee; [0010], [0065], [0073]-[0075], [0130]).
Regarding claim 15, modified Lahiri discloses the method according to claim 12.
Modified Lahiri fails to explicitly disclose that the process data are a combination of a) to c).
Lee teaches inlet chloride moderator concentration (Lee [0010]), ethane inlet concentration (Lee [0064]), carbon dioxide and oxygen inlet concentrations (Lee; [0073], [0075]), moisture inlet concentration (Lee [0075]), work rate (Lee [0130]), and ethylene inlet concentration (Lee [0074]) as variables for optimizing ethylene oxide production. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Lee so that the process data are a combination of a) to f), which thereby includes a) to c), as these variables can be used to optimize ethylene oxide production (Lee; [0010], [0065], [0073]-[0075], [0130]).
Regarding claim 16, modified Lahiri discloses the method according to claim 12.
Modified Lahiri fails to explicitly disclose that the process data are a combination of a) to d).
Lee teaches inlet chloride moderator concentration (Lee [0010]), ethane inlet concentration (Lee [0064]), carbon dioxide and oxygen inlet concentrations (Lee; [0073], [0075]), moisture inlet concentration (Lee [0075]), work rate (Lee [0130]), and ethylene inlet concentration (Lee [0074]) as variables for optimizing ethylene oxide production. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Lee so that the process data are a combination of a) to f), which thereby includes a) to d), as these variables can be used to optimize ethylene oxide production (Lee; [0010], [0065], [0073]-[0075], [0130]).
Regarding claim 17, modified Lahiri discloses the method according to claim 12.
Modified Lahiri fails to explicitly disclose that the process data are a combination of a) to e).
Lee teaches inlet chloride moderator concentration (Lee [0010]), ethane inlet concentration (Lee [0064]), carbon dioxide and oxygen inlet concentrations (Lee; [0073], [0075]), moisture inlet concentration (Lee [0075]), work rate (Lee [0130]), and ethylene inlet concentration (Lee [0074]) as variables for optimizing ethylene oxide production. It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of modified Lahiri with the teachings of Lee so that the process data are a combination of a) to f), which thereby includes a) to e), as these variables can be used to optimize ethylene oxide production (Lee; [0010], [0065], [0073]-[0075], [0130]).
Conclusion
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/JOHN MCGUIRK/Primary Examiner, Art Unit 1798
