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 .
This is a response to U.S. Patent Application No. 18/401,365 filed on 12/30/2023 in which Claims 1 – 20 were filed for examination.
Status of the Claims
Claims 1 – 8, 10 – 15 and 17 – 20 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) and Claims 9 and 16 are rejected under 35 U.S.C. 103.
Title of the Invention
37 C.F.R. 1.72(a) states: "The title of the invention may not exceed 500 characters in length and must be as short and specific as possible" (emphasis added). Thus, the title of the invention is not sufficiently descriptive. A new title is required that is more clearly and more specifically indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 8, 10 – 15 and 17 – 20 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Gray (US 2023/0257283) (hereinafter, Gray).
Regarding Claim 1, Gray teaches a method (See Gray’s Abstract), comprising:
receiving, by a control system at a water treatment plant, from a control model, a recommended dose of coagulant for source water into the water treatment plant, the recommended dose of coagulant determined by the control model based on one or more water quality metrics measured for the source water (Gray in par 0002, teaches that coagulation is a water treatment technique typically applied prior filtration to enhance the ability of a treatment process to remove particles from the water. Gray in par 0014, teaches that once the water quality index is predicted by using the mathematical model, it is determined whether the predicted water quality index is within a target range. If the predicted water quality index is not within the target range, then the nominal coagulant dosage is adjusted and a sequent water quality index is predicted based on the mathematical model using the adjusted coagulant dosage. This process can be repeated until the predicted water quality index is within the target range. Then, the corresponding coagulant dosage (i.e., either the nominal or an adjusted coagulant dosage, whichever is predicted to achieve the target water quality index) can be administered to the water. Gray in par 0015, further teaches employing machine learning techniques and feedforward controls to continuously or periodically calculate the optimal dosage for meeting the treatment goal);
receiving, by the control system during a manual override, a manual input for the dose of coagulant (Gray in par 0022 and Fig. 1, further teaches that after predicting a water quality index that would be achieved if the nominal coagulant dosage is administered to the water by evaluating the coagulation-related parameter(s) measured in the untreated water and the nominal coagulant dosage via Regression Model 2, the method includes determining whether the predicted water quality index is within a target range. As shown in FIG. 1, if the predicted water quality index is within the target range, then the nominal coagulant dosage may be output by the process controller. For example, the process controller can generate control signals to automatically administer the nominal coagulant dosage to the water (e.g., by sending signals to control a pump that pumps the coagulant from a container into the water) or may output instructions to a display or user interface (UI) to instruct an operator of the system to administer the nominal coagulant dosage, or may adjust the nominal coagulant dosage based on feedback controls); and
providing, by the control system to the control model as feedback, data corresponding to the manual input during the manual override (Gray in par 0024 and Fig. 1, teaches that the adjusted coagulant dosage (which is increased or decreased relative to the nominal coagulant dosage first evaluated) is evaluated along with the one or more coagulation-related parameters of the untreated water to predict a new water quality index that would be achieved if the adjusted coagulant dosage is administered to the untreated water. Gray in par 0045 and Fig(s). 3 – 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data (including data for both feedforward and feedback parameters). The process controller can be configured to evaluate the data input from the on-line sensors and/or laboratory to determine the optimal coagulant dosage. The process controller can be further configured to control the coagulant pump to administer the coagulant dosage determined by the feedforward and/or feedback control methodologies. Alternatively, the water system may have another dedicated controller for controlling the coagulant pump, including dosage amount and schedule. The process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water).
Regarding Claim 2, Gray teaches the limitations contained in parent Claim 1. Gray further teaches:
wherein the control model is hosted in a cloud environment (Gray in par 0048 – 0049, teaches that the process controller may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use may include distributed cloud computing environments. The various components of the water treatment system may be connected with each other via any type of digital data communication such as a communication network. The method may be practiced in clouding computing environments, including public, private, and hybrid clouds. The method can also or alternatively be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network), and wherein the control system is locally deployed at the water treatment plant (Gray in par 0022, further teaches that the process controller can generate control signals to automatically administer the nominal coagulant dosage to the water (e.g., by sending signals to control a pump that pumps the coagulant from a container into the water) or may output instructions to a display or user interface (UI) to instruct an operator of the system to administer the nominal coagulant dosage, or may adjust the nominal coagulant dosage based on feedback controls. Gray in par 0043, further teaches that the system includes a coagulant pump for pumping the coagulant into the raw water. The coagulant pump may be any suitable pump or injector for administering the coagulant to the raw water at the determined dosage. The coagulant is pumped into the water upstream of any flash mix tank, flocculation (floc) tank, and solids separation tank in FIG. 4, but may be administered to the water at any suitable position).
Regarding Claim 3, Gray teaches the limitations contained in parent Claim 1. Gray further teaches:
wherein the control model comprises i) a model predictive control system comprising a first dynamic model and an optimizer, and ii) a second dynamic model (Gray in par 0026, teaches that the coagulant dosage may be calculated by evaluating the real time data, including the measured coagulation-related parameter(s) of the raw, untreated water using Regression Model 1. Regression Model 1 is a mathematical model constructed from historical data of the water including (i) previously measured values of the coagulation-related incoming water parameters of the water that has not been treated with the coagulant; and (ii) previously administered coagulant dosages. Upon receiving the measured value(s) of the one or more coagulation-related parameters of the raw water, Regression Model 1 can calculate the coagulant dosage that the controller would have administered in the past based on the measured value(s) of the coagulation-related parameters(s). The calculated coagulant dosage may then be input into Regression Model 2 as the nominal coagulant dosage for determining the optimal coagulant dosage predicted to achieve the target water quality index).
Regarding Claim 4, Gray teaches the limitations contained in parent Claim 3. Gray further teaches:
wherein the model predictive control system is trained to generate the recommended dose of coagulant based on the one or more water quality metrics and a predicted settled turbidity from the second dynamic model (Gray in par 0023, teaches that if the predicted value of the pre-filter turbidity of the treated water is higher than the target pre-filter turbidity, then the nominal coagulant dosage can be expected to provide insufficient coagulation and flocculation. In this case, the processor increases the nominal coagulant dosage, and then evaluates the increased nominal coagulant dosage, along with the coagulation-related parameter(s) of the untreated water via Regression Model 2 to predict the water quality index that would be achieved if the increased coagulant dosage is administered to the untreated water. Gray in par 0029, teaches that the machine learning models can be single- or multi-variable regression models that are trained to provide a robust and optimal dosage calculation by taking into account one or several explanatory variables, including the coagulation-related incoming water parameter(s) and any other raw water parameters, and the treatment goal or objective variable (e.g., water quality index within the target range). Gray in par 0034, further teaches that once trained and validated, the regression model files are generated and stored, for example, in memory, and the trained models can then be used (e.g., by the controller) to evaluate real time data to calculate the coagulant dosage and predict the water quality index).
Regarding Claim 5, Gray teaches the limitations contained in parent Claim 4. Gray further teaches:
wherein the second dynamic model is trained to determine the predicted settled turbidity based on the one or more water quality metrics, the recommended dose of coagulant from the model predictive control system, and the data corresponding to the manual input, and wherein the data corresponding to the manual input is provided as an input disturbance to the recommended dose of coagulant (Gray in par 0014, teaches that the method includes predicting a water quality index that would be achieved if a nominal coagulant dosage is administered to the water. The water quality index can be any measure of the treated water quality, such as turbidity. The water quality index can be predicted by evaluating the nominal coagulant dosage and at least one measured coagulation-related incoming water parameter of the water with a mathematical model. Gray in par 0045 and Fig. 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data. The process controller can be configured to evaluate the data input from the on-line sensors and/or laboratory to determine the optimal coagulant dosage. The process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water. The controller may further be configured to receive instructions from a user (e.g., via the user interface). For example, a user may input the nominal coagulant dosage to the controller for analysis via Regression Model 1 and/or 2, or may instruct the controller to otherwise adjust the coagulant dosage).
Regarding Claim 6, Gray teaches the limitations contained in parent Claim 3. Gray further teaches:
wherein the first dynamic model comprises a first instance of a dynamic model, and the second dynamic model comprises a second instance of the dynamic model (Gray in par 0026, teaches that the coagulant dosage may be calculated by evaluating the real time data, including the measured coagulation-related parameter(s) of the raw, untreated water using Regression Model 1. Regression Model 1 is a mathematical model constructed from historical data of the water including (i) previously measured values of the coagulation-related incoming water parameters of the water that has not been treated with the coagulant; and (ii) previously administered coagulant dosages. Upon receiving the measured value(s) of the one or more coagulation-related parameters of the raw water, Regression Model 1 can calculate the coagulant dosage that the controller would have administered in the past based on the measured value(s) of the coagulation-related parameters(s). The calculated coagulant dosage may then be input into Regression Model 2 as the nominal coagulant dosage for determining the optimal coagulant dosage predicted to achieve the target water quality index).
Regarding Claim 7, Gray teaches the limitations contained in parent Claim 1. Gray further teaches:
wherein the data corresponding to the manual input is provided to the control system as feedback, to maintain synchronization of the control model with the control system during the manual override (Gray in par 0045 and Fig. 4, further teaches that the process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water. The controller may further be configured to receive instructions from a user (e.g., via the user interface). For example, a user may input the nominal coagulant dosage to the controller for analysis via Regression Model 1 and/or 2, or may instruct the controller to otherwise adjust the coagulant dosage).
Regarding Claim 8, Gray teaches the limitations contained in parent Claim 1. Gray further teaches:
further comprising switching, by the control system, the control model to an offline mode responsive to receiving the manual input according to the manual override (Gray in par 0022, teaches that the process controller can generate control signals to automatically administer the nominal coagulant dosage to the water (e.g., by sending signals to control a pump that pumps the coagulant from a container into the water) or may output instructions to a display or user interface (UI) to instruct an operator of the system to administer the nominal coagulant dosage, or may adjust the nominal coagulant dosage based on feedback controls. Gray in par 0049, further teaches that the system may be also be configured to work offline).
Regarding Claim 10, Gray teaches the limitations contained in parent Claim 1. Gray further teaches:
wherein the one or more water quality metrics comprise a turbidity of the source water (Gray in par 0019, teaches that the water quality index is an objective variable that is indicative of the effectiveness of the coagulant. The water quality index may be a measurable parameter of the water that has been treated with the coagulant that is indicative of the amount of coagulation. For instance, the water quality index may be one or more of the pre-filter turbidity, total organic content, ultraviolet absorbance at a wavelength of 254 nm, ultraviolet transmittance at a wavelength of 254 nm, or any other parameter that is indicative of the effectiveness of the coagulant).
Regarding Claim 11, Gray teaches a method (See Gray’s Abstract) comprising:
receiving, by a computing system configured to execute a control model, from a control system of a water treatment plant, input data comprising one or more metrics indicative of a water quality of source water and a settled turbidity setpoint of output water (Gray in par 0002, teaches that coagulation is a water treatment technique typically applied prior filtration to enhance the ability of a treatment process to remove particles from the water. Gray in par 0014, teaches that the method includes predicting a water quality index that would be achieved if a nominal coagulant dosage is administered to the water. The water quality index can be any measure of the treated water quality, such as turbidity. The water quality index can be predicted by evaluating the nominal coagulant dosage and at least one measured coagulation-related incoming water parameter of the water with a mathematical model. Then, once the water quality index is predicted by using the mathematical model, it is determined whether the predicted water quality index is within a target range. If the predicted water quality index is not within the target range, then the nominal coagulant dosage is adjusted and a sequent water quality index is predicted based on the mathematical model using the adjusted coagulant dosage. Gray in par 0015, further teaches employing machine learning techniques and feedforward controls to continuously or periodically calculate the optimal dosage for meeting the treatment goal);
receiving, by the computing system, from the control system of the water treatment plan as feedback, a manual input corresponding to a dose of coagulant received during a manual override at a first time instance (Gray in par 0022 and Fig. 1, further teaches that after predicting a water quality index that would be achieved if the nominal coagulant dosage is administered to the water by evaluating the coagulation-related parameter(s) measured in the untreated water and the nominal coagulant dosage via Regression Model 2, the method includes determining whether the predicted water quality index is within a target range. As shown in FIG. 1, if the predicted water quality index is within the target range, then the nominal coagulant dosage may be output by the process controller. For example, the process controller can generate control signals to automatically administer the nominal coagulant dosage to the water (e.g., by sending signals to control a pump that pumps the coagulant from a container into the water) or may output instructions to a display or user interface (UI) to instruct an operator of the system to administer the nominal coagulant dosage, or may adjust the nominal coagulant dosage based on feedback controls. Gray in par 0024 and Fig. 1, further teaches that the adjusted coagulant dosage (which is increased or decreased relative to the nominal coagulant dosage first evaluated) is evaluated along with the one or more coagulation-related parameters of the untreated water to predict a new water quality index that would be achieved if the adjusted coagulant dosage is administered to the untreated water. Gray in par 0045 and Fig(s). 3 – 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data (including data for both feedforward and feedback parameters). The process controller can be configured to evaluate the data input from the on-line sensors and/or laboratory to determine the optimal coagulant dosage);
determining, by the control model of the computing system, a recommended dose of coagulant based on the input data and the manual input for a second time instance (Gray in par 0023, teaches that if the predicted value of the pre-filter turbidity of the treated water is higher than the target pre-filter turbidity, then the nominal coagulant dosage can be expected to provide insufficient coagulation and flocculation. In this case, the processor increases the nominal coagulant dosage, and then evaluates the increased nominal coagulant dosage, along with the coagulation-related parameter(s) of the untreated water via Regression Model 2 to predict the water quality index that would be achieved if the increased coagulant dosage is administered to the untreated water. Gray in par 0029, teaches that the machine learning models can be single- or multi-variable regression models that are trained to provide a robust and optimal dosage calculation by taking into account one or several explanatory variables, including the coagulation-related incoming water parameter(s) and any other raw water parameters, and the treatment goal or objective variable (e.g., water quality index within the target range). Gray in par 0034, further teaches that once trained and validated, the regression model files are generated and stored, for example, in memory, and the trained models can then be used (e.g., by the controller) to evaluate real time data to calculate the coagulant dosage and predict the water quality index); and
transmitting, by the computing system, data corresponding to the recommended dose of coagulant to the control system of the water treatment plant (Gray in par 0045 and Fig(s). 3 – 4, further teaches that the process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water).
Regarding Claim 12, Gray teaches the limitations contained in parent Claim 11. Gray further teaches:
wherein the control model comprises i) a model predictive control system comprising a first dynamic model and an optimizer, and ii) a second dynamic model (Gray in par 0026, teaches that the coagulant dosage may be calculated by evaluating the real time data, including the measured coagulation-related parameter(s) of the raw, untreated water using Regression Model 1. Regression Model 1 is a mathematical model constructed from historical data of the water including (i) previously measured values of the coagulation-related incoming water parameters of the water that has not been treated with the coagulant; and (ii) previously administered coagulant dosages. Upon receiving the measured value(s) of the one or more coagulation-related parameters of the raw water, Regression Model 1 can calculate the coagulant dosage that the controller would have administered in the past based on the measured value(s) of the coagulation-related parameters(s). The calculated coagulant dosage may then be input into Regression Model 2 as the nominal coagulant dosage for determining the optimal coagulant dosage predicted to achieve the target water quality index).
Regarding Claim 13, Gray teaches the limitations contained in parent Claim 12. Gray further teaches:
wherein the model predictive control system is trained to generate the recommended dose of coagulant based on the one or more water quality metrics and a predicted settled turbidity from the second dynamic model (Gray in par 0023, teaches that if the predicted value of the pre-filter turbidity of the treated water is higher than the target pre-filter turbidity, then the nominal coagulant dosage can be expected to provide insufficient coagulation and flocculation. In this case, the processor increases the nominal coagulant dosage, and then evaluates the increased nominal coagulant dosage, along with the coagulation-related parameter(s) of the untreated water via Regression Model 2 to predict the water quality index that would be achieved if the increased coagulant dosage is administered to the untreated water. Gray in par 0029, teaches that the machine learning models can be single- or multi-variable regression models that are trained to provide a robust and optimal dosage calculation by taking into account one or several explanatory variables, including the coagulation-related incoming water parameter(s) and any other raw water parameters, and the treatment goal or objective variable (e.g., water quality index within the target range). Gray in par 0034, further teaches that once trained and validated, the regression model files are generated and stored, for example, in memory, and the trained models can then be used (e.g., by the controller) to evaluate real time data to calculate the coagulant dosage and predict the water quality index).
Regarding Claim 14, Gray teaches the limitations contained in parent Claim 13. Gray further teaches:
wherein the second dynamic model is trained to determine the predicted settled turbidity based on the one or more water quality metrics, the recommended dose of coagulant from the model predictive control system, and the manual input, and wherein the manual input is provided as an input disturbance to the recommended dose of coagulant (Gray in par 0014, teaches that the method includes predicting a water quality index that would be achieved if a nominal coagulant dosage is administered to the water. The water quality index can be any measure of the treated water quality, such as turbidity. The water quality index can be predicted by evaluating the nominal coagulant dosage and at least one measured coagulation-related incoming water parameter of the water with a mathematical model. Gray in par 0045 and Fig. 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data. The process controller can be configured to evaluate the data input from the on-line sensors and/or laboratory to determine the optimal coagulant dosage. The process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water. The controller may further be configured to receive instructions from a user (e.g., via the user interface). For example, a user may input the nominal coagulant dosage to the controller for analysis via Regression Model 1 and/or 2, or may instruct the controller to otherwise adjust the coagulant dosage).
Regarding Claim 15, Gray teaches the limitations contained in parent Claim 11. Gray further teaches:
further comprising:
determining, by the computing system, during the manual override at the first time instance, a recommended dose of coagulant (Gray in par 0002, teaches that coagulation is a water treatment technique typically applied prior filtration to enhance the ability of a treatment process to remove particles from the water. Gray in par 0014, teaches that once the water quality index is predicted by using the mathematical model, it is determined whether the predicted water quality index is within a target range. If the predicted water quality index is not within the target range, then the nominal coagulant dosage is adjusted and a sequent water quality index is predicted based on the mathematical model using the adjusted coagulant dosage. This process can be repeated until the predicted water quality index is within the target range. Then, the corresponding coagulant dosage (i.e., either the nominal or an adjusted coagulant dosage, whichever is predicted to achieve the target water quality index) can be administered to the water. Gray in par 0015, further teaches employing machine learning techniques and feedforward controls to continuously or periodically calculate the optimal dosage for meeting the treatment goal); and
foregoing, by the computing system, transmitting data corresponding to the recommended dose of coagulant to the control system during the manual override Gray in par 0024 and Fig. 1, teaches that the adjusted coagulant dosage (which is increased or decreased relative to the nominal coagulant dosage first evaluated) is evaluated along with the one or more coagulation-related parameters of the untreated water to predict a new water quality index that would be achieved if the adjusted coagulant dosage is administered to the untreated water. Gray in par 0045 and Fig(s). 3 – 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data (including data for both feedforward and feedback parameters). The process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water).
Regarding Claim 17, this claim merely recites a computing system comprising: a communication system communicably coupled to a control system of a water treatment plant (See Gray’s par 0049, communication network); and one or more processors () configured to perform the steps as similarly recited in Claim 11. Accordingly, Gray discloses/teaches every limitation of Claim 17, as indicated in the above rejection of Claim 11.
Regarding Claim 18, Gray teaches the limitations contained in parent Claim 17. Gray further teaches:
further comprising the control system of the water treatment plant (Gray in par 0048 - 0049, teaches that the process controller may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use may include distributed cloud computing environments. The various components of the water treatment system may be connected with each other via any type of digital data communication such as a communication network. The method may be practiced in clouding computing environments, including public, private, and hybrid clouds. The method can also or alternatively be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network).
Regarding Claim 19, Gray teaches the limitations contained in parent Claim 17. Gray further teaches:
wherein the one or more processors are configured to execute a control model to determine the recommended dose of coagulant, wherein the control model comprises: i) a model predictive control system comprising a first dynamic model and an optimizer; and ii) a second dynamic model (Gray in par 0026, teaches that the coagulant dosage may be calculated by evaluating the real time data, including the measured coagulation-related parameter(s) of the raw, untreated water using Regression Model 1. Regression Model 1 is a mathematical model constructed from historical data of the water including (i) previously measured values of the coagulation-related incoming water parameters of the water that has not been treated with the coagulant; and (ii) previously administered coagulant dosages. Upon receiving the measured value(s) of the one or more coagulation-related parameters of the raw water, Regression Model 1 can calculate the coagulant dosage that the controller would have administered in the past based on the measured value(s) of the coagulation-related parameters(s). The calculated coagulant dosage may then be input into Regression Model 2 as the nominal coagulant dosage for determining the optimal coagulant dosage predicted to achieve the target water quality index).
Regarding Claim 20, Gray teaches the limitations contained in parent Claim 19. Gray further teaches:
wherein:
the model predictive control system is trained to generate the recommended dose of coagulant based on the one or more water quality metrics and a predicted settled turbidity from the second dynamic model (Gray in par 0023, teaches that if the predicted value of the pre-filter turbidity of the treated water is higher than the target pre-filter turbidity, then the nominal coagulant dosage can be expected to provide insufficient coagulation and flocculation. In this case, the processor increases the nominal coagulant dosage, and then evaluates the increased nominal coagulant dosage, along with the coagulation-related parameter(s) of the untreated water via Regression Model 2 to predict the water quality index that would be achieved if the increased coagulant dosage is administered to the untreated water. Gray in par 0029, teaches that the machine learning models can be single- or multi-variable regression models that are trained to provide a robust and optimal dosage calculation by taking into account one or several explanatory variables, including the coagulation-related incoming water parameter(s) and any other raw water parameters, and the treatment goal or objective variable (e.g., water quality index within the target range). Gray in par 0034, further teaches that once trained and validated, the regression model files are generated and stored, for example, in memory, and the trained models can then be used (e.g., by the controller) to evaluate real time data to calculate the coagulant dosage and predict the water quality index);
the second dynamic model is trained to determine the predicted settled turbidity based on the one or more water quality metrics, the recommended dose of coagulant from the model predictive control system, and the manual input, and wherein the manual input is provided as an input disturbance to the recommended dose of coagulant (Gray in par 0014, teaches that the method includes predicting a water quality index that would be achieved if a nominal coagulant dosage is administered to the water. The water quality index can be any measure of the treated water quality, such as turbidity. The water quality index can be predicted by evaluating the nominal coagulant dosage and at least one measured coagulation-related incoming water parameter of the water with a mathematical model. Gray in par 0045 and Fig. 4, further teaches that the system further includes a process optimization controller that is configured to receive data (e.g., real time data) from the one or more on-line sensors and/or laboratory data. The process controller can be configured to evaluate the data input from the on-line sensors and/or laboratory to determine the optimal coagulant dosage. The process controller can output the determined optimal coagulant dosage, e.g., as a control signal, to the dedicated controller for controlling the coagulant pump. The process controller may alternatively or additionally be configured to output the coagulant dosage determined to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water. The controller may further be configured to receive instructions from a user (e.g., via the user interface). For example, a user may input the nominal coagulant dosage to the controller for analysis via Regression Model 1 and/or 2, or may instruct the controller to otherwise adjust the coagulant dosage).
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.
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gray in view of Chandler, Jr. et al. (US 11,209,839) (hereinafter, Chandler).
Regarding Claim 9, Gray teaches the limitations contained in parent Claim 8.
Gray further teaches:
wherein the manual input is received at a first time instance (Gray in par 0045, teaches that the process controller may alternatively or additionally be configured to output the coagulant dosage determined by any of the methodologies disclosed herein to an operator of the system, for example, via a user interface such that the user can manually operate the coagulant pump to administer the determined coagulant dosage to the water. The controller may further be configured to receive instructions from a user (e.g., via the user interface). For example, a user may input the nominal coagulant dosage to the controller for analysis via Regression Model 1 and/or 2, or may instruct the controller to otherwise adjust the coagulant dosage), the method further comprising:
receiving, by the control system, from the control model at a third time instance subsequent to the second time instance, a second recommended dose of coagulant determined by the control model, the control model determining the second recommended dose of coagulant according to the data corresponding to the manual input (Gray in par 0025, further teaches that the newly predicted water quality index based on the adjusted coagulant dosage is then evaluated to determine whether it is within the target range. If the newly predicted water quality index is within the target range, then the adjusted coagulant dosage is output by the process controller, e.g., as a signal to control process equipment, or to a display or user interface that is operated by a user. If the newly predicted water quality index is not within the target range, then the adjusted coagulant dosage is adjusted again and the process is repeated to predict a new water quality index. The method may include incrementally adjusting the nominal coagulant dosage, and repeating the predicting and determining steps until the predicted water quality index is determined to be within the target range).
However, Gray does not specifically disclose the use of a switch when the user is manually operating the system, accordingly, Gray does not specifically disclose switching, by the control system at a second time instance subsequent to the first time instance, the control model to an online mode responsive to receiving an input to switch to automated control.
Chandler teaches a pump controller to control the flow of water (See Chandler’s Abstract). Chandler in Col. 5 line 65 – Col. 6 line 2, teaches that the source of untreated liquid may be a well, reservoir or other source of water that requires the treatment provided by passing the liquid through the water treatment tank. Chandler in Col. 29 line 65 – Col. 30 line 18, further teaches that a water softener may be adapted to change from a current service mode of operation to another mode of operation (via operation of the valve) in which all liquid output from the water softener is either shut off (prevented) or turned back on (permitted). The application on the user interface device may include a selectable option to shut off water to a user's house.
Chandler in Col. 31 lines 32 – 41, further teaches that once the user interface device has operatively connected with the master controller, the user is enabled to use the interface device to interact with the master controller. Through inputs through the touch screen or other user interface on the tablet, the user can change settings, view data and send commands to the master controller. Such commands may include shutting off certain devices, placing devices in a bypass condition or otherwise controlling slave assemblies and the associated devices that are connected in the wireless system with the master controller.
Chandler in Col. 63 lines 52 – 63, further teaches that at least one control circuit is operative to make a short cycle determination that a plurality of pump runtime cycles have had an elapsed duration that is at or below the short cycle time. Responsive to this determination, the pump control circuit is operative to set a shutdown status and report the condition as represented by step 1528. In a step 1530 the logic waits for receipt of a reset instruction. When a reset instruction is received as represented by a step 1532 the control circuit is operative to clear the status as represented by step 1534 and the logic returns to normal operation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Chandler with the teachings as in Gray to control the pump of Gray as disclosed in Chandler. The motivation for doing so would have been to effectively provide an interface to control the status of a pump and provide the ability of return the system to normal operation after interrupting the process (See Chandler’s Col. 1 lines 57 – 67).
Regarding Claim 16, Gray teaches the limitations contained in parent Claim 11.
Gray further teaches:
wherein transmitting the data corresponding to the recommended dose of coagulant to the control system of the water treatment plant is performed responsive to detecting the switch (Gray in par 0022, teaches that the process controller can generate control signals to automatically administer the nominal coagulant dosage to the water (e.g., by sending signals to control a pump that pumps the coagulant from a container into the water) or may output instructions to a display or user interface (UI) to instruct an operator of the system to administer the nominal coagulant dosage, or may adjust the nominal coagulant dosage based on feedback controls. Gray in par 0025, further teaches that the newly predicted water quality index based on the adjusted coagulant dosage is then evaluated to determine whether it is within the target range. If the newly predicted water quality index is within the target range, then the adjusted coagulant dosage is output by the process controller, e.g., as a signal to control process equipment, or to a display or user interface that is operated by a user. If the newly predicted water quality index is not within the target range, then the adjusted coagulant dosage is adjusted again and the process is repeated to predict a new water quality index. The method may include incrementally adjusting the nominal coagulant dosage, and repeating the predicting and determining steps until the predicted water quality index is determined to be within the target range).
However, Gray does not specifically disclose the use of a switch when the user is manually operating the system, accordingly Gray does not specifically disclose further comprising detecting, by the computing system, a switch from an offline mode to an online mode, responsive to termination of the manual override.
Chandler teaches a pump controller to control the flow of water (See Chandler’s Abstract). Chandler in Col. 5 line 65 – Col. 6 line 2, teaches that the source of untreated liquid may be a well, reservoir or other source of water that requires the treatment provided by passing the liquid through the water treatment tank. Chandler in Col. 29 line 65 – Col. 30 line 18, further teaches that a water softener may be adapted to change from a current service mode of operation to another mode of operation (via operation of the valve) in which all liquid output from the water softener is either shut off (prevented) or turned back on (permitted). The application on the user interface device may include a selectable option to shut off water to a user's house.
Chandler in Col. 31 lines 32 – 41, further teaches that once the user interface device has operatively connected with the master controller, the user is enabled to use the interface device to interact with the master controller. Through inputs through the touch screen or other user interface on the tablet, the user can change settings, view data and send commands to the master controller. Such commands may include shutting off certain devices, placing devices in a bypass condition or otherwise controlling slave assemblies and the associated devices that are connected in the wireless system with the master controller.
Chandler in Col. 63 lines 52 – 63, further teaches that at least one control circuit is operative to make a short cycle determination that a plurality of pump runtime cycles have had an elapsed duration that is at or below the short cycle time. Responsive to this determination, the pump control circuit is operative to set a shutdown status and report the condition as represented by step 1528. In a step 1530 the logic waits for receipt of a reset instruction. When a reset instruction is received as represented by a step 1532 the control circuit is operative to clear the status as represented by step 1534 and the logic returns to normal operation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Chandler with the teachings as in Gray to control the pump of Gray as disclosed in Chandler. The motivation for doing so would have been to effectively provide an interface to control the status of a pump and provide the ability of return the system to normal operation after interrupting the process (See Chandler’s Col. 1 lines 57 – 67).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/ARIEL MERCADO-VARGAS/Primary Examiner, Art Unit 2118
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