DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 02/02/2026
Claims 1, 3, 9-10, and 12-16 are presented for examination
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/02/2026 has been entered.
Response to Arguments - 101
Applicant's arguments filed 02/02/2026 have been fully considered but they are not persuasive.
Applicant argues that the newly added step of “making a physical tool based on second digital 3D model in response to determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance.” Integrates the claims into a practical application/ provides significantly more, and further that this makes the claims analogous to claim 1 of example 25.
Examiner responds by firstly explaining that this addition still does not make the claims equivalent to claim 1 of example 25. Part of what made the manufacturing features of claim 1 of example 25 eligible, as opposed to mere insignificant post solution activity, was the interactivity between the control system and the manufacturing equipment. Claim 1 of example 25 did not merely calculate initial manufacturing parameters then perform manufacturing using those parameters, it provided constant and active adjustment and communication between the control system and the manufacturing equipment, continually monitoring and adjusting the system based on feedback from the manufacturing equipment. ([Example 25 Claim 1] “initiating an interval timer in said computer upon the closure of the press for monitoring the elapsed time of said closure,• constantly determining the temperature (Z) of the mold at a location closely adjacent to the mold cavity in the press during molding,• constantly providing the computer with the temperature (Z),• repetitively calculating in the computer, at frequent intervals during each cure, the Arrhenius equation for reaction time during the cure, which is ln v = CZ+x, where v is the total required cure time,• repetitively comparing in the computer at said frequent intervals during the cure each said calculation of the total required cure time calculated with the Arrhenius equation and said elapsed time,”
([Example 25 Analysis – Claim 1] “when viewing the claim as a whole, the combination of all these steps taken together, including the constant determination of the temperature of the mold, the repetitive calculations and comparisons, and the opening of the press based on the calculations, amount to significantly more than simply calculating the mold time using the Arrhenius equation because they add meaningful limits on use of the equation.”)
In contrast, the present claims amount to no more than a series of abstract steps to create a design of a tool, to which the claims are directed, with a final step of actually producing the tool. The interplay and continuous integration of the control system with the manufacturing equipment, as in claim 1 of example 25, is totally absent in the present claims.
As such, the step of “making a physical tool based on second digital 3D model in response to determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance” as the final step after an abstract idea, to which the claims are directed, of designing that model of the tool is more akin to a step of cutting hair after performing an abstract process, to which the claims are directed, of designing a particular hair style. ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
It should be noted that the manufacturing of a design based on an optimized digital model of that design is an example of well-understood, routine, conventional activity (WURC). See the following:
Henry (US 20190388859 A1)[Par 101, 228-229]
Roux (US 20160078161 A1)[Par 2-3]
Schmidt (US 20200143009 A1)[Par 3-4]
Response to Arguments - 103
Applicant's arguments filed 02/02/2026 have been fully considered but they are not persuasive.
Applicant argues that no prior art teaches making a physical tool based on second digital 3D model in response to determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance.
Examiner responds by explaining that this feature is taught by the combination of the previously cited references and new reference Henry (US 20190388859 A1)
Particularly, Hanada makes obvious making a physical tool based on second digital 3D model ([Fig.4] Shows a 3D model of the first geometry of the mixing tool (4b) next to the new, optimized model of the second mixing tool (4a) [Page 13 Par 1- 4] “We validated the simulation results by measuring the actual pressure drop. The difference between the upstream and downstream pressures was measured for a flow velocity of 1 m/s. We also calculated the Z-factor, which is the ratio of the pressure drop of the TDM to that of an empty pipe of the same diameter (8 mm) and length (100 mm): … The experimentally determined pressure drops for all the models in Fig. 11 were roughly consistent with those obtained by CFD simulation, although the values for the different blade models differed significantly.” [Fig. 11] Shows comparisons between the simulated and experimental (i.e. performed using a physical tool representative of the corresponding design) performance of each of the optimized models)
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([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”)
Chu makes obvious ([Fig. 17(I)(b) and Fig. 17(II)(b)] Show a second (i.e. updated and modified from the first) particle density distribution and a corresponding second (i.e. updated) spatial distribution of a concentration below it)
Henry makes obvious producing a physical tool in response to determining that a model of that tool achieves a target performance ([Par 194-202] “The design process may comprise any of the embodiments previously described herein relating to the process for preparing the catalytic static mixer (CSM) element comprising additive manufacturing, such as 3D printing. The additive manufacturing provides flexibility in preliminary design and testing, and further re-design and re-configuration of the static mixers to facilitate development of more commercially viable static mixers. A process for design and manufacture of a catalytic static mixer (CSM) element for a continuous flow chemical reactor chamber may comprise the steps of: designing a prototype static mixer element comprising a scaffold defining a plurality of passages configured for mixing one or more fluidic reactants during flow and reaction thereof through the mixer; … testing the prototype CSM for at least one of suitability for catalytic coating or operational performance and durability in a continuous flow chemical reactor; … redesigning the static mixer element to enhance at least one of suitability for catalytic coating or operational performance and durability in a continuous flow chemical reactor; and manufacturing the redesigned static mixer element comprising a redesigned scaffold defining a plurality of passages configured for mixing one or more fluidic reactants during flow and reaction thereof through the mixer, … The step of testing the CSM, re-designing the static mixer element and manufacturing the CSM may be repeated one or more times to further enhance at least one of performance, durability, manufacturability, or scaffold suitability for catalytic coating. Computational fluid dynamics (CFD) software can be used in the design (or re-design) to obtain various enhanced configurations of the CSMs and scaffolds, which will by determined by the desired applications and associated catalytic reactions. For example, a design process can be used to develop configurations and geometries having enhanced microscopic and macroscopic mixing properties, which may be indicated by the turbulent length scales in turbulent flow, in the vicinity of the scaffold and hence the catalyst, while also providing enhanced heat transfer properties. [Par 228-229] “The design process may also comprise an iterative approach to optimise or enhance at least one of performance, durability, manufacturability, or scaffold suitability for catalytic coating. For example, if the results can be enhanced by certain changes to the geometry, then changes (based on knowledge of fluid dynamics) can be made to the geometry and the design optimisation procedure repeated. The initial geometry may be chosen and optimised to enhance various characteristics of the static mixer element, such as the specific surface area, volume displacement ratio, line-of-sight accessibility for cold-spraying, strength and stability for high flow rates, suitability for fabrication using additive manufacturing, or to achieve a high degree of chaotic advection, turbulent mixing, catalytic interactions, or heat transfer.”)
Henry is analogous art because it is within the field of mixing tool geometry optimization. It would have been obvious to one of ordinary skill in the art to combine it with Hanada, Chu, Rabha, and Cho before the effective filing date. One or ordinary skill in the art would have been motivated to make this combination in order to further improve efficiency through design optimization and allow for mixers that are more easily removable and replaceable. Henry notes the particular need for such a mixer and associated design system ([Par 4] “Towards improving process productivity through increased reaction yields, there is a clear need for developing enhanced static mixers and/or reaction chambers for continuous flow chemical reactors that are readily removable and easily replaced, allow further re-design enhancement and are capable of providing more efficient mixing, heat transfer and catalytic reaction of reactant chemical and/or electrochemical reactants.”) To this end, Henry presents a method for the optimal design of static mixers that are more efficient at both the mechanical mixing itself and the activation of mixed chemicals, while allowing the mixers to be easily replaced for easy maintenance ([Par 5] “The present inventors have undertaken significant research and development into alternative continuous flow chemical reactors and have identified that static mixers can be provided with a catalytic surface such that the resulting static mixer is capable of being used with a continuous flow chemical reactor. It was surprisingly found that incorporating catalytic material on the surface of additive manufactured static mixers can provide catalytic static mixers that can be configured to be readily removable and easily replaced, allow for further re-design enhancement, and provide for efficient mixing, heat transfer and catalytic reaction of reactants in continuous flow chemical reactors. The static mixers may be provided for use with in-line continuous flow reactors as inserts or as modular packages with the static mixer as an integral part of a section of the reactor tube itself.”) Overall, one of ordinary skill in the art would have recognized that combining Henry with Hanada, Chu, Rabha, and Cho would result in a more efficient mixing tool design system that also integrates the flexibility of easily removable and swappable components.
Claim Objections
Claims 1, 3, 9-10, and 12-16 objected to because of the following informalities:
Claim 1 recites “making a physical tool based on second digital 3D model…” This is a simple grammatical mistake and should instead read “making a physical tool based on the second digital 3D model…”
Claim 1 recites “…fluid concentration to determine whether that the first and second spatial distributions match with each other…” This is a simple grammatical mistake and should instead read “…fluid concentration to determine whether
Appropriate correction is required.
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, 3, 9-10, and 12-16 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
A method of designing and optimizing a mixing tool to mix a plurality of fluids to obtain a mixture, the method comprising: representing a first geometry of the mixing tool with a first digital 3D model; … representing the second geometry with a second digital 3D model;
“Representing” a model of a mixing tool is a mental process that is equivalent to merely drawing a representation of said model with a pencil and paper.
Doing so with a “digital 3D model” is equivalent to mere instructions to apply this judicial exception on a general purpose computer.
Should it be found that this is not a mental process, it is also an example of mere data gathering.
comparing the first and second spatial distributions of fluid concentration to determine that the first and second spatial distributions match with each other; based on the first and second spatial distributions matching with each other, validated the CFD model;
Comparing two things to see if they match is a mental process that involves observing both items, whether they be physical items or representations of data, and judging if they are identical. Completing common children’s “spot the difference” puzzles are essentially equivalent analysis processes. Deciding that the model is “validated” based on the information matching is merely a mental judgment made about the relationship between the distributions and the model’s performance.
optimizing the first geometry of the mixing tool to a second geometry using the validated CFD simulation model;
Creating a second “optimized” geometry based on the simulation output is a mental process equivalent to observing the simulation output, judging where improvements could be made, and drawing a new modified representation of the geometry incorporating these improvements using a pen and paper.
The use of a generic CFD model amounts to no more than mere instructions to apply the judicial exception using a general purpose computer.
Should it be found that the use of a generic CFD is not mere instructions to apply, it is also an example of mere data gathering.
…determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance.
Determining that this target performance is reached is a mental process equivalent to mentally comparing the updated spatial distribution to an arbitrarily chosen performance requirement, and judging if that requirement is met. This can be done by simply observing the updated distribution and judging if it sufficiently achieves the desired, arbitrarily chosen distribution.
The claim also recites a mathematic process, specifically:
converting the particle density distribution of the mixture to a first spatial distribution of fluid concentration of the mixture at least in part by calculating a fluid concentration at a given point by weighting adjacent discrete fluid representing particles of the particle density distribution; … converting the updated particle density distribution of the mixture to an updated spatial distribution of fluid concentration of the mixture; and
In light of the specification ([See Page 7 line 32 - Page 8 Line 25 “Referring again to FIG. 2, the particle density distribution of the mixture 230 obtained from the CFD simulation model220 can be converted to a spatial distribution of fluid concentration of the mixture at 240. …The fluid concentration at any given point of the simulation domain (e.g. inside the mixing tool 300) can be calculated by weighting nearby particles by, e.g., a decay function… The local concentration ca(r) can then be calculated through Equation (1) below: ”) and the content of the claim, it is clear that the “converting” step is textual placeholder for a mathematic calculation. With this in mind, this limitation is a mathematic process.
determining a spatial distribution of mixing index based on the updated spatial distribution of fluid concentration of the mixture.
In light of the specification, ([See Page 9 line 6-17] “The obtained local fluid concentration ca(r) at 240 can be further processed to calculate a spatially resolved mixing index Mp(r) that can easily be visualized to assess mixing performance. The local mixing quality depends on the respective fluid concentration distributions of the components (species) of the mixture. For example, for a binary mixture with a concentration ratio between the two components or species of N1:N2, a mixing index can be defined by Equation (3):…”) it is clear that the “determining” step is textual placeholder for a mathematic calculation. With this in mind, this limitation is a mathematic process.
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
Measuring, via an X-ray scan of the mixing tool, a second spatial distribution of fluid concentration of the mixture inside the mixing tool when mixing the plurality of fluids using the mixing tool;
Obtaining data via an x-ray scan amounts to no more than mere data gathering.
A method of designing and optimizing a mixing tool to mix a plurality of fluids to obtain a mixture, the method comprising: representing a first geometry of the mixing tool with a first digital 3D model; … representing the second geometry with a second digital 3D model;
Without explaining how this representation is generated or created, and recited at such a high level of generality, these limitations are merely equivalent to gathering data representative of these models.
Should it be found that this is not mere data gathering, it is also an example of mere instructions to apply.
providing the first digital three-dimensional (3D) model to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process of the plurality of fluids to generate a particle density distribution of the mixture inside the mixing tool; … using the validated CFD simulation model; … providing the second digital 3D model to the validated CFD simulation model to simulate the mixing of the plurality of fluids, and implementing, via the processor, the CFD simulation model to generate an updated particle density distribution of the mixture inside the mixing tool;
When recited at such a high level of generality without explaining how the CFD simulation is performed nor how the model is “provided” to the simulation, these limitations are merely equivalent to gathering data into a generic CFD simulation and then subsequently gathering the output of that simulation.
Should it be found that this is not mere data gathering, it is also an example of mere instructions to apply.
Post-solution activity:
making a physical tool based on second digital 3D model in response to determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance.
Producing this tool based on the designed model is merely equivalent to acting on the results of the abstract idea, and therefore amounts to no more than insignificant post-solution activity, equivalent to designing a hairstyle through abstract methods, to which the claims are directed, with a final step of actually cutting the hair using a generic tool. See ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
The determination that the spatial distribution achieves a target performance is a mental process, as analyzed above.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
representing a first geometry of the mixing tool with a first digital 3D model; providing the first digital three-dimensional (3D) model to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process of the plurality of fluids to generate a particle density distribution of the mixture inside the mixing tool; … using the validated CFD simulation model; … … representing the second geometry with a second digital 3D model; providing the second digital 3D model to the validated CFD simulation model to simulate the mixing of the plurality of fluids, and implementing, via the processor, the CFD simulation model to generate an updated particle density distribution of the mixture inside the mixing tool;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the mixing is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Page 6 line 7-11] “The processor 12 can be included in any computing device. The processor 12 may include, for example, one or more general-purpose microprocessors, specially designed processors, application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), a collection of discrete logic, and/or any type of processing device capable of executing the techniques described herein.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “a first digital 3D model; a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process; the validated CFD simulation model; a second digital 3D model;” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are Insignificant Extra-Solution Activity or Mere Instructions to Apply and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
Measuring, via an X-ray scan of the mixing tool, a second spatial distribution of fluid concentration of the mixture inside the mixing tool when mixing the plurality of fluids using the mixing tool;
Obtaining data via an x-ray scan amounts to no more than mere data gathering.
A claim element that amounts to merely gathering data is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
A method of designing and optimizing a mixing tool to mix a plurality of fluids to obtain a mixture, the method comprising: representing a first geometry of the mixing tool with a first digital 3D model; … representing the second geometry with a second digital 3D model;
Without explaining how this representation is generated or created, and recited at such a high level of generality, these limitations are merely equivalent to gathering data representative of these models.
A claim element that amounts to merely gathering data is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Should it be found that this is not mere data gathering, it is also an example of mere instructions to apply.
providing the first digital three-dimensional (3D) model to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process of the plurality of fluids to generate a particle density distribution of the mixture inside the mixing tool; … using the validated CFD simulation model; … providing the second digital 3D model to the validated CFD simulation model to simulate the mixing of the plurality of fluids, and implementing, via the processor, the CFD simulation model to generate an updated particle density distribution of the mixture inside the mixing tool;
When recited at such a high level of generality without explaining how the CFD simulation is performed nor how the model is “provided” to the simulation, these limitations are merely equivalent to gathering data into a generic CFD simulation and then subsequently gathering the output of that simulation.
A claim element that amounts to merely gathering data is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Should it be found that this is not mere data gathering, it is also an example of mere instructions to apply.
Post-solution activity:
making a physical tool based on second digital 3D model in response to determining that the updated spatial distribution of fluid concentration of the mixture achieves a target performance.
Producing this tool based on the designed model is merely equivalent to acting on the results of the abstract idea, and therefore amounts to no more than insignificant post-solution activity, equivalent to designing a hairstyle through abstract methods, to which the claims are directed, with a final step of actually cutting the hair using a generic tool.
This element merely acts on the results of the previous abstract steps. A claim element that merely acts on a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
The determination that the spatial distribution achieves a target performance is a mental process, as analyzed above.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
representing a first geometry of the mixing tool with a first digital 3D model; providing the first digital three-dimensional (3D) model to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process of the plurality of fluids to generate a particle density distribution of the mixture inside the mixing tool; … using the validated CFD simulation model; … … representing the second geometry with a second digital 3D model; providing the second digital 3D model to the validated CFD simulation model to simulate the mixing of the plurality of fluids, and implementing, via the processor, the CFD simulation model to generate an updated particle density distribution of the mixture inside the mixing tool;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the mixing is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Page 6 line 7-11] “The processor 12 can be included in any computing device. The processor 12 may include, for example, one or more general-purpose microprocessors, specially designed processors, application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), a collection of discrete logic, and/or any type of processing device capable of executing the techniques described herein.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
In addition, the following are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
providing the first digital three-dimensional (3D) model to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process … providing the second digital 3D model to the validated CFD simulation model to simulate the mixing of the plurality of fluids is a well-understood, routine, and conventional activity, as evidenced by:
Pros and Cons of CFD and Physical Flow Modeling ([Page 2 Par 2])
Computational Fluid Dynamics Use, Application and Challenges in Support of Store/Aircraft Compatibility ([Page 1 Introduction Par 1])
Computational Fluid Dynamics | Aerospace at Illinois ([Page 1 Par 2])
… making a physical tool based on … digital 3D model..
Henry (US 20190388859 A1)[Par 101, 228-229]
Roux (US 20160078161 A1)[Par 2-3]
Schmidt (US 20200143009 A1)[Par 3-4]
As per MPEP § 2106.05(d), an additional element that is “no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality,” does not integrate a judicial exception into a practical application, nor provide significantly more.
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “a first digital 3D model; a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process; the validated CFD simulation model; a second digital 3D model;” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 3 recites “wherein the first and second spatial distributions are respectively represented by first and second sets of iso-surfaces, and when the respective shapes and distributions of the first and second sets of iso-surfaces match with each other, the CFD simulation model is validated.”
This merely clarifies how the distributions are represented, and is thus merely an extension of the mental process and mere instructions to apply.
Claim 9 recites “further comprising visualizing at least one of the spatial distributions of fluid concentration and mixing index in a graphic user interface (GUI) to guide a user to determine whether the plurality of fluids is uniformly mixed.”
Displaying the spatial distributions of fluid concentration and mixing index is merely the act of presenting the results of the previous abstract steps, and is therefore merely an example of insignificant post solution activity.
Claim 10 recites “wherein when the first and second spatial distributions do not match with each other, adjusting the CFD simulation model to generate an updated particle density distribution of the mixture.”
Comparing two things to see if they match is a mental process that involves observing both items, whether they be physical items or representations of data, and judging if they are identical. Completing common children’s “spot the difference” puzzles are essentially equivalent analysis processes.
Adjusting the simulation is a mental process, equivalent to judging that certain parameters should have different values. Adjusting the parameters as such in a computer simulation is equivalent to mere instructions to apply such an exception on a computer.
Claim 12 recites “wherein comparing the first and second spatial distributions comprises visualizing the first and second spatial distributions of fluid concentration in a graphic user interface.”
Displaying the spatial distributions of fluid concentration is merely the act of presenting the results of the previous abstract steps, and is therefore merely an example of insignificant post solution activity.
Comparing two things on a display readout to see if they match is a mental process that involves observing both items and judging if they are identical. Completing common children’s “spot the difference” puzzles are essentially equivalent analysis processes.
Claim 13 recites “wherein the visualizing further comprises overlaying digital representations of the first and second first and second spatial distributions in a graphic user interface.”
This merely specifies how the visualization is displayed, and is therefore merely an extension of the mere post-solution activity.
Claim 14 recites “wherein overlaying the digital representations of the first and second first and second spatial distributions comprises importing corresponding polygon surfaces to a same coordinate system in the graphic user interface.”
This merely specifies how the visualization is displayed, and is therefore merely an extension of the mere post-solution activity.
Claim 15 recites “wherein the mixing of the plurality of fluids via the simulation with the CFD simulation model and via the mixing tool is under the same operation conditions.”
This merely clarifies the conditions under which the physical measurements and simulation are performed, and is therefore merely an extension of the data gathering and mere instructions to apply/WURC steps.
Claim 16 recites “wherein the plurality of fluids includes two or more specifies of an adhesive.”
This merely clarifies what the mixed fluids are, and is therefore merely an extension of the mere instructions to apply/WURC steps of simulating them and mental process of comparing them.
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.
(1) Claims 1, 9, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over CFD geometrical optimization to improve mixing performance of axial mixer (Hereinafter Hanada) in view of CFD–DEM study of the effect of particle density distribution on the multiphase flow and performance of dense medium cyclone (Hereinafter Chu) in further view of Visualization and quantitative analysis of dispersive mixing by a helical static mixer in upward co-current gas–liquid flow (Hereinafter Rabha) as well as A non-sampling mixing index for multicomponent mixtures (Hereinafter Cho) in addition to Henry (US 20190388859 A1)
Claim 1. Hanada makes obvious A method of designing and optimizing a mixing tool to mix a plurality of fluids to obtain a mixture, the method comprising: ([Abstract] “Although various axial static mixers have been proposed, their commercialization have been hindered by their high pressure drop characteristic and the limited operating conditions where a wide frequency range of input disturbances cannot be filtered … In the present study, the TDM was modified to decrease the pressure drop and smoothen the outlet concentration profile. The geometry of the element tip was modified based on the results of CFD simulation that were successfully validated by the corresponding experiments, with the purpose of reducing the flow resistance … The performance of the TDM was sufficiently optimized to make it commercially viable.” [Page 4 Par 2] “This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction” [Page 1 Par 1- Page 2 Par 2] “Fig. 1a shows a model of mixing by a conventional static mixer, where the closed and open circles represent the mixture components” [Fig. 1] Shows a model of fluids mixed by a mixing tool)
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representing a first geometry of the mixing tool with a first digital 3D model; ([Page 6 Par 2] “We thus propose a new design of this part of the mixer as shown in Fig. 4a. The shape and dimensions of the other parts are as in the original model (see Fig. 4b)” [Fig.4b] Shows a 3D model of the first geometry of the mixing tool)
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providing the first digital three-dimensional (3D) model (Fig.4b] Shows a 3D model of the first geometry of the mixing tool) to a computational fluid dynamics (CFD) simulation model implemented by a processor to simulate a mixing process of the plurality of fluids to generate a ([Fig. 3] Shows a side view of the output of a CFD using the first model, including a first distribution [Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM” ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction. Fig. 3a shows the cross-sectional view of the original TDM”)
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([Fig. 3] Shows a side view of the output of a CFD using the first model, including a first distribution Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM”) ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.”) at least in part by calculating a fluid concentration ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.”) ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … we injected a pulse of salt water of adjusted conductivity 100mS/cm into a continuous water flow generated by a volute pump, and measured the conductivity of the mixture downstream of the mixer using a conductivity measuring system that we developed (see Fig. 14)… The residence measurement circuit is shown in Fig.15. … Thus, it is possible to determine the concentration of salt water from the resistance value… We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of two concentration measurements (one from the output of the model, the other measured experimentally) to see if they match in order to validate the model)
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based on the first and second ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … we injected a pulse of salt water of adjusted conductivity 100mS/cm into a continuous water flow generated by a volute pump, and measured the conductivity of the mixture downstream of the mixer using a conductivity measuring system that we developed (see Fig. 14)… The residence measurement circuit is shown in Fig.15. … Thus, it is possible to determine the concentration of salt water from the resistance value… We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of two concentration measurements to see if they match in order to validate the model) optimizing the first geometry of the mixing tool to a second geometry ([Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 6 Par 2] “We thus propose a new design of this part of the mixer as shown in Fig. 4a. The shape and dimensions of the other parts are as in the original model (see Fig. 4b)” [Fig.4] Shows a 3D model of the first geometry of the mixing tool (4b) next to the new, optimized model of the second mixing tool) using the validated CFD simulation model. ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of two concentration measurements to see if they match in order to validate the model) representing the second geometry with a second digital 3D model; ([Fig.4] Shows a 3D model of the first geometry of the mixing tool (4b) next to the new, optimized model of the second mixing tool) providing the second digital 3D model ([Fig.4] Shows a 3D model of the first geometry of the mixing tool (4b) next to the new, optimized model of the second mixing tool (4a)) to the validated CFD simulation model ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of two concentration measurements to see if they match in order to validate the model)
to simulate the mixing of the plurality of fluids, and implementing, via the processor, the CFD simulation model to generate an updated ([Fig. 5] Shows a side view of the output of a CFD using the second model, including an updated distribution output generated by the new model[Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM” ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”)
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([Fig. 5] Shows a side view of the output of a CFD using the second model, including an updated distribution output generated by the new model[Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM”) to an ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”) ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”) making a physical tool based on second digital 3D model([Fig.4] Shows a 3D model of the first geometry of the mixing tool (4b) next to the new, optimized model of the second mixing tool (4a) [Page 13 Par 1- 4] “We validated the simulation results by measuring the actual pressure drop. The difference between the upstream and downstream pressures was measured for a flow velocity of 1 m/s. We also calculated the Z-factor, which is the ratio of the pressure drop of the TDM to that of an empty pipe of the same diameter (8 mm) and length (100 mm): … The experimentally determined pressure drops for all the models in Fig. 11 were roughly consistent with those obtained by CFD simulation, although the values for the different blade models differed significantly.” [Fig. 11] Shows comparisons between the simulated and experimental (i.e. performed using a physical tool representative of the corresponding design) performance of each of the optimized models)
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([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”)
Hanada does not explicitly teach generate a particle density distribution of the mixture; converting the particle density distribution of the mixture to a first spatial distribution of a concentration; calculating a fluid concentration at a given point by weighting adjacent discrete fluid representing particles of the particle density distribution; measuring, via an X-ray scan, a second spatial distribution of concentration inside the tool; the first and second spatial distributions of concentration; first and second spatial distributions; a second particle density distribution of the mixture inside the tool; converting an updated particle density distribution to an updated spatial distribution of a concentration; determining a spatial distribution of mixing index based on the updated spatial distribution; producing a physical mixing tool in response to determining that the updated spatial distribution of a concentration achieves a target performance.
Chu makes obvious generate a particle density distribution of the mixture; converting the particle density distribution of the mixture to a first spatial distribution of a concentration ([Fig. 17] Shows visualizations of particle density distributions within the DMC device model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively, while Fig. 17(II)(a) and Fig. 17(II)(b) are mapped to the second particle density distribution and second spatial distribution of a concentration, respectively)
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at least in part by calculating a fluid concentration at a given point ([Fig. 17] Shows particle density distributions within the model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively. Note that an indication of the concentration is represented at each point in the figure) by weighting adjacent discrete fluid representing particles of the particle density distribution; ([Page 900 Col 1 Par 1] “The operational parameters used in the simulation are summarised in Table 3. M:C ratio, defined as the weight ratio between medium and coal, in the validation case is 5.4.” [Fig. 17] Shows particle density distributions within the model as well as corresponding concentration distributions below. [Fig. 12] Shows the density distribution of the medium (i.e. one of the mixture components) isolated from the other mixture components) ([Fig. 17] Shows visualizations of particle density distributions within the DMC device model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively, while Fig. 17(II)(a) and Fig. 17(II)(b) are mapped to the second particle density distribution and second spatial distribution of a concentration, respectively) a second particle density distribution of the mixture inside the tool; ([Fig. 17] Shows visualizations of particle density distributions within the DMC device model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively, while Fig. 17(II)(a) and Fig. 17(II)(b) are mapped to the second particle density distribution and second spatial distribution of a concentration, respectively) converting an updated particle density distribution to an updated spatial distribution of a concentration; ([Fig. 17(I)(b) and Fig. 17(II)(b)] Show a second (i.e. updated and modified from the first) particle density distribution and a corresponding second (i.e. updated) spatial distribution of a concentration below it) ([Fig. 17(I)(b) and Fig. 17(II)(b)] Show a second (i.e. updated and modified from the first) particle density distribution and a corresponding second (i.e. updated) spatial distribution of a concentration below it) ([Fig. 17(I)(b) and Fig. 17(II)(b)] Show a second (i.e. updated and modified from the first) particle density distribution and a corresponding second (i.e. updated) spatial distribution of a concentration below it)
Chu is analogous art because it is within the field of particle tracking computational fluid dynamics. It would have been obvious to one of ordinary skill in the art to combine it with Hanada before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to expand the mixer modelling beyond just liquids. As mentioned by Chu, the modelling of gas/liquid and solid particulate mixtures is severely lacking due to its complexity ([Page 893 Col 1 Par 1] “Dense medium cyclone (DMC) is a high-tonnage device that has been widely used to upgrade run-of-mine coal in the coal industry by separating gangue from product coal. The flow in a DMC is very complicated with the presence of swirling turbulence, an air-core and segregation of medium and coal particles. It involves multiple phases: air, water, coal and magnetite particles of different sizes, densities and other properties. The flow in DMCs is so complex that the experimental study of DMCs is quite expensive and the numerical modelling of DMCs is very challenging. So far, there are still very limited data available about the effects of the operational, geometrical and material parameters on the performance of DMCs.”) To this end, Chu presents a system for effectively modelling combinations of gas, liquids, and solid particulates ([Page 893 Col 2 Par 3- Page 894 Col 1 Par 2] “On the other hand, a coupled Computational Fluid Dynamics and Discrete Element Method (CFD–DEM), also known as the Combined Continuum and Discrete Model (CCDM), has found wide application in particle–fluid flow systems … ). Compared to the LPT model, it has an obvious advantage that it can fully account for particle–particle and particle–fluid interaction forces. In this work, the CCDM two-phase flow model developed in our group is extended to model the complex flow in DMCs. The model is first validated against experimental measurements and then the effect of coal particle density distribution, as a major parameter to distinguish coal type, is investigated … In this work, the continuous phase represents a mixture of water, air, and magnetite particles of different sizes and densities. There are also some new features in the present CFD–DEM model as described below.”) Overall, one of ordinary skill in the art would have recognized that combining Hanada with Chu would result in a mixer simulation system capable of effectively simulating the mixture of a much wider variety of different materials, including materials in different phases of matter.
The combination of Hanada and Chu does not explicitly teach measuring, via an X-ray scan, data; determining a spatial distribution of mixing index; producing a physical mixing tool in response to determining that a model of that tool achieves a target performance.
Rabha makes obvious measuring, via an X-ray scan, data ([Abstract] “In the present work, the dispersion provided by a helical static mixer in a vertical pipe at turbulent gas– liquid flow conditions was studied using ultrafast electron beam X-ray tomography.” [Page 3 Col 2 Par 3] “liquid flow in a pipe packed with three, six and nine helical static mixer elements was investigated using ultrafast electron beam X-ray tomography. The flow conditions were studied for two representative superficial liquid velocities”)
Rabha is analogous art because it is within the field of fluid mixing. It would have been obvious to one of ordinary skill in the art to combine it with Hanada and Chu before the effect filing date. One of ordinary skill in the art would have been motivated to make this combination in make the system even more accurate with mixtures besides liquid/liquid mixtures. As noted by Rabha, previous experiments and models of the performance of static mixers have failed to accurately capture the behavior of gas/liquid mixtures, noting that improved models can only be made by the use of improved measurement techniques ([Page 3 Col 1 Par 4 – Col 2 Par 2] “Up till now, the known information about the fluids mixing inside the helical static mixer is only for the distributive mixing of two liquids in laminar flow using experimental techniques such as planar laser induced Fluorescence (PLIF) [22] and CFD simulations [23,24]. The flow development for the gas–liquid dispersive mixing is still theoretical; neither experiments nor numerical simulations have been done to investigate the flow dispersion inside the helical static mixer. Moreover, information addressing the effect of the number of the helical static mixer elements along with the slip (fluid velocity) ratio on the decisive parameters like specific interfacial area, pressure drop, and bubble size reduction in the gas–liquid systems is largely missing. Similarly, no numerical investigations for the gas–liquid flow development in the helical static mixer have been done yet. The reason could be the lack of adequate closure models in complex geometries particularly bubble breakup models, and most importantly unavailability of any experimental data for model validation. The present knowledge on the gas–liquid fluid dynamics in helical static mixers can only improve by use of an advanced measurement technique, which can disclose the flow inside the opaque and complex geometries.”) To this end, Rabha presents a system for accurately analyzing the behavior of gas/liquid mixtures in helical static mixers ([Page 3 Col 2 Par 2 – Par 3] “Thus, the present study aimed to target this problem and provide a high-resolution insight into the flow development inside static mixer structures, elucidating both gas holdup and interfacial area development. In this work, turbulent dispersion of co-current upward gas– liquid flow in a pipe packed with three, six and nine helical static mixer elements was investigated using ultrafast electron beam X-ray tomography.”) Overall, one of ordinary skill in the art would have recognized that combining Rabha with Hanada and Chu would allow for the simulations to be even more accurate by integrating the use of more precise measurement methods, allowing for more higher precision model validation and refinement.
The combination of Hanada, Chu, and Rabha does not explicitly teach determining a spatial distribution of mixing index; producing a physical mixing tool in response to determining that a model of that tool achieves a target performance.
Cho makes obvious determining a spatial distribution of mixing index;
([Fig. 12] Shows a spatial distribution of mixing indices)
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Cho is analogous art because it is within the field of mixing simulation. It would have been obvious to one of ordinary skill in the art to combine Cho with Hanada, Chu, and Rabha before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to generate a comprehensive metric that accurately reflects how mixed a mixture is at a certain point. As noted by Cho, accurate mixture measurement is extremely important to a variety of industries, and previously tried mixing indices can provide inaccurate information ([Page 434 Col 1 Par 1] “In various industries, such as food [1], drug [2], chemical [3] and cement industries [4,5], perfect mixing is essential as there are different types of particles that have different sizes and characteristics” [Page 435 Col 1 Par 1] “The generalized mean mixing index (GMMI) uses the contribution of all particles in calculating the index [24] although the GMMI routinely over-predicts the mixing (with a mixing index higher than 1) and does not provide a single index for a mixture. Subsequently, the modified generalized mean mixing index (MGMMI) [25] was introduced to bind a mixing index between 0 and 1. However, the disadvantage of the MGMMI involves overestimating the mixing state such that it results in a value close to 1 even if mixtures are segregated.” [Page 437 Col 2 Par 2] “Both GMMI and MGMMI have advantages, including the ease of implementation, speed of evaluation and the lack of dependence on sample size. Nevertheless, they involve serious disadvantages because they are based on the mean position of particles. For example, both GMMI and MGMMI can provide a value close to 1 even when mixing is locally poor. The overestimated index can result in wrong information with respect to mixing conditions and thus this may correspond to an ideal index.”) To this end, Cho presents a more accurate mixing index that Is better capable of taking into account local conditions ([Page 435 Col 1 Par 2] “The present study focuses on resolving existing issues in extant mixing indices by presenting a new mixing index based on subdomain analysis. The proposed method uses all particles to evaluate the mixing condition and therefore constitutes a non-sampling mixing index. The advantage of the proposed index is that it provides a linear correlation between the mixing condition of mixtures and the mixing index that is close to 0 when mixing does not occur and close to 1 when near perfect mixing occurs.”) Overall, one of ordinary skill in the art would have recognized that combining the mixing index of Cho with Hanada, Chu, and Rabha would result in a more accurate, easier to interpret mixing determination system.
Henry makes obvious producing a physical mixing tool in response to determining that a model of that tool achieves a target performance ([Par 194-202] “The design process may comprise any of the embodiments previously described herein relating to the process for preparing the catalytic static mixer (CSM) element comprising additive manufacturing, such as 3D printing. The additive manufacturing provides flexibility in preliminary design and testing, and further re-design and re-configuration of the static mixers to facilitate development of more commercially viable static mixers. A process for design and manufacture of a catalytic static mixer (CSM) element for a continuous flow chemical reactor chamber may comprise the steps of: designing a prototype static mixer element comprising a scaffold defining a plurality of passages configured for mixing one or more fluidic reactants during flow and reaction thereof through the mixer; … testing the prototype CSM for at least one of suitability for catalytic coating or operational performance and durability in a continuous flow chemical reactor; … redesigning the static mixer element to enhance at least one of suitability for catalytic coating or operational performance and durability in a continuous flow chemical reactor; and manufacturing the redesigned static mixer element comprising a redesigned scaffold defining a plurality of passages configured for mixing one or more fluidic reactants during flow and reaction thereof through the mixer, … The step of testing the CSM, re-designing the static mixer element and manufacturing the CSM may be repeated one or more times to further enhance at least one of performance, durability, manufacturability, or scaffold suitability for catalytic coating. Computational fluid dynamics (CFD) software can be used in the design (or re-design) to obtain various enhanced configurations of the CSMs and scaffolds, which will by determined by the desired applications and associated catalytic reactions. For example, a design process can be used to develop configurations and geometries having enhanced microscopic and macroscopic mixing properties, which may be indicated by the turbulent length scales in turbulent flow, in the vicinity of the scaffold and hence the catalyst, while also providing enhanced heat transfer properties. [Par 228-229] “The design process may also comprise an iterative approach to optimise or enhance at least one of performance, durability, manufacturability, or scaffold suitability for catalytic coating. For example, if the results can be enhanced by certain changes to the geometry, then changes (based on knowledge of fluid dynamics) can be made to the geometry and the design optimisation procedure repeated. The initial geometry may be chosen and optimised to enhance various characteristics of the static mixer element, such as the specific surface area, volume displacement ratio, line-of-sight accessibility for cold-spraying, strength and stability for high flow rates, suitability for fabrication using additive manufacturing, or to achieve a high degree of chaotic advection, turbulent mixing, catalytic interactions, or heat transfer.”)
Henry is analogous art because it is within the field of mixing tool geometry optimization. It would have been obvious to one of ordinary skill in the art to combine it with Hanada, Chu, Rabha, and Cho before the effective filing date. One or ordinary skill in the art would have been motivated to make this combination in order to further improve efficiency through design optimization and allow for mixers that are more easily removable and replaceable. Henry notes the particular need for such a mixer and associated design system ([Par 4] “Towards improving process productivity through increased reaction yields, there is a clear need for developing enhanced static mixers and/or reaction chambers for continuous flow chemical reactors that are readily removable and easily replaced, allow further re-design enhancement and are capable of providing more efficient mixing, heat transfer and catalytic reaction of reactant chemical and/or electrochemical reactants.”) To this end, Henry presents a method for the optimal design of static mixers that are more efficient at both the mechanical mixing itself and the activation of mixed chemicals, while allowing the mixers to be easily replaced for easy maintenance ([Par 5] “The present inventors have undertaken significant research and development into alternative continuous flow chemical reactors and have identified that static mixers can be provided with a catalytic surface such that the resulting static mixer is capable of being used with a continuous flow chemical reactor. It was surprisingly found that incorporating catalytic material on the surface of additive manufactured static mixers can provide catalytic static mixers that can be configured to be readily removable and easily replaced, allow for further re-design enhancement, and provide for efficient mixing, heat transfer and catalytic reaction of reactants in continuous flow chemical reactors. The static mixers may be provided for use with in-line continuous flow reactors as inserts or as modular packages with the static mixer as an integral part of a section of the reactor tube itself.”) Overall, one of ordinary skill in the art would have recognized that combining Henry with Hanada, Chu, Rabha, and Cho would result in a more efficient mixing tool design system that also integrates the flexibility of easily removable and swappable components.
Claim 9. Hanada makes obvious further comprising visualizing at least one of the ([Fig. 16] Shows a visualization of a comparison of two concentration measurements (one from the output of the model, the other measured experimentally [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM”)
Chu makes obvious visualizing at least one of the spatial distributions and ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. Note that the color overlays on the tool geometries represent different densities/ concentrations [Page 894 Col 2 Par 1] “VOF and multiphase mixture flow models are available in commercial CFD software packages (e.g. Fluent) and used to model the gas– water–magnetite three-phase medium flow in the DMC”)
Cho makes obvious visualizing a mixing index to guide a user to determine whether the plurality of mixture components is uniformly mixed. ([Fig. 12] Shows a visualization of a spatial distribution of mixing indices. It can be seen from the index values how uniformly mixed the fluids are)
Claim 12. Hanada makes obvious wherein comparing the first and second([Fig. 16] Shows a visualization of a comparison of two concentration measurements (one from the output of the model, the other measured experimentally [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM”)
Chu makes obvious first and second spatial distributions of concentration, visualizing the first and second spatial distributions of concentration ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. Note that the color overlays on the tool geometries represent different densities/ concentrations [Page 894 Col 2 Par 1] “VOF and multiphase mixture flow models are available in commercial CFD software packages (e.g. Fluent) and used to model the gas– water–magnetite three-phase medium flow in the DMC”)
Claim 13. Hanada makes obvious wherein the ([Fig. 16] Shows a visualization of a comparison of two concentration measurements (one from the output of the model, the other measured experimentally [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM”)
Chu makes obvious visualizing further comprises overlaying digital representations of first and second spatial distributions in a graphic user interface. ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. Note that the color overlays on the tool geometries represent different densities/ concentrations [Page 894 Col 2 Par 1] “VOF and multiphase mixture flow models are available in commercial CFD software packages (e.g. Fluent) and used to model the gas– water–magnetite three-phase medium flow in the DMC”)
Claim 14. Hanada makes obvious ([Fig. 16] Shows a visualization of a comparison of two concentration measurements (one from the output of the model, the other measured experimentally)
Chu makes obvious wherein overlaying the digital representations of first and second spatial distributions comprises importing corresponding polygon surfaces to a same coordinate system in the graphic user interface. ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. Note that the color overlays on the tool geometries represent different densities/ concentrations and that the geometry of the device’s surfaces (i.e. the corresponding polygon surfaces) are used in the visualization in the same coordinate system of the colored density/concentration indications [Page 894 Col 2 Par 1] “VOF and multiphase mixture flow models are available in commercial CFD software packages (e.g. Fluent) and used to model the gas– water–magnetite three-phase medium flow in the DMC”)
Claim 15. Hanada makes obvious wherein the mixing of the plurality of fluids via the simulation with the CFD simulation model and via the mixing tool is under the same operation conditions. ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … we injected a pulse of salt water of adjusted conductivity 100mS/cm into a continuous water flow generated by a volute pump, and measured the conductivity of the mixture downstream of the mixer using a conductivity measuring system that we developed (see Fig. 14)… The residence measurement circuit is shown in Fig.15. … Thus, it is possible to determine the concentration of salt water from the resistance value… We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of two concentration measurements to see if they match in order to validate the model [Page 13 Par 1-6] “We validated the simulation results by measuring the actual pressure drop. The difference between the upstream and downstream pressures was measured for a flow velocity of 1m/s. … The experimentally determined pressure drops for all the models in Fig. 11 were roughly consistent with those obtained by CFD simulation”)
(2) Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over CFD geometrical optimization to improve mixing performance of axial mixer (Hereinafter Hanada) in view of CFD–DEM study of the effect of particle density distribution on the multiphase flow and performance of dense medium cyclone (Hereinafter Chu) in further view of Visualization and quantitative analysis of dispersive mixing by a helical static mixer in upward co-current gas–liquid flow (Hereinafter Rabha) as well as A non-sampling mixing index for multicomponent mixtures (Hereinafter Cho) in addition to Henry (US 20190388859 A1) and Identifying Nearly Equally Spaced Isosurfaces for Volumetric Data Sets (hereinafter Imre)
Claim 3. Hanada makes obvious wherein the first and second ([Page 16 Par 1 – Page 18 Par 1] “To verify the validity of the CFD analysis results, we conducted a real flow test using several practical models. … we injected a pulse of salt water of adjusted conductivity 100mS/cm into a continuous water flow generated by a volute pump, and measured the conductivity of the mixture downstream of the mixer using a conductivity measuring system that we developed (see Fig. 14)… The residence measurement circuit is shown in Fig.15. … Thus, it is possible to determine the concentration of salt water from the resistance value… We initially conducted a single pulse injection test to examine the difference between the experimental and simulation results. As can be seen from Fig. 16, the experimental results for Model No. 6 are exceptionally consistent with the simulation results.” [Fig. 16] Shows a comparison of the graph shapes of two concentration measurements (one from the output of the model, the other measured experimentally) to see if they match in order to validate the model)
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Chu makes obvious ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively, while Fig. 17(II)(a) and Fig. 17(II)(b) are mapped to the second particle density distribution and second spatial distribution of a concentration, respectively)
The combination of Hanada, Chu, Rabha, Cho, and Henry fails to make obvious first and second sets of iso-surfaces; comparing the similarity of data represented by first and second isosurfaces
Imre makes obvious first and second sets of iso-surfaces; comparing the similarity of data represented by first and second isosurfaces ([Page 3 Col 1 Par 1] “The similarity between two isosurfaces is defined as the mutual information shared by the distance fields of the two isosurfaces. Representative isosurfaces are identified using the iso surface similarity map, which stores all pairwise similarity values” [Figure 6] Shows several sets of isosurfaces being compared.)
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Imre is analogous art because it is within the field of numeric simulation evaluation. It would have been obvious to one of ordinary skill in the art to combine it with Hanada, Chu, Rabha, Cho, and Henry before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to better analyze and visualize the model output. As mentioned by Imre, numerical simulations like CFD can produce large amounts of data that can be difficult to interpret. To this end, using isosurfaces can make data easier to digest and visualize. ([Page 1 Col 1 Par 1] “Numerical simulations are extensively used by scientists to observe various phenomena that are not easily captured by real experiments. These simulations normally produce an ample amount of data, requiring effective tools to visualize and analyze them. A typical visualization presents the simulation results as a series of volumes. One of the essential techniques to gain insights into these volumes is isosurface rendering. To de scribe the structure of a volume, one can extract and visualize isosurfaces. These surfaces describe surface geometries with all points sharing the same isovalue.) Overall, one of ordinary skill in the art would have recognized that combining Imre with Hanada, Chu, Rabha, Cho, and Henry would result in model outputs that were significantly easier to interpret.
(3) Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over CFD geometrical optimization to improve mixing performance of axial mixer (Hereinafter Hanada) in view of CFD–DEM study of the effect of particle density distribution on the multiphase flow and performance of dense medium cyclone (Hereinafter Chu) in further view of Visualization and quantitative analysis of dispersive mixing by a helical static mixer in upward co-current gas–liquid flow (Hereinafter Rabha) as well as A non-sampling mixing index for multicomponent mixtures (Hereinafter Cho) in addition to Henry (US 20190388859 A1) and Formal calibration methodology for CFD models of naturally ventilated indoor environments (Hereinafter Hajdukiewicz)
Claim 10. Hanada makes obvious wherein when the first and second ([Fig. 5] Shows a side view of the output of a CFD using the second model, including an updated distribution output generated by the new model[Page 5 Par 1] “Hence, the aim of the present study was the geometrical optimization of a newly developed axial TDM to achieve a low pressure drop and high mixing performance. We used computational fluid dynamics (CFD) to determine the part where significant pressure loss occurred, and according modified the geometry of the element. The branch path arrangement was also optimized by CFD to improve the mixing performance” [Page 5 Par 2] “We used the commercially available STAR-CCM+ software (ver.7.04.012) (CD-adapco) to conduct steady-state and unsteady-state analyses to clarify the axial mixing mechanism of the TDM” ([Page 4 Par 2] “According to its basic principle, the concentration profile is considered to be divided into a number of branch paths, and the divided profiles then merge again after some time corresponding to each branch path׳s length (see Fig. 2). This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction.” [Page 6 Par 2] “Fig. 5b shows the pressure distribution determined by the model. It is obvious from Fig. 5b that the static pressure around the tip of the element is lower than that of the original model, and the congestion of the isobar at this point is thus relaxed.”)
Chu makes obvious first and second spatial distributions ([Fig. 17] Shows visualizations of particle density distributions within the model as well as corresponding concentration distributions below. In particular Fig. 17(I)(a) and Fig. 17(I)(b) are mapped to this first particle density distribution and first spatial distribution of a concentration, respectively, while Fig. 17(II)(a) and Fig. 17(II)(b) are mapped to the second particle density distribution and second spatial distribution of a concentration, respectively)
The combination of Hanada, Chu, Rabha, Cho, and Henry fails to make obvious when experimental and measured data do not match with each other, adjusting the CFD simulation;
Hajdukiewicz makes obvious when experimental and measured data do not match with each other, adjusting the CFD simulation; ([Fig. 1] Shows the model calibration process. Note that the “validation data” is measured data.)
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Hajdukiewicz is analogous art because it is within the field of CFD simulation. It would have been obvious to one of ordinary skill in the art to combine it with Hanada, Chu, Rabha, Cho, and Henry before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to produce more accurate simulations. As noted by Hajdukiewicz, uncalibrated CFD simulations tend to suffer from a lack of accuracy ([Page 2 Par 6] “However, the reliability of the results remains a significant concern regarding CFD simulations. While CFD models can produce visually appealing results, accuracy is often a key issue [9]. In order to produce credible and verifiable results, the CFD model should be created using verified software and experimental data to support model validation”) To this end, Hajdukiewicz presents a method for improving the accuracy of CFD simulations by validating them based on measured data. ([Page 3 Par 2] “Calibration is, de facto, the adjustment of numerical and physical model input parameters to amend the agreement between the model results and corresponding experimental data” [Page 3 Par 4] “The goal of the research was to develop a consistent and systematic calibration methodology of CFD models ... During the CFD model calibration process the input boundary conditions that most influenced the model output were identified. Those input parameters were adjusted to obtain a reliable CFD model representing the real environment.”) Overall, one of ordinary skill in the art would have recognized that combining Hajdukiewicz with Hanada, Chu, Rabha, Cho, and Henry would result in a significantly more accurate simulation.
(4) Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over CFD geometrical optimization to improve mixing performance of axial mixer (Hereinafter Hanada) in view of CFD–DEM study of the effect of particle density distribution on the multiphase flow and performance of dense medium cyclone (Hereinafter Chu) in further view of Visualization and quantitative analysis of dispersive mixing by a helical static mixer in upward co-current gas–liquid flow (Hereinafter Rabha) as well as A non-sampling mixing index for multicomponent mixtures (Hereinafter Cho) in addition to Henry (US 20190388859 A1) and Curing reaction of epoxy resin composed of mixed base resin and curing agent: Experiments and molecular simulation (Hereinafter Okabe)
Claim 16. Hanada makes obvious wherein the plurality of fluids ([Page 4 Par 2] “This principle is similar to the moving average principle, and the component fluids are therefore effectively mixed in the flow direction” [Page 1 Par 1- Page 2 Par 2] “Fig. 1a shows a model of mixing by a conventional static mixer, where the closed and open circles represent the mixture components” [Fig. 1] Shows a model of fluids mixed by a mixing tool)
The combination of Hanada, Chu, Rabha, Cho, and Henry fails to make obvious a mixture that includes two or more specifies of an adhesive.
Okabe makes obvious a mixture that includes two or more specifies of an adhesive. ([Page 1 Col 2 Par 2] “In this study, we investigated the influence of base resin and curing agent and their mixture on the curing characteristics. We conducted curing experiments focusing on epoxy resin with mixed base resin and curing agent, and compared the curing characteristics. We also simulated the curing reaction process on a molecular scale using molecular simulation, and investigated the effect of differences in resin composition on the curing characteristics. In the molecular simulation…” [Page 4 Col 1 Par 2-3] “Using atomistic simulation, we investigated the influence of selection and mixture of base resins and curing agents on the curing characteristics … In this simulation, the curing reaction was modeled using the molecular orbital method (MO) and MD. MO was used to calculate the energy curve when one monomer of base resin and one monomer of curing agent approached and reacted”)
Okabe is analogous art because it is within the field of adhesive mixing simulation. It would have been obvious to one of ordinary skill in the art to combine it with Hanada, Chu, Rabha, Cho, and Henry before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to expand the simulation to not just take mechanical mixing into account, but also the chemical reactions between the mixture components. As mentioned by Okabe, typical adhesive mixing simulation systems do not consider the actual chemical reaction between adhesive mixture components ([Page 1 Col 1 Par 2 – Col 2 Par 3] “For example, Rosu et al. [4] and Silvia et al. [6] investigated the cure kinetics of epoxy resin using the addition of reactive diluent and the modifier of epoxy thermoset. However, this model does not consider the chemical reactions based on the molecular structure in the curing reaction process. Komarov et al. [7] evaluated the mechanical characteristics of a cured product using the coarse-grained molecular dynamics method (MD) at the functional group level. They assumed that chemical reactions occur when the reactive sites approach a fixed distance. Their reaction model is widely used [8e10]. But, this technique is problematic in that it does not consider chemical reactivity. Since it is desirable for cure time to be controllable with the selection of base resin and curing agent, the simulation should consider chemical reactivity.”) To this end, Okabe presents a simulation system that considers the chemical reactions between an epoxy base resin and curing agent during mixing ([Page 1 Col 2 Par 2] “In this study, we investigated the influence of base resin and curing agent and their mixture on the curing characteristics. We conducted curing experiments focusing on epoxy resin with mixed base resin and curing agent, and compared the curing characteristics. We also simulated the curing reaction process on a molecular scale using molecular simulation, and investigated the effect of differences in resin composition on the curing characteristics. In the molecular simulation, the curing of an epoxy resin was simulated by considering the effect of activation energy, heat of formation, and polarization of a molecule”) Overall, one of ordinary skill in the would have recognized that integrating the chemical reaction simulation of Okabe with the largely kinetic simulations of Hanada, Chu, Rabha, Cho, and Henry would result in a simulation system that is better able to capture the whole of interactions between adhesive mixture components, resulting in a more complete, accurate simulation.
Conclusion
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/M.P.M./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187