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 .
Response to Amendment
The Amendment filed December 22nd, 2025 has been entered. Claims 1-2, 5-7, 10-12 and 15 remain pending in the application.
Response to Arguments
Applicant's arguments filed December 22nd, 2025 have been fully considered but they are not persuasive.
Applicant asserts that the claimed invention differs from the prior art in that it utilizes a pretrained model, including a neural network and Bayesian optimization techniques, and achieves improved predictive capability using limited instrumentation. However, the pending independent claims broadly recite “implementing a pretrained model” and, in certain claims, training using “a neural network technique and Bayesian optimization technique.” The claims do not recite any particular network architecture, model structure, computational infrastructure, dataset scale, or other specific technical implementation that would distinguish over known optimization and control approaches in the prior art. The prior art of record establishes that water hardness affects bubble formation and flotation behavior, that agitator speed affects induction time and particle recovery, and that ash rejection, combustible recovery, and efficiency index are recognized flotation performance metrics. Where variables are known to influence a common performance outcome, it would have been obvious to a person of ordinary skill in the art to coordinate adjustment of one variable in view of the other to achieve known optimization targets. The claimed use of a trained model to perform such optimization represents a predictable application of known control techniques to known result-effective variables.
With respect to the limitation reciting control “with data gathered solely from a hardness analyzer sensor and a shaft speed sensor,” selecting known relevant variables for control constitutes an obvious design choice where those variables are recognized in the art as affecting the same process performance characteristics. The record does not demonstrate that limiting the inputs to these two variables yields an unexpected result.
To the extent Applicant relies on specific model architectures, advanced learning techniques, or other implementation details for patentability, such features are not presently reflected in the claim language. Patentability is determined based on the claims as written.
Accordingly, the rejection under 35 U.S.C. §103 is maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson (WO 9745203 A1) in view of Koh and Smith "The effect of stirring speed and induction time on flotation, Minerals Engineering" and further in view of Zhang and Liu "Effect of Calcium Ions on Induction Time Between a Coal Particle and Air Bubble" and further in view of Gui "Flotation process design based on energy input and distribution". The combination of these references will be referred to as "modified Nelson".
Regarding claim 1, Nelson discloses a method of controlling a froth floatation process, the method comprising: receiving a water hardness value (the control computer receiving a variety of inputs listed on page 12 which also include the chemical or mineralogical composition of the feed [p. 13 line 23] the pH of the feed, concentrate, froth or tailings streams of which the water hardness is related as concentration of minerals such as calcium and magnesium will be shown as an increase in pH) and an agitator speed value corresponding to water and an agitator involved in the froth floatation process (Nelson [pg. 12 line 43] of input table and output to control of operations in right column as well), wherein the water hardness value is received from a hardness analyzer (“chemical composition” Nelson [p.28 line 1] which is analyzed by (Laser Induced Breakdown Spectroscopy (LIBS) [p. 21 line 8] "which are particularly useful in the determination of elemental composition in situ" provides the concentration of calcium and magnesium present, and this in conjunction with the pH [p. 13 line 25] sensors provides the water hardness values which are recorded and updated by the computer control system [see p. 9 line 24 through p. 10 line 8]); analyzing the agitator speed value vis-a-vis an optimal speed value required to achieve one or more target parameter values during the froth floatation process (Nelson describes on p. 15 that the “computerized monitoring and control system of this invention may utilize the aforementioned sensors to monitor various parameters with respect to time and thereby provide a detailed historical record of the flotation machine operation. This record may be used by the control computer to model flotation machine operation, adjust models for flotation machine operation, or generally learn how the flotation machine behaves in response to changes in various inputs” where the inputs are listed on p. 12 one of which is the agitation speed value); and implementing a pretrained model (Nelson p. 17 lines 6-7 “process model”), based on the analyzing (Nelson [p. 18 line 3 “heuristic” knowledge), to adjust the agitator speed value to the optimal speed value in such a manner that the one or more target parameter values are achieved during the froth floatation process (controller actuates at least one control device in response to the data received from the LIBS sensor and an internal process model [p.21 lines 24-25]), wherein the pretrained model is generated by: receiving a plurality of past water hardness values (chemical composition [p.28 line 1] which is analyzed by (Laser Induced Breakdown Spectroscopy (LIBS) [p. 21 line 8] "which are particularly useful in the determination of elemental composition in situ" would provide the concentration of calcium and magnesium present, and this in conjunction with the pH [p. 13 line 25] sensors provides the water hardness values which are recorded and updated by the computer control system (see p. 9 line 24 through p. 10 line 8) and a plurality of past agitator speed values corresponding to a past froth flotation process ([Nelson p. 18 lines 1-7] describes a learning process applied to the models which are based on operational values which are collected by the sensors); capturing a plurality of parameter values based on the plurality of past chemical composition values and the plurality of past agitator speed values (Nelson lists the values captured by sensors on p. 12); identifying the one or more target parameter values among the plurality of parameter values such that the one or more target parameter values optimizes efficiency of the froth floatation process (Nelson p. 18 lines 1-7 describes the data collected as “heuristic knowledge”, which is knowledge gained by learning and doing, which is synonymous with observing the data collected in response to changes within the system and identifying target values to optimize the process); and determining the optimal speed value of the agitator corresponding to the one or more target parameter values (Nelson p. 12 table indicates that agitator speed is one of the many operational values which are monitored, recorded and acted upon), wherein the froth floatation process is controlled based on the adjustment of the agitator speed value to the optimal speed value.
While Nelson discloses analyzing and recording chemical and mineralogical content within the system as well as the agitator speed value it does not refer to the water hardness value and comparing that to the agitator speed value corresponding to water based on the water hardness value or that wherein the one or more target parameter values comprises an ash rejection percentage value, a combustible recovery percentage value, and an efficiency index value. Nelson also does not limit the method of controlling a froth floatation process with data gathered solely from a hardness analyzer sensor and a shaft speed sensor.
Zhang and Liu disclose that the induction time of the coal particle and air bubble attachment in calcium solution increased with increasing concentration of calcium ions (p. 37 par. 3) and that induction time plays a critical role in flotation. (p.31 Introduction) Also, the shorter the induction time, the stronger the floatability of particles. (p.32 par. 1) This demonstrates the prior knowledge in the art that calcium ions found in hard water would consequently increase induction time of the coal particle and air bubble attachment and would be a relevant parameter to measure in any system designed to control the froth flotation system.
Koh and Smith disclose that for hydrophobic particles with short induction times, the attachment rate increases with increasing stirring speed (agitator speed) because of the increased rates of collision. But for low hydrophobicity cases, the induction times are longer. So, the attachment rate decreases with increasing stirring speed (agitator speed). ([p. 447 par. 3] and [Fig. 11]). This demonstrates the prior knowledge in the art that agitator speed plays a role in altering the induction times of coal particles and therefore will affect the froth flotation process. Because these two parameters are directly involved in the induction time of the coal particle which determines the floatability of coal particles it would have been obvious to one of ordinary skill in the art at the time of filing to combine the method of Nelson with the teachings of Zhang and Liu and further guidance of Koh and Smith and adapt the method to comprise receiving a water hardness value and comparing the agitator speed value corresponding to water based on the water hardness value. There would have been reasonable expectation of success as these values are already measured by the sensors of the system and method to optimize the process and obtain the predictable result of improving coal floatability by optimizing the induction time of coal into the froth.
Gui discloses a flotation process design based on the energy input directly related to the speed and toque of the shaft which drives the agitator shaft. (p.62 par. 3) Gui discloses that combustible matter recovery and ash content are the primary evaluation indexes for the flotation process and correlates the energy consumption related to driving the agitation speed to this efficiency index. ([p.66 section 3.2 par. 2] and Fig. 17).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Nelson’s model-based flotation control method in view of Zhang and Liu, Koh and Smith, and Gui to adjust agitator speed based on water hardness in order to optimize ash rejection and combustible recovery. Zhang and Liu establish that calcium ion concentration ( a contributor to water hardness) affects induction time and flotation performance, while Koh and Smith establish that agitator speed affects induction time and bubble-particle attachment behavior. Gui identifies ash rejection and combustible recovery as recognized flotation efficiency indices. Because both water hardness and agitator speed influence the same flotation performance characteristics, a person of ordinary skill in the art seeking to optimize these known performance metrics would have coordinated adjustment of agitation speed in view of water hardness conditions to maintain favorable induction time and floatability characteristics, with a reasonable expectation of success. The additional limitation that the process is controlled “with data gathered solely from a hardness analyzer sensor and a shaft speed sensor” does not render the claimed subject matter non-obvious, as selecting known result-effective variables as control inputs constitutes an obvious design choice absent evidence that limiting the inputs to these two parameters yields an unexpected technical result.
Regarding claim 2, modified Nelson teaches the method of claim 1, wherein the agitator speed value ("rotational speed of agitation mechanism" p.12 line 43) is measured by a shaft speed sensor communicatively coupled to the agitator. ("equipment parameters" and "equipment sensors" [p.14 line 1] and [Fig. 2 #44]) also described as "impeller speed" (p. 22 line 22) which will inherently contain a shaft, the speed of which will coordinate with the rotational speed of the agitation mechanism.
Regarding claim 11, Nelson discloses a non-transitory computer readable medium (central control computer[p.10 line 19]) including instructions stored thereon (computerized, "intelligent" systems for operating, controlling, monitoring and diagnosing various parameters [p.6 lines 4-6]) that when processed by at least one processor (microprocessor [p.19 line 2]) cause a control system to perform operations with data gathered solely from a hardness analyzer sensor and a shaft speed sensor (by suggestion of Zhang and Liu, Koh and Smith) comprising: receiving a water hardness value (via laser-induced breakdown spectroscopy sensors (LIBS) and /or laser induced mass spectroscopy sensors (LIMS sensors)[pg. 21 par. 2]) and an agitator speed value corresponding to water and an agitator involved in the froth floatation process (pg. 12 line 43), wherein the water hardness value is received from a hardness analyzer (LIBS and LIMS sensors [pg. 21 par. 2] are capable of analyzing chemical and mineralogical composition); analyzing the agitator speed value vis-a-vis an optimal speed value required to achieve one or more target parameter values during the froth floatation process based on the water hardness value (the processor of Nelson is capable of receiving the chemical composition from the LIBS or LIMS sensors and adjusting the agitator speed required to meet one or more target parameter values. If an apparatus with elements similar to the elements in the instant application is capable of performing the intended use of the control system described by the instant claim, the intended use will be considered anticipated or made obvious. See MPEP 2114.); and implementing a pretrained model, based on the analyzing (controller actuates at least one control device in response to the data received from the LIBS sensor and an internal process model [p.21 lines 24-25]), to adjust the agitator speed value to the optimal speed value in such a manner that the one or more target parameter values are achieved during the froth floatation process, (In response to one or more of the parameters sensed by the sensors [#42 and 44 shown in Fig. 2] the operation of the flotation machine and thereby its ultimate efficiency can be adjusted, changed, and preferably optimized.[p.16 lines 22-25]) wherein the instructions when processed by the at least one processor cause the control system to perform operations comprising: receiving a plurality of past water hardness values (chemical composition [Nelson p.28 line 1] which is analyzed by (Laser Induced Breakdown Spectroscopy (LIBS) [Nelson p. 21 line 8] by suggestion of Zhang and Lui comprise water hardness values, see rejection of claim 1 above) and a plurality of past agitator speed values corresponding to a past froth flotation process (Nelson pg. 12 line 43 lists that agitator speed as one of the monitored items which is sent to the processor and stored in memory [Nelson p. 10 line 26]); capturing a plurality of parameter values based on the plurality of past water hardness values and the plurality of past agitator speed values; identifying the one or more target parameter values among the plurality of parameter values such that the one or more target parameter values optimizes efficiency of the froth floatation process (Nelson p. 18 lines 1-7 describes the data collected as “heuristic knowledge”, which is knowledge gained by learning and doing, which is synonymous with observing the data collected in response to changes within the system and identifying target values to optimize the process); and determining the optimal speed value of the agitator corresponding to the one or more target parameter values (Nelson p. 12 table indicates that agitator speed is one of the many operational values which are monitored, recorded and acted upon), wherein the one or more target parameter values comprises an ash rejection percentage value, a combustible recovery percentage value, and an efficiency index value (Gui [p.66 section 3.2 par. 2] and Fig. 17).
Regarding claim 12, modified Nelson discloses the medium of claim 11, wherein the agitator speed value is measured (Nelson "rotational speed of agitation mechanism" p.12 line 43) by a shaft speed sensor communicatively coupled to the agitator. (Nelson "equipment parameters" and "equipment sensors" [p.14 line 1] and [Fig. 2 #44]) and the table on page 12 lists many equipment parameters sensed of which listed is the rotational speed of agitation mechanism later described as "impeller speed" (Nelson p. 22 line 22) which will possess a shaft, the speed of which will coordinate with the rotational speed of the agitation mechanism.)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over modified Nelson: (Nelson WO9745203, Koh and Smith "The effect of stirring speed and induction time on flotation", Zhang and Lui "Effect of Calcium Ions on Induction Time Between a Coal Particle and Air Bubble" and Gui "Flotation process design based on energy input and distribution") as applied to claim 1 above, and further in view of Yan et al (Hao Yan, Fuli Wang, Dakuo He, Luping Zhao, and Qingkai Wang, Industrial & Engineering Chemistry Research 2020 59 (5), 2025-2035).
Regarding claim 5, modified Nelson discloses the method of claim 1, wherein the pretrained model is trained using a neural network technique. (p. 10, lines 2-5)
Modified Nelson does not teach the additional use of a Bayesian optimization technique.
Yan et al discloses the method of using a Bayesian optimization technique ("Bayesian Network-based modeling", [p.2025 abstract]) to adjust operational parameters of an industrial plant flotation process. Yan et al discloses that use of this method in the decision making of operational adjustment can not only replace the person to complete the operational adjustment work but also has a better effect than the manual method. (p. 2034 par. 2)
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the Bayesian technique of Yan et al with the method of modified Nelson for the increased efficiency of adjusting the operational parameters based on the inputs of the monitored parameters to obtain a more efficient process and reduce costs of man hours and training required for a manually operated system that is less efficient.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over modified Nelson: (Nelson WO9745203, Koh and Smith "The effect of stirring speed and induction time on flotation", Zhang and Lui "Effect of Calcium Ions on Induction Time Between a Coal Particle and Air Bubble" and Gui "Flotation process design based on energy input and distribution") as applied to claim 1 above.
Regarding claim 6, Nelson discloses a control system for controlling a froth floatation process with data gathered solely from a hardness analyzer sensor and a shaft speed sensor (by suggestion of Zhang and Liu, Koh and Smith, for further explanation see rejection of claim 1 above), the control system comprising: a processor (Nelson pg. 19 line 2); and a memory communicatively coupled to the processor (Nelson pg. 10 line 26), wherein the processor is configured to: receive a water hardness value (via laser-induced breakdown spectroscopy sensors (LIBS) and /or laser induced mass spectroscopy sensors (LIMS sensors)[Nelson pg. 21 par. 2]) and an agitator speed value corresponding to water and an agitator involved in the froth floatation process (Nelson pg. 12 line 43); wherein the water hardness value is received from a hardness analyzer (LIBS and LIMS sensors[Nelson pg. 21 par. 2] are capable of analyzing chemical and mineralogical composition); analyze the agitator speed value vis-a-vis an optimal speed value required to achieve one or more target parameter values during the froth floatation process based on the water hardness value (the processor of Nelson is capable of receiving the chemical composition from the LIBS or LIMS sensors and adjusting the agitator speed required to meet one or more target parameter values. If an apparatus with elements similar to the elements in the instant application is capable of performing the intended use of the control system described by the instant claim, the intended use will be considered anticipated or made obvious. See MPEP 2114.); and implement a pretrained model, based on the analyzing, to adjust the agitator speed value to the optimal speed value in such a manner that the one or more target parameter values are achieved during the froth floatation process, (In response to one or more of the parameters sensed by the sensors [Nelson #42 and 44 shown in Fig. 2] the operation of the flotation machine and thereby its ultimate efficiency can be adjusted, changed, and preferably optimized [Nelson p.16 lines 22-25]) wherein the processor is configured to: receive a plurality of past water hardness values (via LIBS sensors of Nelson by suggestion of Zhang and Lui see above rejection of claim 1) and a plurality of past agitator speed values corresponding to a past froth flotation process ([Nelson p. 18 lines 1-7] describes a learning process applied to the models which are based on operational values which are collected by the sensors); capture a plurality of parameter values based on the plurality of past water hardness values and the plurality of past agitator speed values (Nelson p. 12 lists the plurality of values captured which includes these values); identify the one or more target parameter values among the plurality of parameter values such that the one or more target parameter values optimizes efficiency of the froth floatation process (Nelson p. 18 lines 1-7 describes the data collected as “heuristic knowledge”, which is knowledge gained by learning and doing, which is synonymous with observing the data collected in response to changes within the system and identifying target values to optimize the process); and determine the optimal speed value of the agitator corresponding to the one or more target parameter values (Nelson p. 12 table indicates that agitator speed is one of the many operational values which are monitored, recorded and acted upon), and wherein the one or more target parameter values comprises an ash rejection percentage value, a combustible recovery percentage value, and an efficiency index value (suggested by Gui ([p.66 section 3.2 par. 2] and Fig. 17).
Regarding claim 7, modified Nelson discloses the control system of claim 6, wherein the agitator speed value ("rotational speed of agitation mechanism" p.12 line 43) is measured by a shaft speed sensor communicatively coupled to the agitator. ("equipment parameters" and "equipment sensors" [p.14 line 1] and [Fig. 2 #44]) and the table on page 12 lists many equipment parameters sensed of which listed is the rotational speed of agitation mechanism later described as "impeller speed" (p. 22 line 22) the impeller of which will contain a shaft, the speed of which will coordinate with the rotational speed of the agitation mechanism.)
Claims 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over modified Nelson: (Nelson WO9745203, Koh and Smith "The effect of stirring speed and induction time on flotation", Zhang and Lui "Effect of Calcium Ions on Induction Time Between a Coal Particle and Air Bubble" and Gui "Flotation process design based on energy input and distribution") as applied to claims 6 and 11 above, and further in view of Yan et al (Hao Yan, Fuli Wang, Dakuo He, Luping Zhao, and Qingkai Wang, Industrial & Engineering Chemistry Research 2020 59 (5), 2025-2035).
Regarding claim 10, modified Nelson discloses the control system of claim 6, wherein the pretrained model is trained using a neural network technique (p. 10, lines 2-5).
Modified Nelson does not teach the additional use of a Bayesian optimization technique in the training.
Yan et al discloses the method of using a Bayesian optimization technique ("Bayesian Network-based modeling", [p.2025 abstract]) to adjust operational parameters of an industrial plant flotation process. Yan et al discloses that use of this method in the decision making of operational adjustment can not only replace the person to complete the operational adjustment work but also has a better effect than the manual method. (p. 2034 par. 2)
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the Bayesian technique of Yan et al with the control system of modified Nelson for the increased efficiency of adjusting the operational parameters based on the inputs of the monitored parameters to obtain a more efficient process and reduce costs of man hours and training required for a manually operated system that is less efficient.
Regarding claim 15, modified Nelson discloses the medium as claimed in claim 11, wherein the pretrained model is trained using a neural network technique and Bayesian optimization technique ("Bayesian Network-based modeling" Yan et al, [p.2025 abstract]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 WILLIAM ADDISON GEISBERT whose telephone number is (703)756-5497. The examiner can normally be reached Mon-Fri 7:30-5:00 EDT.
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, Bobby RAMDHANIE can be reached at (571)270-3240. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/W.A.G./Examiner, Art Unit 1779
/Bobby Ramdhanie/Supervisory Patent Examiner, Art Unit 1779