DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, 7 and 15 have been amended. Claims 1-20 have been examined.
Response to Arguments/Amendments
The prior claim objections are withdrawn in view of the 4/27/2026 claim amendments.
Applicant's arguments on pp. 9-11 filed 4/27/2026 have been fully considered but they are not persuasive.
On pp. 9-10 of Applicant’s remarks, Applicant argues that “the combination of Chen and Goncalves fails to teach or suggest ’recommending ... one or more of a working volume or an impeller speed for a given product based on the trained machine learning model’ as recited by claim 1.”
Applicant notes that Chen “relates to geometric parameters for expandable pipe design used in ‘sand control in oil and gas production,’” which is “fundamentally different from the tank-based mixing systems with impellers recited by claim 1 which requires ‘steady state mixing configurations in which inlet streams are mixed in tanks.’” In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). It should be noted that Chen is more particularly directed to “neural-network based surrogate model construction methods” (see ¶ 0045). Chen’s expandable pipe is merely provided as an exemplary application (see ¶ 0049) for a process of training a tool model to find optimal parameter values (see ¶ 0054 and Fig. 4). Even if Chen fails to expressly disclose “tank-based mixing systems with impellers” as argued by Applicant, Chen’s models are ready for improvement using Goncalves’ mixing system as noted in the rejection.
Applicant also notes that Goncalves: “does not ‘recommend’ an impeller speed,” and “only describes using rotation speed as an input parameter for CFO simulations and surrogate model building.” As noted above, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. As cited in the rejection, Chen is relied upon to teach recommendations of system parameters based upon a trained machine learning model, while Goncalves is relied upon for a general teaching of process modeling including mixing performance evaluation using parameters that include impeller speed. Chen’s recommendations combine with Goncalves’ parameters, which one of ordinary skill in the art would understand as being able to provide design optimizations as indicated in the claim rejection.
Applicant also argues: “Goncalves' inline mixer involves a rotor-stator inline device, which is structurally different from a tank where inlet streams are mixed with working volume and impeller speed.” Applicant has not clearly explained how Goncalves’ inline mixer is particularly “structurally different.” Goncalves expressly describes a “tank” where fluid is mixed in terms of “flow,” which could apply to a broad but reasonable interpretation of the claimed inlet streams. Applicant’s argument is not persuasive.
On pp. 10-11, Applicant argues that the motivation to combine provided in the prior rejection (i.e. “to provide design optimization”) was insufficient. It is noted that the top of p. 4 of the 2/5/2026 rejection provides additional motivation “in order to reproduce key predictions of a complete CFD simulation at a fraction of the computational cost for commonly used industrial equipment as suggested by Goncalves (see p. 3, section 2 and p. 7, section 3).” In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art recognizes that consideration of model parameters is essential in order to provide optimal models and Goncalves clearly teaches consideration of such parameters for inexpensive CFD simulation. Goncalves’ teaching of “complete CFD simulation at a fraction of the computational cost” (see p. 2, section 2) is not a generic teaching, but instead is specifically directed to optimization of CFD simulation. Applicant’s argument is not persuasive.
Further arguments on p. 11 of Applicant’s remarks are based upon previous arguments, and are not persuasive for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 7, 9, 11-12 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 20080228680 by Chen et al. ("Chen") in view of “Data-driven surrogate modeling and benchmarking for process equipment” by Gonçalves et al. (“Goncalves”).
In regard to claim 1, Chen discloses:
1. A method, comprising: See at least Fig. 4, broadly depicting a method.
generating, by one or more processors, a plurality of training computational fluid dynamic (CFD) models for a plurality of training … configurations …, wherein each training CFD model is generated based on a plurality of … factors associated with each training … configuration;. See Chen ¶ 0026, “Usage of high-fidelity simulation tools such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), for example, has become standard practice in engineering today.” … ¶ 0053, “The FEA model takes the four parameters in the design space as input variables, and performs a simulation to measure the resulting plastic strain and tensile load at the given expansion rate.” Also ¶ 0056, “Process 412 begins with the engineer obtaining a sparse data set from the high-fidelity tool model.”
Chen does not expressly disclose:
steady state mixing configurations in which inlet streams are mixed in tanks, … training CFD model is generated based on steady state mixing factors associated with each training steady state mixing configuration;
This is taught by Goncalves. Goncalves, p. 7, last paragraph of section 2.3, “All simulations utilized second-order discretization schemes for the spatial terms, and steady state was assumed.” Also p. 10, section 3.3 “Case 3: flow in an inline mixer (2D)”:
For this system, the power number (per nondimensional length Le=d) Np,2D is chosen as the main variable of interest. It is given by:
Np,2D = 2πT2D (19)
N2d4
where T2D is the torque applied per fluid mass and N is the rotation speed. Through dimensional analysis, it can be shown that this value is a function of the Reynolds number, Re ( d2πNð2-nÞd2=k), nondimensional gap between rotor and stator α ( D d =D), flow index n, and number of blades Nb.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Goncalves’s mixing configuration/factors in Chen’s CFD in order to reproduce key predictions of a complete CFD simulation at a fraction of the computational cost for commonly used industrial equipment as suggested by Goncalves (see p. 3, section 2 and p. 7, section 3).
calculating, by the one or more processors, a mixing quality for each training steady state mixing configuration using each respective training CFD model; Chen ¶ 0053, “measure the resulting plastic strain and tensile load at the given expansion rate.” Also ¶ 0060, “the computer obtains a pool of unique neural networks that each perform adequately over their respective training sets.” Also see Goncalves, p. 8, top paragraph, “To determine the mixing performance, cv at the outlet is calculated, which corresponds to the variance of the tracer concentration at the outlet … where the average concentration, c, is computed over the outlet area.”
generating, by the one or more processors, a training dataset that includes the steady state mixing factors associated with each training steady state mixing configuration, and the calculated mixing quality for each training steady state mixing configuration; Chen, ¶ 0061, “the computer formulates a diverse set of evolutionary selection parameters to form a pool of candidate ensembles.” Also ¶ 0067, “To this point (block 424 of FIG. 4), the neural network training and ensemble selection have been performed using the primary data set. In block 426, the secondary data set is used to select local ensembles from the pool of neural network ensembles developed in block 424.”
training, by the one or more processors, a machine learning model, using the training dataset, to predict mixing qualities for steady state mixing configurations based on steady state mixing factors associated with the steady state mixing configurations; Chen ¶ 0060, “Returning again to FIG. 4, the computer trains a set of neural networks in block 420, varying the training parameters for each network.”
recommending, by the one or more processors, … for a given product based on the trained machine learning model. Chen, Fig. 4, elements 406, 408, 430 and 432, depicting implementation using a recommended design based upon the trained ensemble. Also ¶ 0054, “The computer displays the optimal solution to the engineer in block 410 for use in implementing the tool.”
Chen does not expressly disclose: one or more of a working volume or an impeller speed. This is taught by Goncalves, p. 10, section 3.3, “The impeller rotates in the counterclockwise direction around the z-axis … where T₂D is the torque applied per fluid mass and N is the rotation speed.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Goncalves’s impeller speed with Chen’s recommended design in order to provide design optimization as suggested by Goncalves (see p. 1, section 1).
In regard to claim 2, Chen does not expressly disclose:
2. The method of claim 1, further comprising: applying, by the one or more processors, the trained machine learning model to new steady state mixing factors associated with a new steady state mixing configuration; and predicting, by the one or more processors, based on applying the trained machine learning model to the steady state mixing factors associated with the new steady state mixing configuration, a mixing quality for the new steady state mixing configuration. This is taught by Goncalves, section 3, discussing multiple mixing arrangements. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Goncalves’s multiple/new mixing arrangements with Chen’s simulations and predictions in order to test and benchmark new algorithms for active learning for a class of problems as suggested by Goncalves (see p. 1, Impact Statement).
In regard to claim 3, Chen and Goncalves also teach:
3. The method of claim 1, wherein the steady state mixing factors include one or more of: tank geometry, stirrer geometry, working volume, inlet configuration, outlet configuration, inlet flow rates for each inlet, outlet flow rates for each outlet, agitation speed, impeller speed, fluid Reynolds number for each substance, and other chemical and pharmaceutical properties for each substance. Goncalves, p. 10, section 3.3, “The impeller rotates in the counterclockwise direction around the z-axis … where T₂D is the torque applied per fluid mass and N is the rotation speed.”
In regard to claim 4, Chen and Goncalves also teach:
4. The method of claim 1, wherein the mixing quality is a measure of standard deviation of trace concentration in the tank. See Goncalves, p. 8, section 3.1, “To determine the mixing performance, cv at the outlet is calculated, which corresponds to the variance of the tracer concentration at the outlet.” Also see p. 20, “The variational method, based on estimates of standard deviation provided by the GP, presented the best performance on Cases 1 and 3 …”
In regard to claim 7, Chen discloses:
7. A computer system, comprising: one or more processors; and a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the processors to: See Chen, at least Fig. 1, along with ¶ 0047 “The engineer's tools include a computer 104 and software (represented by removable storage media 106), which they control via one or more input devices 108 and output devices 110. The software is stored in the computer's internal memory for execution by one or more processors. The software configures the processor to accept commands and data from the engineer, to process the data in accordance with one or more of the methods disclosed below, and to responsively provide predictions for the performance of the tool being developed or improved.”
All further limitations of claim 7 have been addressed in the above rejection of claim 1.
In regard to claims 9 and 11-12, parent claim 7 is addressed above.
All further limitations of claims 9 and 11-12 have been addressed in the above rejections of claims 2-4, respectively.
In regard to claim 15, Chen discloses:
15. A non-transitory computer readable storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to: See Chen, at least Fig. 1, along with ¶ 0047 “The engineer's tools include a computer 104 and software (represented by removable storage media 106), which they control via one or more input devices 108 and output devices 110. The software is stored in the computer's internal memory for execution by one or more processors. The software configures the processor to accept commands and data from the engineer, to process the data in accordance with one or more of the methods disclosed below, and to responsively provide predictions for the performance of the tool being developed or improved.”
All further limitations of claim 15 have been addressed in the above rejection of claim 1.
In regard to claims 16-18, parent claim 15 is addressed above.
All further limitations of claims 16-18 have been addressed in the above rejections of claims 2-4, respectively.
Claim(s) 5, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Goncalves as addressed above, and further in view of U.S. Patent Application Publication 20190005187 by Costello et al. ("Costello").
In regard to claim 5, Chen does not expressly disclose:
5. The method of claim 1, further comprising: generating, by the one or more processors, a testing computational fluid dynamic (CFD) model for a testing steady state mixing configuration in which inlet streams are mixed in tanks, wherein the testing CFD model is generated based on a plurality of steady state mixing factors associated with the testing steady state mixing configuration; calculating, by the one or more processors, a mixing quality for the testing steady state mixing configuration using the testing CFD model; applying, by the one or more processors, the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration; predicting, by the one or more processors, based on applying the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration, a quality of mixing for the testing steady state mixing configuration; and evaluating, by the one or more processors, the trained machine learning model by comparing the mixing quality calculated for the testing steady state mixing configuration using the testing CFD model and the mixing quality predicted for the testing steady state mixing configuration using the trained machine learning model. These steps correspond with a test run which essentially corresponds with the prior training which is addressed above in the rejection of parent claim 1. Chen does not expressly disclose testing. This is taught by Costello ¶ 0015, “Cross validation techniques trained multiple models with a subset of the training data, and then test these model on data not used for training.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Costello’s cross validation with the training of Chen and Goncalves in order to test model performance as suggested by Costello.
In regard to claim 13, parent claim 7 is addressed above.
All further limitations of claim 13 have been addressed in the above rejection of claim 5.
In regard to claim 19, parent claim 15 is addressed above.
All further limitations of claim 19 have been addressed in the above rejection of claim 5.
Claim(s) 6, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Goncalves as addressed above, and further in view of U.S. Patent Application Publication 20200082041 by Albert et al. ("Albert").
In regard to claim 6, Chen does not expressly disclose:
6. The method of claim 1, wherein the machine learning model is a deep learning model. This is taught by Albert ¶ 0055, “deep learning.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Albert’s deep learning with Chen’s learning models in order to utilize modular, ultra-fast, scalable models as suggested by Albert.
In regard to claim 14, parent claim 7 is addressed above.
All further limitations of claim 14 have been addressed in the above rejection of claim 6.
In regard to claim 20, parent claim 15 is addressed above.
All further limitations of claim 20 have been addressed in the above rejection of claim 6.
Claim(s) 8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Goncalves as addressed above, and further in view of U.S. Patent 10600005 to Gunes et al. ("Gunes").
In regard to claim 8, Chen does not expressly disclose:
8. The computer system of claim 7, wherein a first set of one or more processors, of the one or more processors, generate the plurality of training computational fluid dynamic (CFD) models, and wherein a second set of one or more processors, of the one or more processors, train the machine learning model. This is taught by Gunes, col. 8, lines 9-12, “Model training device 100 may coordinate access to training dataset 124 and validation dataset 126 that are distributed across distributed computing system 128 that may include one or more computing devices.” Also col. 22, lines 55-59, “Although some of the operational flows are presented in sequence, the various operations may be performed in various repetitions, concurrently (in parallel, for example, using threads and/or a distributed computing system), and/or in other orders than those that are illustrated.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Gunes’ distributed computing in order to execute operations in parallel (which save execution time) as suggested by Gunes.
In regard to claim 10, Chen does not expressly disclose:
10. The computer system of claim 9, wherein a third set of one or more processors, of the one or more processors, apply the trained machine learning model to the new steady state mixing factors associated with the new steady state mixing configuration and predict the mixing quality for the new steady state mixing configuration. This is taught by Gunes, col. 8, lines 9-12, “Model training device 100 may coordinate access to training dataset 124 and validation dataset 126 that are distributed across distributed computing system 128 that may include one or more computing devices.” Also col. 22, lines 55-59, “Although some of the operational flows are presented in sequence, the various operations may be performed in various repetitions, concurrently (in parallel, for example, using threads and/or a distributed computing system), and/or in other orders than those that are illustrated.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Gunes’ distributed computing in order to execute operations in parallel (which save execution time) as suggested by Gunes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Macqueron “Solid-Liquid Mixing in Stirred Vessels: Numerical Simulation, Experimental Validation and Suspension Quality Prediction Using Multivariate Regression and Machine Learning”. This reference teaches the use of machine learning to predict mixing quality using stirring speed and tank volume. See section 5.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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