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 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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “high-level optimizer module” “low-level simulation module” and “aggregator module” in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend 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 avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DEVEREUX US 2017/0242981 A1.
DEVEREUX teaches:
1.A method of material flow optimization in an industrial process by using an integrated optimizing system, the integrated optimizing system comprising:
a high-level optimizer module [system 100 uses an optimizer] describing the material flow by coarse high-level process parameters (x) and including an optimization program for the high-level process parameters (x), the optimization program being dependent on high-level model parameters (A, b, c) and including an objective function subject to constraints; [para. 0039, “In one embodiment, the chemical process simulator system 100 can use mixed-integer nonlinear programming (MINLP) techniques using the set of integer options to identify the optimal combination of operation options of the chemical process facility in an efficient manner. The integer options are variables in MINLP optimization. MINLP combines the combinatorial difficulty of optimizing discrete variable sets with the challenges of handling nonlinear functions. MINLP includes both nonlinear programming (NLP) and mixed-integer linear programming (MILP) as sub-problems.”]
a low-level simulation module for simulating the material flow, the low-level simulation module including a low-level simulation function adapted for obtaining detailed low-level material flow data (F) based on the high-level process parameters (x); [para. 0035 simulated unit operations and simulated process models 232 generates simulated flow stream results 315] and
an aggregator module including an aggregator function adapted for calculating the high-level model parameters (A, b, c) based on the low-level material flow data (F), [para. 0015, “Disclosed chemical process simulation solves the above-described problems with conventional chemical process simulators by providing a chemical process simulation system referred to as a process simulator which stores discrete sets of alternate unit operations in a simulation case of fixed topology. This allows the process simulator to use various optimization methods (e.g., mixed integer non-linear programming (MINLP)) to find process solutions over the space of discrete combinations of alternative operation choices.” (emphasis added.)]
the method including approaching an optimum value of the objective function by iteratively modifying the high-level process parameters (x), wherein an iteration includes:
a) carrying out, by the low-level simulation module, a low-level simulation thereby obtaining the detailed low-level material flow data (F); [Simulator system 100]
b) aggregating, by the aggregator module, the low-level material flow data (F) thereby calculating, from the low-level material flow data (F), aggregated high-level model parameters (fA, fb, fc); [para. 0015; “alternate unit operations” stored in order to use an optimizer] and
c) inputting the aggregated high-level model parameters (fA, fb, fc) into the optimization program. [para. 0015 “MINLP”]
DEVEREUX teaches:
2. The method of material flow optimization according to claim 1, wherein the low- level simulation is carried out based on high-level process parameters selected from the following:
- process parameters (x) obtained in a previous iteration, or
- proxy process parameters (x) iteratively approaching the high-level process parameters (x), wherein the proxy process parameters (x) are further input parameters of the optimization program, and [para. 0039]
wherein the objective function includes a proxy process parameter penalty term penalizing a deviation between the proxy process parameters (x) and the high-level process parameters (x). [para. 0039]
DEVEREUX teaches:
3. The method of material flow optimization according to claim1,wherein the low-level simulation includes a nonlinear model for the process parameters (x). [para. 0039, “The integer options are variables in MINLP optimization. MINLP combines the combinatorial difficulty of optimizing discrete variable sets with the challenges of handling nonlinear functions. MINLP includes both nonlinear programming (NLP) and mixed-integer linear programming (MILP) as sub-problems.”]
DEVEREUX teaches:
4. The method of material flow optimization according to claim1,wherein the aggregator function maps the low-level material flow data (F) onto high-level model parameters (fA, fb, fc). [para. 0015, “Disclosed chemical process simulation solves the above-described problems with conventional chemical process simulators by providing a chemical process simulation system referred to as a process simulator which stores discrete sets of alternate unit operations in a simulation case of fixed topology.”]
DEVEREUX teaches:
5. The method of material flow optimization according to claim 1,wherein the optimization program uses, as the high-level model parameters (A, b, c), respective parameters selected from the following:- the aggregated high-level model parameters (fA(.Z), fb(Z), fc(x)) obtained in step b), or - proxy model parameters (A,b,c) iteratively approaching the aggregated high- level model parameters (fA, fb, fc), whereinthe proxy model parameters (A, b,c) are further input parameters of the optimization program, and wherein the objective function includes a proxy model parameter penalty term penalizing a deviation between the proxy model parameters (A, and the high-level model parameters (fA, fb, fc). [para. 0015, “This allows the process simulator to use various optimization methods (e.g., mixed integer non-linear programming (MINLP)) to find process solutions over the space of discrete combinations of alternative operation choices.”]
DEVEREUX teaches:
6. The method of material flow optimization according to claim 1,wherein the objective function is a function cTx (1),subject to boundary conditions Ax = b (2), wherein c, x are vectors of length n, b is a vector of length m, and A is an mxn matrix. [para. 0015, “This allows the process simulator to use various optimization methods (e.g., mixed integer non-linear programming (MINLP)) to find process solutions over the space of discrete combinations of alternative operation choices.”]
DEVEREUX teaches:
7-. The method of material flow optimization according to claim 6, wherein the optimization program uses, as the high-level model parameters A and c in expressions (1), (2) the aggregated high-level model parameters (fA(Z), fc(x)) obtained in step b). [para. 0015, “This allows the process simulator to use various optimization methods (e.g., mixed integer non-linear programming (MINLP)) to find process solutions over the space of discrete combinations of alternative operation choices.”]
DEVEREUX teaches:
8. The method of material flow optimization according to claim 1,wherein an iteration of the method includes a) carrying out the low-level simulation based on the high-level process parameters (x) obtained by the previous high-level optimization, thereby obtaining the low-level material flow data (F); and c) carrying out a high-level optimization based on the aggregated high-level model parameters (fA, fb, fc) obtained by aggregating the low-level material flow data (F) obtained by the previous low-level simulation, thereby obtaining the high-level process parameters (x). [para. 0015]
DEVEREUX teaches:
9. The method of material flow optimization according to claim 1,wherein the output of the low-level simulation module is used as input to the aggregator module, which outputs the high-level model parameters to be used in the high-level optimizer module as an input for the optimization; and wherein the output of the high-level optimizer module is then fed as an input to the low- level simulation module. [para. 0021-0023]
DEVEREUX teaches:
10. The method of material flow optimization according to claim 1,wherein one or more selected from the group consisting of the proxy process parameter penalty term and/or the proxy model parameter penalty term contains a penalty multiplier p, and wherein the method comprises:i) defining a penalty multiplier p; ii) an inner iterative loop in which the optimum value of the objective function is approached by a high-level optimization code iteratively modifying the high- level process parameters (x); and iii) an outer iterative loop in which the penalty multiplier p is modified depending on an optimizing criterion for the inner loop. [para. 0021-0023; constraints of the mixed integer linear programming]
DEVEREUX teaches:
11. The method of material flow optimization according to claim 1,wherein the system comprises a user interface [GUI 206], and the
method includes - selecting, by an operator, a scenario from a plurality of predetermined scenarios presented by the user interface, [Fig. 4 412 – user selects one combination]
wherein each of the predetermined scenarios include definitions of a plurality of model parameters belonging to the respective scenario, and using the model parameters for the material flow optimization; - selecting, by an operator, a filter from a plurality of predetermined filters presented by the user interface, and using the selected filter for filtering the output of the material flow optimization; and - presenting, by the user interface, an advice proposing one or more preferred actions based on the material flow optimization. [Fig. 4]
Regarding system claims 12-14 and CRM claims 15-18, these claims recite the functions for executing the steps of method claims above and the storage of the steps for method claims above and are rejected on the same grounds and rationale as corresponding claims above.
Conclusion
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/GARY COLLINS/Primary Examiner, Art Unit 2115