DETAILED ACTION
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 1-2, 11, 15-16 objected to because of the following informalities:
Claims recite “a small polymerization vessel”. The term "small" is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Appropriate correction is required.
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.
Claim(s) 1-4, 7, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 20080222067) in view of Castillo et al. “Production of MCM-41 Nanoparticles with Control of Particle Size and Structural Properties: Optimizing Operational Conditions during Scale-Up.”
Regarding claim 1, Lin teaches a method for developing a
wherein each of the experimental results includes a plurality of structural parameter groups and a plurality of product qualities corresponding to the structural parameter groups ([0066], [0070]), and each of the structural parameter groups includes a plurality of
performing a prediction process with Taguchi experimental design method and the experimental results to obtain a plurality of prediction results ([0038], [0040]),
wherein the prediction results include a plurality of prediction parameter groups and a plurality of prediction qualities corresponding to the prediction parameter groups ([0047]-[0048]), and each of the prediction parameter groups includes a plurality of predictive
performing a simulation process with the experimental results and the prediction results to obtain a simulated prediction model ([0039] “error between the experimentally measured result and the simulated result … is set up as an objective function”), wherein the simulation process is performed by an artificial intelligence neural network ([0073], [0075]);
obtaining an optimized ([0039]) simulation parameter group of the small polymerization vessel by the simulated prediction model, wherein the optimized simulation parameter group includes a plurality of simulated
Lin does not explicitly teach, however Castillo discloses a method for developing an agitation system of a scale-up polymerization vessel, and the agitation system is configured to be applied in the scale-up polymerization vessel (p2. Last paragraph – p.3 2nd par. “results are tested in a bigger reactor (volume-based scale-up)”, p.8 ¶2.4 where 5L pilot plant cylindrical reactor, commonly used for polymerization and is a laboratory-scale vessel), wherein the method comprises: performing a reaction with a small polymerization vessel to obtain a plurality of experimental results (p.11 ¶2.4.3 “scale-up study, the particles obtained in the small reactor”), wherein each of the experimental results includes a plurality of structural parameter groups (p.15 L2-3 “parameters that govern the process are the following: stirring speed, pH initial value, reaction time, reagents’ addition rate, and temperature”, F4 “Experimental conditions: 1 L round-bottom flask reactor with 0.5 L working volume and magnetic stirring (800 rpm”) and a plurality of product qualities corresponding to the structural parameter groups (p.3 L9-10 “particle size, morphology, and other structural properties”, p.4 L7-30 “average value of the particle size (y) and its variance (s2) are computed using the method of moments for the peaks of particle size distribution”), and each of the structural parameter groups includes a plurality of agitation parameters (p.9 “The agitation rate was varied in a short interval … Stirring speeds were set at 400 and 650 rpm and two runs were performed for each of these speeds”, p.14 3.4 “pH, temperature, agitation speed (mass transport), reaction time, and addition rate of TEOS seem to be easily modifiable parameters”); and constructing the scale-up polymerization vessel based on the simulated agitation parameters (p.3 2nd par. “results are tested in a bigger reactor (volume-based scale-up)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin to include agitation system of a scale-up polymerization vessel, and the agitation system is configured to be applied in the scale-up polymerization vessel as disclosed by Castillo. Doing so would allow to operate at different plant scales and give important information for process control.
Regarding claim 2, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein the small polymerization vessel includes a plurality of stirring blades and a plurality of choke tubes, and the agitation parameters include a stirring speed, a blade diameter and a blade width of each of the stirring blades, a position of an uppermost stirring blade of the stirring blades, a distance between each of the choke tubes and a vessel wall, and/or a combination thereof (Castillo F4-5, 9).
Regarding claim 3, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein the agitation parameters are determined by a L9 orthogonal array (Lin [0036]).
Regarding claim 4, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 3, wherein each of the agitation parameters is divided into three levels of medium level, high level and low level (Castillo p.4 (eq.2) , although Lin as modified by Castillo doesn’t explicitly teach high, medium or low levels, such determination is obvious in view of the eq.2).
NOTE in analogous art Lee et al. (US 20070250214) discloses parameters divided into high, medium or low levels and further obviate the teachings of Lin and Castillo.
Regarding claim 7, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein the agitation parameters and the predictive agitation parameters are used as an input layer of the artificial intelligence neural network, and the product qualities and the prediction qualities are used as a corresponding output layer of the artificial intelligence neural network (Lin [0059], [0062], C4).
Regarding claim 13, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein after the prediction process and/or the simulation process is performed, the method further comprises: performing a verification process (Lin [0070], [0081]).
Regarding claim 14, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein a volume of the scale-up polymerization vessel is 220 M3 (Castillo p.3 “bigger reactor (volume-based scale-up)”, F4, ¶2.4, p.12, ¶4, While the prior art does not explicitly disclose as exact volume claimed, the particular elements are obvious, and any particular equation would be an obvious to try combination of elements in order to achieve a predictable results. See MPEP 2143.).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 20080222067) in view of Castillo et al. “Production of MCM-41 Nanoparticles with Control of Particle Size and Structural Properties: Optimizing Operational Conditions during Scale-Up” and in further view of AHN et al. (US 2020035449).
Regarding claim 5, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1,
Lin as modified does not explicitly teach, however AHN discloses wherein the scale-up polymerization vessel is configured to produce polyvinyl chloride ([0039]), and the simulation quality includes an average particle size, a standard deviation of particle size, an oil absorption and an apparent specific gravity of the polyvinyl chloride ([0029], [0049]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin as modified to produce polyvinyl chloride as disclosed by AHN. Doing so would improve polymerization productivity (AHN [0039]).
Regarding claim 6, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 5, wherein the optimized simulation parameter group is determined according to the average particle size of the polyvinyl chloride (Lin [0010], Castillo p.2 AHN [0049]).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 20080222067) in view of Castillo et al. “Production of MCM-41 Nanoparticles with Control of Particle Size and Structural Properties: Optimizing Operational Conditions during Scale-Up” and in further view of Thierry Meyer “Scale-Up of Polymerization Process: A Practical Example” or D.F. Ryan “CIRCULATION TIME PREDICTION IN THE SCALE-UP OF POLYMERIZATION REACTORS WITH HELICAL RIBBON AGITATORS.”
Regarding claim 11, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 1, wherein after the optimized simulation parameter group is obtained, the method further comprises: performing
Lin as modified does not explicitly teach, however Thierry Meyer and D.F. Ryan discloses performing a flow field simulation with the optimized simulation parameter group, wherein the flow field simulation is performed for the small polymerization vessel and the scale-up polymerization vessel (Thierry Meyer p.298 ¶ Polymerization Reactors, p.301 ¶ Scale-Up Strategy Applied to In-Line Control, D.F. Ryan p.1963 C1-C2 “Flow … can be modelled”, p.1964 C2, p.1966 ¶SCALE-UP). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lin as modified to produce polyvinyl chloride as disclosed by Thierry Meyer and D.F. Ryan. Doing so would provide vital data for the optimization and scale-up of the reactor (D.F. Ryan p.1961 C1-C2).
Regarding claim 12, Lin as modified teaches the method for developing the agitation system of the scale-up polymerization vessel of claim 11, wherein the flow field simulation is performed with computational fluid dynamics simulation (Castillo p.9 D.F. Ryan Abstract).
Allowable Subject Matter
Claims 8-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 15-18 are allowed.
The cited reference above, failed to specifically teach claim 15 as a whole. Although Lin et al. (US 20080222067) in view of Castillo et al. “Production of MCM-41 Nanoparticles with Control of Particle Size and Structural Properties: Optimizing Operational Conditions during Scale-Up” is the closest prior art. The reference failed to teach - the agitation system is configured to be applied in the scale-up polymerization vessel configured to produce polyvinyl chloride, wherein the method comprises: performing an augmentation step after the simulation process, wherein the augmentation step is to divide a numerical range consisting of the agitation parameters and the predictive agitation parameters into a plurality of levels to obtain a plurality of augmentation parameters, thereby obtaining a plurality of augmentation parameter groups, wherein each of the augmentation parameter groups includes the augmentation parameters and a corresponding augmentation quality; estimating a stirring power according to each of the augmentation parameter groups; and modifying the simulated prediction model with the experimental results, the prediction results, the augmentation parameter groups and the stirring power corresponding to each of the augmentation parameter groups, thereby obtaining an optimized simulation parameter group for constructing the scale-up polymerization vessel.
Further note - AGARWAL et al. (US 20220235676) discloses plurality of predictive models to optimize the plurality of key performance parameters by generating test cases for a simulation, wherein case generation include Taguchi a design method. The optimizer is configured using the plurality of predictive models (artificial neural networks) to optimize the plurality of key performance parameters, but failed to teach a plurality of augmentation parameters, thereby obtaining a plurality of augmentation parameter groups, wherein each of the augmentation parameter groups includes the augmentation parameters and a corresponding augmentation quality; estimating a stirring power according to each of the augmentation parameter groups; and modifying the simulated prediction model with the experimental results, the prediction results, the augmentation parameter groups and the stirring power corresponding to each of the augmentation parameter groups.
Further note Jeyaganesh Devaraj et al. “Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance,” which teaches combining artificial neural network (ANN) with grey-based Taguchi optimization. However, Jeyaganesh failed to teach performing an augmentation step after the simulation process, wherein the augmentation step is to divide a numerical range consisting of the agitation parameters and the predictive agitation parameters into a plurality of levels to obtain a plurality of augmentation parameters, thereby obtaining a plurality of augmentation parameter groups, wherein each of the augmentation parameter groups includes the augmentation parameters and a corresponding augmentation quality; estimating a stirring power according to each of the augmentation parameter groups; and modifying the simulated prediction model with the experimental results, the prediction results, the augmentation parameter groups and the stirring power corresponding to each of the augmentation parameter groups, thereby obtaining an optimized simulation parameter group for constructing the scale-up polymerization vessel.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 February 4, 2026