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 .
DETAILED ACTION
This action is a non-final First Office Action.
This action is in response to communications filed on 11/28/2022 and 04/29/2022.
Claims 1-20 are pending and have been considered.
Claims 17- 20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter, (software per se).
Claims 1- 20 are rejected under 35 U.S.C. 102 as being anticipated by Tao, ChengCheng, Optimization of cement production and hydration for improved performance, energy conservation, and cost PhD Thesis, U Florida, August 2017
Priority
The application claims priority to the Provisional Application 62/927,533, filed on 10/29/2019. The priority is acknowledged.
Information Disclosure Statement (IDS)
The information disclosure statement (IDS) submitted on 4/29/2022, 08/26/2022 is/are in compliance with the provisions of 37 CFR 1.97.
Specification
The abstract of the disclosure does not commence on a separate sheet in accordance with 37 CFR 1.52(b)(4) and 1.72(b). A new abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text.
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 17-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an integrated modeling computer program. The claim’s scope is therefore software per se – which is non-statutory subject matter (MPEP 2106.03).
One way to overcome this rejection, is to amend the claim to recite a non-transitory computer-readable medium.
Claim Rejections - 35 USC § 102
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 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 that share substantially similar limitations (even though not verbatim) are grouped and analyzed together; the analysis is done on the claim with most comprehensive limitations.
Claims 1-12 are rejected under 35 U.S.C. 102 (a()1) as being anticipated by Tao, ChengCheng, Optimization of cement production and hydration for improved performance, energy conservation, and cost PhD Thesis, U Florida, August 2017 hereinafter TAO.
Regarding Claims 1, 9, 17, TAO discloses
A system for modeling cement and a cement kiln plant, the system comprising: {[p15 ln5] kiln-cement virtual model}
at least a first processor configured to perform an integrated modeling computer program comprising {[p15 ln5] kiln-cement virtual model on the HiPerGator High Performance Computer (HPC);}
a Virtual Cement and Concrete Testing Laboratory (VCCTL) modeling computer program and a virtual cement plant (VCP) modeling computer program, { [p. 14, line 6 from bottom] VCCTL was also coupled with a virtual cement plant (VCP) }
the VCP modeling computer program receiving VCP input and producing a VCP output {see at least [ p. 101 middle] In order to simulate the VCP, input files including the mass fraction of raw meal, peak gas temperature and the location where peak gas temperature occurs within the kiln are required; [Chapter 5, p. 104, line 4 from bottom] Based on the output of VCP, mass fractions of different cement clinker phases are plotted}
the VCCTL modeling computer program receiving the VCP output and producing a virtual cement performance based at least in part on the VCP output received by the VCCTL modeling computer program from the VCP modeling computer program; {[p. 19] Chapter 5 discusses the coupling of VCP and VCCTL model by importing the output of the cement kiln model into VCCTL and running the integrated VCP-VCCTL model on HiPerGator High Performance Computer (HPC); VCCTL was also coupled with a virtual cement plant (VCP); [ p. 16 ln. 3] imported into the VCCTL to simulate hydration and predict mechanical performance for hardened mortar and concrete.}
and memory in communication with said at least a first processor. { [p. 19] running the integrated VCP-VCCTL model on HiPerGator High Performance Computer (HPC); } Though not explicitly, a POSITA would recognize that a memory in communication with at least a first processor is inherently disclosed since a computer cannot function otherwise.
Regarding Claim 2, 10, 18, TAO further teaches, wherein said at least a first processor is also configured to perform one or more multi-objective metaheuristic optimization computer programs. {[Abstract/Intro, p. 19] Chapter 5 discusses the coupling of VCP and VCCTL model by importing the output of the cement kiln model into VCCTL and running the integrated VCP-VCCTL model on HiPerGator High Performance Computer (HPC) at the University of Florida. Metaheuristic algorithms are applied on the integrated model}
Regarding Claim 3, 11, TAO teaches
wherein said one or more multi-objective metaheuristic optimization computer programs terminate when preselected convergence criteria are met. {[p. 46 ln 4] multi-objective metaheuristic optimization technique; [p. 54 middle] And the optimization process is considered converged when the objective function is less than 10-6}
Regarding Claim 4, 12, 19 TAO teaches
wherein said one or more multi-objective metaheuristic optimization computer programs generate a plurality of Pareto fronts from the output of the integrated modeling computer program. { [p. 18 7th ln from bottom] Metaheuristic algorithms are applied on the integrated model to optimize energy consumption, cement strength, CO2 emissions, and production cost. Pareto fronts are plotted}
Regarding Claim 5, 13, TAO teaches
wherein a respective Pareto front is generated for each of a plurality of objectives of a multi-objective optimization problem associated with the VCCTL modeling computer program. { [p. 19 ln 9 from bottom] importing the output of the cement kiln model into VCCTL and running the integrated VCP-VCCTL model…. Metaheuristic algorithms are applied on the integrated model to optimize energy consumption, cement strength, CO2 emissions, and production cost. Pareto fronts are plotted to show the trade-off solutions between energy, price and greenhouse gas emissions.}
Regarding claims 6, 14, 20 TAO teaches
wherein said one or more multi-objective metaheuristic optimization computer programs perform at least one of a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA).
{[p.16. top] The computational framework presented in this dissertation applies multi-objective metaheuristic optimization to virtual cement and virtual cement plant modeling [p. 18 middle] Among these metaheuristic methods, two important and widely used computational methods that deal with the engineering optimization problems are the particle swarm optimization (PSO) (Hu, Eberhart, & Shi, 2003; L. Li, Huang, & Liu, 2009; L. Li, Huang, Liu, & Wu, 2007; Shi, 2001) and the genetic algorithm (GA) (Goldberg & Samtani, 1986; Rajeev & Krishnamoorthy, 1992; Wu & Chow, 1995)… This dissertation will show how metaheuristic algorithms are applied for discrete problems matching the discrete input data required by VCCTL.}
Regarding claims 7, 16 TAO teaches
wherein the virtual cement performance provides information regarding control parameters for the cement kiln plant that can be adjusted to reduce material costs and consumption. {p. 105 Multi-objective PSO was integrated into the coupled VCP-VCCTL model to create an integrated computational optimization VCP-VCCTL tool for energy saving, cost saving and greenhouse gas emissions reduction without sacrificing cement productivity and performance. Similar to Chapter 3, Pareto fronts of four different bi-objective scenarios are plotted in Figure 5-7 to show clear trade-off between modulus and material cost.} In BRI and in view of the specification, material cost interpreted as cost savings, consumption interpreted as energy consumption.
Regarding claims 8, 15, TAO teaches
wherein the virtual cement performance provides information regarding control parameters for the cement kiln plant that can be adjusted to decrease carbon dioxide emissions that are emitted by the cement kiln plant. [{p.106 middle] In order to reduce CO2 emissions without sacrificing cement strength, CO2 emissions from limestone and 7-day modulus are considered as the objectives in PSO at the same time. Figure 5-8 shows the four Pareto fronts for different optimization scenarios on E vs. CO2 emissions from limestone decomposition. In Figure 5-8, the Min(CO2 emission)-Max(E) Pareto front is what the cement industry wants. The point with 0.14 CO2 emissions and 27.8 GPa is the optimal cement. When mass fraction of CO2 emissions is more than 0.14, most of the cement give more emissions without sacrificing too much strength. More alite means more decomposition, which typically gives more strength. The results of this optimization suggest the coupled VCP/VCCTL model could be used as a tool to optimize for a design cement.}
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Naseri, H Cost Optimization of No-Slump Concrete Using Genetic Algorithm and Particle Swarm Optimization, Int J. Innovation, Management and Technology, Vol 10, No 1. Feb 2019
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/A.S./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188