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 action is in response to the applicant’s communication filed on 7/19/2024
Claims 1-15 are pending
Claim Objections
Claim 4 objected to because of the following informalities: the colon in “second-sub predicted temperature information:” should be changed to a semicolon as shown “second-sub predicted temperature information;”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-12, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation "the user interface" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the user interface" in line 3. There is insufficient antecedent basis for this limitation in the claim.
The dependent claims are also rejected under 35 U.S.C. § 112 as they inherit all of the characteristics of the claim from which they depend and none of the dependent claims provide a cure for the indefiniteness of the parent claims.
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.
Claim(s) 1, 2, 6, 8, 9, and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. CN 116382371 A (hereinafter Chen) in view of Keeler US 5,353,207 A (hereinafter Keeler), and further in view of Domoto et al. (Expectations for Changing Steam Power Plants and Supporting Technologies, 2019) (hereinafter Domoto).
Regarding claim 1, Chen teaches an electronic device for implementing a temperature prediction and control system (Par. [0003], “This invention relates to the field of decomposition furnace temperature modeling and control, and in particular to a method for controlling the temperature of a decomposition furnace in a cement kiln co-processing waste.”), the electronic device comprising:
a communication interface (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing”; Par. [n0061], “Due to the harsh industrial environment and numerous sources of interference, errors inevitably occur during data collection and transmission. Therefore, the data from Step 1 needs to be filtered to reduce the impact of interference.”; Par. [n0087], “After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s electronic device receives transmitted field data from the decomposition furnace and transmits calculated control information to the industrial field control system. Thus, Chen’s electronic device must include an input/output communication interface through which the field data are received and the calculated control information is transmitted.);
a memory (Par. [n0051], “The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method”); and
one or more processors configured to (Par. [n0112], “processor is configured to execute the program stored in the memory”):
perform preprocessing on (Par. [n0010], “Use the moving average filtering method to preprocess the field data”), when process information including fuel input information of a cement manufacturing apparatus is received through the communication interface (Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing”), the received process information (Par. [n0009], “… represents the original value of the coal feed rate of the decomposer at time k, and … represents the original value of the waste flow rate of the decomposer at time k” – coal feed amount and garbage flow amount correspond to the fuel input information);
measured temperature of a first preheating chamber in the cement manufacturing apparatus (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing … where T(k) represents the original value of the decomposition furnace outlet temperature at time k” – decomposition furnace is interpreted as a first preheating chamber of the cement manufacturing apparatus.);
a first recycled fuel among input fuels (Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace not only saves fuel and some raw materials for the cement industry but also processes large amounts of municipal solid waste”); and
provide guidance information including predicted temperature information (Par. [n0087], “The predicted sequence of the outlet temperature is obtained through the prediction model … Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – the predicted temperature sequence is used as guidance information for determining the coal feed rate and waste flow rate. The prediction being the second predicted temperature results from the modification discussed below).
Chen does not explicitly teach a trained first neural network model and a trained second neural network model;
input the preprocessed process information into the trained second neural network model to obtain error information on first predicted temperature information output from the trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus;
identify predicted calorific information of a first recycled fuel among input fuels based on the obtained error information and the fuel input information; and
input the fuel input information updated based on the identified predicted calorific information into the trained first neural network model to obtain second predicted temperature information of the first preheating chamber;
However, Keeler teaches a trained first neural network model and a trained second neural network model (Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 8, “The residual of the network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2".” – Keeler’s NET 1 and NET 2 correspond to the trained first and second neural-network models, which would be stored in Chen’s memory for execution by Chen’s processor.);
input the preprocessed process information into the trained second neural network model to obtain error information on first predicted temperature information output from the trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus (Col. 8, lines 4-7, “the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 8, lines 22-25, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t)”; Col. 8, lines 33-41, “The residual of the kth network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2". In the training procedure, the value of r1(t) is utilized as a target value with the input exercised with x(t). Once trained, the weights in the hidden layer 40 and associated interconnect layers 46 and 50 are frozen and then the network exercised with x(t) to provide a predicted output o2(t).” – Keeler first trains NET 1 using the actual plant output y(t) and exercises NET 1 with process input x(t) to produce the first predicted output o1(t). Keeler determines the residual r1(t) as the difference between the actual output y(t) and NET 1’s first predicted output o1(t), and then trains NET 2 using r1(t) as the target and x(t) as the input. When Keeler’s residual neural-network architecture is incorporated into Chen, Chen’s processor inputs Chen’s preprocessed decomposition-furnace field data as x(t) into trained NET 2 and obtains NET 2’s output o2(t). Because NET 2 was trained using the difference between NET 1’s predicted output and the actual measured output as its target, o2(t) constitutes error information on the first predicted decomposition-furnace temperature and the measured decomposition-furnace temperature.); and
input updated input information through a neural-network predictive model to obtain second predicted temperature information of the first preheating chamber (Col.8, lines 6-7, “The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 9, lines 58-63, “a plant predictive model 74 is developed with a neural network to accurately model the plant in accordance with the function f(c(t),s(t)) to provide an output oP(t), which represents the predicted output of plant predictive model 74.”; Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error.” – Keeler teaches a trained first neural network that receives input information and produces a predicted plant output and further teaches updating input information and processing the updated input information through a neural-network predictive model to obtain a new predicted plant output. In the modified Chen system, Chen’s processor would input updated coal-feed and waste-flow information into the trained first neural-network temperature-prediction model and obtain a new predicted decomposition-furnace temperature, which corresponds to the second predicted temperature information of the first preheating chamber.).
Chen and Keeler are analogous art because they contain functional similarities. They both relate to the prediction and control of nonlinear industrial processes using process-input information and measured process-output information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, and incorporate a residual neural network architecture, including a first neural network for predicting a plant output and a second neural network for predicting residual information associated with the predicted and measured plant outputs, as taught by Keeler.
One of ordinary skill in the art would have been motivated to account for unmeasured external influences affecting the predicted decomposition furnace temperature, as suggested by Keeler (Col. 13, lines 60-63).
Chen and Keeler do not explicitly teach identify predicted calorific information of a first recycled fuel among input fuels based on the obtained error information and the fuel input information; and
Updating fuel input information based on the identified predicted calorific information;
However, Domoto teaches identify predicted calorific information based on the obtained error information and the fuel input information (Page 4, Improvement of high-speed load response technology, “T2 control uses the water-fuel ratio (fuel flow rate) to control the steam temperature at the outlet of the secondary superheater”; Page 7, Correction of calorific value fluctuations, “the fluctuation of the calorific value within the same coal brand was corrected according to the water-fuel ratio correction amount based on the idea that the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time. A function to perform this automatically has been provided … “steam temperature deviation => water-fuel ratio => calorific value correction”” – Domoto identifies the water-fuel ratio as the fuel flow rate and teaches identifying the true calorific-value setting from the correction produced in response to the steam-temperature deviation. In the modified system, Keeler’s obtained residual temperature error corresponds to Domoto’s steam-temperature deviation, Chen’s waste-flow-rate information corresponds to Domoto’s fuel-flow information, and Domoto’s identified true calorific-value setting corresponds to the predicted calorific information.); and
Updating fuel input information based on the identified predicted calorific information (Page 7, Correction of calorific value fluctuations, “To make the correction of calorific value fluctuations faster, the process was revised to a method that always calculates the calorific values based on the relationship in the boiler efficiency calculation formula and applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto calculates the fuel calorific value and corrects the calorific-value information used by the control logic when the calculated value differs from the existing value. Thus, correcting the calorific-value information associated with the fuel corresponds to updating the fuel input information based on the identified predicted calorific information.).
Thus, in the modified system, Chen’s processor would use Keeler’s obtained residual temperature error as Domoto’s steam-temperature deviation and Chen’s waste-flow-rate information as Domoto’s fuel-flow information to identify a corrected calorific value for Chen’s recycled waste fuel. Chen’s processor would update the calorific-value information associated with the recycled waste fuel and input the resulting updated fuel input information into Keeler’s trained first neural-network temperature-prediction model to obtain second predicted temperature information of the decomposition furnace.
Chen, Keeler, and Domoto are analogous art because they contain functional similarities. They all relate to the prediction or control of an industrial thermal process based on temperature information and fuel related operating information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen and Keeler, and incorporate a calorific value correction technique, in which temperature deviation information and fuel related input information are used to calculate and correct the calorific value used by the control logic, such that the corrected calorific value of Chen’s waste fuel would be included in the fuel input information used for a subsequent temperature prediction, as taught by Domoto.
One of ordinary skill in the art would have been motivated to account for unavoidable variations in the calorific value of Chen’s waste fuel, as suggested by Domoto (Page 7, Correction of calorific value fluctuations).
Regarding claim 2, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen further teaches information on an input amount corresponding to each of different types of input fuels including the first recycled fuel (Par. [n0012], “Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model” – the coal feed rate and waste flow rate correspond to input amounts of different input fuels),
the process information including the fuel input information and the process state information is input (Par. [n0023], “The filtered decomposition furnace field data is input into the MISO Hammerstein model”; Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing {T(k), Fc(k), FR(k)|k=1,…,N}” – the coal feed and waste feed flow rates correspond to the fuel input information, and the decomposition furnace outlet temperature corresponds to the process state information), and
the measured temperature information of the first preheating chamber (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing … where T(k) represents the original value of the decomposition furnace outlet temperature at time k”).
Chen does not explicitly teach the fuel input information including calorific information corresponding to each of the input fuels,
the trained first neural network model is trained to output predicted temperature information of the first preheating chamber, and
The trained second neural network model is trained to output error information on the predicted temperature information output from the trained first neural network model and the measured temperature information of the first preheating chamber.
However, Keeler teaches the trained first neural network model is trained to output predicted temperature information of the first preheating chamber (Col. 8, lines 4-15, “the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network, labelled "NET 1" is trained on the pattern y(t) as target values … The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)” – NET 1 is trained using input information x(t) and corresponding measured target information y(t) to output predicted information o1(t). When applied to Chen’s decomposition furnace system, the predicted information corresponds to the predicted decomposition furnace temperature.), and
the trained second neural network model is trained to output error information on the predicted temperature information output from the trained first neural network model and the measured temperature information of the first preheating chamber (Col. 8, lines 22-41, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t) … The residual of the network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2". In the training procedure, the value of r1(t) is utilized as a target value with the input exercised with x(t). Once trained, the weights in the hidden layer 40 and associated interconnect layers 46 and 50 are frozen and then the network exercised with x(t) to provide a predicted output o2(t).” – Keeler trains NET 2 using, as its target, the residual r1(t) representing the difference between NET 1’s predicted output o1(t) and the measured target output y(t). Accordingly, trained NET 2 outputs o2(t) as predicted error information, which, when applied to Chen, corresponds to error information between the predicted and measured decomposition-furnace temperatures.).
Chen and Keeler do not explicitly teach the fuel input information including calorific information corresponding to each of the input fuels.
However, Domoto teaches the fuel input information including calorific information corresponding to each of the input fuels (Page 7, Correction of calorific value fluctuations, “the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time … method that always calculates the calorific values based on the relationship in the boiler efficiency calculation formula and applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto teaches maintaining and calculating calorific information corresponding to the fuel used by the thermal process.). Chen supplies the different input fuels, including coal and waste, while Domoto supplies the inclusion of corresponding calorific information in the fuel-related control information.
Chen’s coal feed and waste flow amounts and the corresponding calorific information supplied according to Domoto would be provided as Keeler’s input information x(t), and Chen’s measured decomposition furnace temperature would be used as Keeler’s target information y(t) when training NET 1.
Regarding claim 6, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen, Keeler, and Domoto further teach wherein the one or more processors identify predicted calorific information (Domoto, Page 7, Correction of calorific value fluctuations, “the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time … the process “steam temperature deviation => water-fuel ratio => calorific value correction” – identified true or corrected calorific value corresponds to the predicted calorific information identified based on the temperature deviation) of the first recycled fuel (Chen, Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace”) based on the obtained error information (Keeler, Col. 8, lines 22-25, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t)” – residual output corresponds to the obtained error information) and information on an input amount of the first recycled fuel included in the fuel input information (Chen, Par. [n0009], “FR(k) represents the original value of the waste flow rate of the decomposer at time k” – The waste flow rate corresponds to information on an input amount of the first recycled fuel included in the fuel input information.).
Regarding claim 8, Chen teaches a control method of an electronic device for implementing a temperature prediction and control system, the control method comprising (Par. [0003], “This invention relates to the field of decomposition furnace temperature modeling and control, and in particular to a method for controlling the temperature of a decomposition furnace in a cement kiln co-processing waste.”; Par. [n0051], “The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method”):
performing preprocessing on (Par. [n0010], “Use the moving average filtering method to preprocess the field data”), when process information including fuel input information of a cement manufacturing apparatus is received (Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing”), the received process information (Par. [n0009], “… represents the original value of the coal feed rate of the decomposer at time k, and … represents the original value of the waste flow rate of the decomposer at time k” – coal feed amount and garbage flow amount correspond to the fuel input information);
measured temperature of a first preheating chamber in the cement manufacturing apparatus (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing … where T(k) represents the original value of the decomposition furnace outlet temperature at time k” – decomposition furnace is interpreted as a first preheating chamber of the cement manufacturing apparatus.);
a first recycled fuel among input fuels (Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace not only saves fuel and some raw materials for the cement industry but also processes large amounts of municipal solid waste”); and
provide guidance information including predicted temperature information (Par. [n0087], “The predicted sequence of the outlet temperature is obtained through the prediction model … Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – the predicted temperature sequence is used as guidance information for determining the coal feed rate and waste flow rate. The prediction being the second predicted temperature results from the modification discussed below).
Chen does not explicitly teach inputting the preprocessed process information into a trained second neural network model to obtain error information on first predicted temperature information output from a trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus;
identifying predicted calorific information based on the obtained error information and the fuel input information;
inputting the fuel input information updated based on the identified predicted calorific information into the trained first neural network model to obtain second predicted temperature information of the first preheating chamber.
However, Keeler teaches inputting the preprocessed process information into a trained second neural network model to obtain error information on first predicted temperature information output from a trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus (Col. 8, lines 4-7, “the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 8, lines 22-25, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t)”; Col. 8, lines 33-41, “The residual of the kth network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2". In the training procedure, the value of r1(t) is utilized as a target value with the input exercised with x(t). Once trained, the weights in the hidden layer 40 and associated interconnect layers 46 and 50 are frozen and then the network exercised with x(t) to provide a predicted output o2(t).” – Keeler first trains NET 1 using the actual plant output y(t) and exercises NET 1 with process input x(t) to produce the first predicted output o1(t). Keeler determines the residual r1(t) as the difference between the actual output y(t) and NET 1’s first predicted output o1(t), and then trains NET 2 using r1(t) as the target and x(t) as the input. When Keeler’s residual neural-network architecture is incorporated into Chen, Chen’s processor inputs Chen’s preprocessed decomposition-furnace field data as x(t) into trained NET 2 and obtains NET 2’s output o2(t). Because NET 2 was trained using the difference between NET 1’s predicted output and the actual measured output as its target, o2(t) constitutes error information on the first predicted decomposition-furnace temperature and the measured decomposition-furnace temperature.); and
inputting updated input information into the trained first neural network model to obtain second predicted temperature information of the first preheating chamber (Col.8, lines 6-7, “The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 9, lines 58-63, “a plant predictive model 74 is developed with a neural network to accurately model the plant in accordance with the function f(c(t),s(t)) to provide an output oP(t), which represents the predicted output of plant predictive model 74.”; Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error.” – Keeler teaches a trained first neural network that receives input information and produces a predicted plant output and further teaches updating input information and processing the updated input information through a neural-network predictive model to obtain a new predicted plant output. In the modified Chen system, Chen’s processor would input updated coal-feed and waste-flow information into the trained first neural-network temperature-prediction model and obtain a new predicted decomposition-furnace temperature, which corresponds to the second predicted temperature information of the first preheating chamber.).
Chen and Keeler are analogous art because they contain functional similarities. They both relate to the prediction and control of nonlinear industrial processes using process-input information and measured process-output information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, and incorporate a residual neural network architecture, including a first neural network for predicting a plant output and a second neural network for predicting residual information associated with the predicted and measured plant outputs, as taught by Keeler.
One of ordinary skill in the art would have been motivated to account for unmeasured external influences affecting the predicted decomposition furnace temperature, as suggested by Keeler (Col. 13, lines 60-63).
Chen and Keeler do not explicitly teach identifying predicted calorific information based on the obtained error information and the fuel input information; and
Updating fuel input information based on the identified predicted calorific information.
However, Domoto teaches identifying predicted calorific information based on the obtained error information and the fuel input information (Page 4, Improvement of high-speed load response technology, “T2 control uses the water-fuel ratio (fuel flow rate) to control the steam temperature at the outlet of the secondary superheater”; Page 7, Correction of calorific value fluctuations, “the fluctuation of the calorific value within the same coal brand was corrected according to the water-fuel ratio correction amount based on the idea that the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time. A function to perform this automatically has been provided … “steam temperature deviation => water-fuel ratio => calorific value correction”” – Domoto identifies the water-fuel ratio as the fuel flow rate and teaches identifying the true calorific-value setting from the correction produced in response to the steam-temperature deviation. In the modified system, Keeler’s obtained residual temperature error corresponds to Domoto’s steam-temperature deviation, Chen’s waste-flow-rate information corresponds to Domoto’s fuel-flow information, and Domoto’s identified true calorific-value setting corresponds to the predicted calorific information.); and
Updating fuel input information based on the identified predicted calorific information (Page 7, Correction of calorific value fluctuations, “To make the correction of calorific value fluctuations faster, the process was revised to a method that always calculates the calorific values based on the relationship in the boiler efficiency calculation formula and applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto calculates the fuel calorific value and corrects the calorific-value information used by the control logic when the calculated value differs from the existing value. Thus, correcting the calorific-value information associated with the fuel corresponds to updating the fuel input information based on the identified predicted calorific information.).
Thus, in the modified system, Chen’s processor would use Keeler’s obtained residual temperature error as Domoto’s steam-temperature deviation and Chen’s waste-flow-rate information as Domoto’s fuel-flow information to identify a corrected calorific value for Chen’s recycled waste fuel. Chen’s processor would update the calorific-value information associated with the recycled waste fuel and input the resulting updated fuel input information into Keeler’s trained first neural-network temperature-prediction model to obtain second predicted temperature information of the decomposition furnace.
Chen, Keeler, and Domoto are analogous art because they contain functional similarities. They all relate to the prediction or control of an industrial thermal process based on temperature information and fuel related operating information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen and Keeler, and incorporate a calorific value correction technique, in which temperature deviation information and fuel related input information are used to calculate and correct the calorific value used by the control logic, such that the corrected calorific value of Chen’s waste fuel would be included in the fuel input information used for a subsequent temperature prediction, as taught by Domoto.
One of ordinary skill in the art would have been motivated to account for unavoidable variations in the calorific value of Chen’s waste fuel, as suggested by Domoto (Page 7, Correction of calorific value fluctuations).
Regarding claim 9, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen further teaches information on an input amount corresponding to each of different types of input fuels including the first recycled fuel (Par. [n0012], “Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model” – the coal feed rate and waste flow rate correspond to input amounts of different input fuels),
the process information including the fuel input information and the process state information is input (Par. [n0023], “The filtered decomposition furnace field data is input into the MISO Hammerstein model”; Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing {T(k), Fc(k), FR(k)|k=1,…,N}” – the coal feed and waste feed flow rates correspond to the fuel input information, and the decomposition furnace outlet temperature corresponds to the process state information), and
the measured temperature information of the first preheating chamber (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing … where T(k) represents the original value of the decomposition furnace outlet temperature at time k”).
Chen does not explicitly teach the fuel input information including calorific information corresponding to each of the input fuels,
the trained first neural network model is trained to output predicted temperature information of the first preheating chamber, and
the trained second neural network model is trained to output error information on the predicted temperature information output from the trained first neural network model and measured temperature information of the first preheating chamber.
However, Keeler teaches the trained first neural network model is trained to output predicted temperature information of the first preheating chamber (Col. 8, lines 4-15, “the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network, labelled "NET 1" is trained on the pattern y(t) as target values … The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)” – NET 1 is trained using input information x(t) and corresponding measured target information y(t) to output predicted information o1(t). When applied to Chen’s decomposition furnace system, the predicted information corresponds to the predicted decomposition furnace temperature.), and
the trained second neural network model is trained to output error information on the predicted temperature information output from the trained first neural network model and measured temperature information of the first preheating chamber (Col. 8, lines 22-41, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t) … The residual of the network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2". In the training procedure, the value of r1(t) is utilized as a target value with the input exercised with x(t). Once trained, the weights in the hidden layer 40 and associated interconnect layers 46 and 50 are frozen and then the network exercised with x(t) to provide a predicted output o2(t).” – Keeler trains NET 2 using, as its target, the residual r1(t) representing the difference between NET 1’s predicted output o1(t) and the measured target output y(t). Accordingly, trained NET 2 outputs o2(t) as predicted error information, which, when applied to Chen, corresponds to error information between the predicted and measured decomposition-furnace temperatures.).
Chen and Keeler do not explicitly teach the fuel input information including calorific information corresponding to each of the input fuels.
However, Domoto teaches the fuel input information including calorific information corresponding to each of the input fuels (Page 7, Correction of calorific value fluctuations, “the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time … method that always calculates the calorific values based on the relationship in the boiler efficiency calculation formula and applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto teaches maintaining and calculating calorific information corresponding to the fuel used by the thermal process.). Chen supplies the different input fuels, including coal and waste, while Domoto supplies the inclusion of corresponding calorific information in the fuel-related control information.
Chen’s coal feed and waste flow amounts and the corresponding calorific information supplied according to Domoto would be provided as Keeler’s input information x(t), and Chen’s measured decomposition furnace temperature would be used as Keeler’s target information y(t) when training NET 1.
Regarding claim 13, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen, Keeler, and Domoto further teach wherein the identifying of the calorific information includes identifying predicted calorific information (Domoto, Page 7, Correction of calorific value fluctuations, “the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time … the process “steam temperature deviation => water-fuel ratio => calorific value correction” – identified true or corrected calorific value corresponds to the predicted calorific information identified based on the temperature deviation) of the first recycled fuel (Chen, Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace”) based on the obtained error information (Keeler, Col. 8, lines 22-25, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t)” – residual output corresponds to the obtained error information) and information on an input amount of the first recycled fuel included in the fuel input information (Chen, Par. [n0009], “FR(k) represents the original value of the waste flow rate of the decomposer at time k” – The waste flow rate corresponds to information on an input amount of the first recycled fuel included in the fuel input information.).
Regarding claim 14, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen, Keeler, and Domoto do not explicitly teach identifying control information, when a user input corresponding to the obtained guidance information is received through the user interface, corresponding to the received user input; and
transmitting the identified control information to a control engine.
However, Federspiel teaches identifying control information (Par. [0035], “Upon receiving the instruction to apply the recommended modification, the AI engine 224 may receive the new control setpoints as an input 230” – the new control setpoints correspond to the identified control information.), when a user input corresponding to the obtained guidance information is received through the user interface, corresponding to the received user input (Par. [0027], “the computer 100 may receive, for example via a user interface displayed on the display device 108, an input selecting one of the recommended change(s).”; Par. [0038], “The user may also select the recommended modification or any other modifications via the GUI.”).
The combination of Federspiel and Chen teaches transmitting the identified control information (Federspiel, Par. [0027], “The computer 100 may then apply the selected recommended change to the environmentally controlled space 120”) to a control engine (Chen, [n0087], “After the calculated coal feed rate and waste flow rate are processed … they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s industrial field control system corresponds to the control engine).
Chen, Keeler, Domoto, and Federspiel are analogous art because they contain functional similarities. They all relate to controlling a thermal process based on predicted process information and adjustments to operating inputs.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, Keeler, and Domoto, and incorporate a user interface through which a user selects obtained guidance information, as taught by Federspiel, such that the selected guidance is identified as control information and transmitted to Chen’s industrial field decomposition-furnace control system.
One of ordinary skill in the art would have been motivated to improve the efficiency of the control system by allowing a user to select and apply a recommended modification that provides greater cost or resource savings while maintaining acceptable process control, as suggested by Federspiel (Par. [0042]).
Regarding claim 15, Chen teaches a non-transitory computer-readable recording medium that stores, when executed by a processor of an electronic device for implementing a temperature prediction and control system, computer instructions that cause the electronic device to perform operations of (Par. [0003], “This invention relates to the field of decomposition furnace temperature modeling and control, and in particular to a method for controlling the temperature of a decomposition furnace in a cement kiln co-processing waste.”; Par. [n0051], “The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method”):
performing preprocessing on (Par. [n0010], “Use the moving average filtering method to preprocess the field data”), when process information including fuel input information of a cement manufacturing apparatus is received (Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing”), the received process information (Par. [n0009], “… represents the original value of the coal feed rate of the decomposer at time k, and … represents the original value of the waste flow rate of the decomposer at time k” – coal feed amount and garbage flow amount correspond to the fuel input information);
measured temperature of a first preheating chamber in the cement manufacturing apparatus (Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing … where T(k) represents the original value of the decomposition furnace outlet temperature at time k” – decomposition furnace is interpreted as a first preheating chamber of the cement manufacturing apparatus.);
a first recycled fuel among input fuels (Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace not only saves fuel and some raw materials for the cement industry but also processes large amounts of municipal solid waste”); and
providing guidance information including predicted temperature information (Par. [n0087], “The predicted sequence of the outlet temperature is obtained through the prediction model … Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – the predicted temperature sequence is used as guidance information for determining the coal feed rate and waste flow rate. The prediction being the second predicted temperature results from the modification discussed below).
Chen does not explicitly teach inputting the preprocessed process information into a trained second neural network model to obtain error information on first predicted temperature information output from a trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus;
identifying predicted calorific information based on the obtained error information and the fuel input information; and
inputting the fuel input information updated based on the identified predicted calorific information into the trained first neural network model to obtain second predicted temperature information of the first preheating chamber.
However, Keeler teaches inputting the preprocessed process information into a trained second neural network model to obtain error information on first predicted temperature information output from a trained first neural network model and measured temperature of a first preheating chamber in the cement manufacturing apparatus (Col. 8, lines 4-7, “the pattern y(t) is provided as a time series output of a plant for a time series input x(t). The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 8, lines 22-25, “the residual output layer 62 represents the first residue output r1(t) that constitutes the difference between the predicted output o1(t) of the first network NET 1 and the target output y(t)”; Col. 8, lines 33-41, “The residual of the kth network is used to train the (k+ 1) kth network, which residue is utilized to train the second network, labelled "NET 2". In the training procedure, the value of r1(t) is utilized as a target value with the input exercised with x(t). Once trained, the weights in the hidden layer 40 and associated interconnect layers 46 and 50 are frozen and then the network exercised with x(t) to provide a predicted output o2(t).” – Keeler first trains NET 1 using the actual plant output y(t) and exercises NET 1 with process input x(t) to produce the first predicted output o1(t). Keeler determines the residual r1(t) as the difference between the actual output y(t) and NET 1’s first predicted output o1(t), and then trains NET 2 using r1(t) as the target and x(t) as the input. When Keeler’s residual neural-network architecture is incorporated into Chen, Chen’s processor inputs Chen’s preprocessed decomposition-furnace field data as x(t) into trained NET 2 and obtains NET 2’s output o2(t). Because NET 2 was trained using the difference between NET 1’s predicted output and the actual measured output as its target, o2(t) constitutes error information on the first predicted decomposition-furnace temperature and the measured decomposition-furnace temperature.); and
inputting updated input information into the trained first neural network model to obtain second predicted temperature information of the first preheating chamber (Col.8, lines 6-7, “The first network, labelled "NET 1" is trained on the pattern y(t) as target values”; Col. 8, lines 13-15, “The first network NET 1 is run by exercising the network with the time series x(t) to generate a predicted output o1(t)”; Col. 9, lines 58-63, “a plant predictive model 74 is developed with a neural network to accurately model the plant in accordance with the function f(c(t),s(t)) to provide an output oP(t), which represents the predicted output of plant predictive model 74.”; Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error.” – Keeler teaches a trained first neural network that receives input information and produces a predicted plant output and further teaches updating input information and processing the updated input information through a neural-network predictive model to obtain a new predicted plant output. In the modified Chen system, Chen’s processor would input updated coal-feed and waste-flow information into the trained first neural-network temperature-prediction model and obtain a new predicted decomposition-furnace temperature, which corresponds to the second predicted temperature information of the first preheating chamber.).
Chen and Keeler are analogous art because they contain functional similarities. They both relate to the prediction and control of nonlinear industrial processes using process-input information and measured process-output information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, and incorporate a residual neural network architecture, including a first neural network for predicting a plant output and a second neural network for predicting residual information associated with the predicted and measured plant outputs, as taught by Keeler.
One of ordinary skill in the art would have been motivated to account for unmeasured external influences affecting the predicted decomposition furnace temperature, as suggested by Keeler (Col. 13, lines 60-63).
Chen and Keeler do not explicitly teach identifying predicted calorific information based on the obtained error information and the fuel input information; and
Updating fuel input information based on the identified predicted calorific information.
However, Domoto teaches identifying predicted calorific information based on the obtained error information and the fuel input information (Page 4, Improvement of high-speed load response technology, “T2 control uses the water-fuel ratio (fuel flow rate) to control the steam temperature at the outlet of the secondary superheater”; Page 7, Correction of calorific value fluctuations, “the fluctuation of the calorific value within the same coal brand was corrected according to the water-fuel ratio correction amount based on the idea that the calorific value setting at which the water-fuel ratio correction for steam temperature control is 0 (zero) is the true value of the calorific value at that time. A function to perform this automatically has been provided … “steam temperature deviation => water-fuel ratio => calorific value correction”” – Domoto identifies the water-fuel ratio as the fuel flow rate and teaches identifying the true calorific-value setting from the correction produced in response to the steam-temperature deviation. In the modified system, Keeler’s obtained residual temperature error corresponds to Domoto’s steam-temperature deviation, Chen’s waste-flow-rate information corresponds to Domoto’s fuel-flow information, and Domoto’s identified true calorific-value setting corresponds to the predicted calorific information.); and
Updating fuel input information based on the identified predicted calorific information (Page 7, Correction of calorific value fluctuations, “To make the correction of calorific value fluctuations faster, the process was revised to a method that always calculates the calorific values based on the relationship in the boiler efficiency calculation formula and applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto calculates the fuel calorific value and corrects the calorific-value information used by the control logic when the calculated value differs from the existing value. Thus, correcting the calorific-value information associated with the fuel corresponds to updating the fuel input information based on the identified predicted calorific information.).
Thus, in the modified system, Chen’s processor would use Keeler’s obtained residual temperature error as Domoto’s steam-temperature deviation and Chen’s waste-flow-rate information as Domoto’s fuel-flow information to identify a corrected calorific value for Chen’s recycled waste fuel. Chen’s processor would update the calorific-value information associated with the recycled waste fuel and input the resulting updated fuel input information into Keeler’s trained first neural-network temperature-prediction model to obtain second predicted temperature information of the decomposition furnace.
Chen, Keeler, and Domoto are analogous art because they contain functional similarities. They all relate to the prediction or control of an industrial thermal process based on temperature information and fuel related operating information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen and Keeler, and incorporate a calorific value correction technique, in which temperature deviation information and fuel related input information are used to calculate and correct the calorific value used by the control logic, such that the corrected calorific value of Chen’s waste fuel would be included in the fuel input information used for a subsequent temperature prediction, as taught by Domoto.
One of ordinary skill in the art would have been motivated to account for unavoidable variations in the calorific value of Chen’s waste fuel, as suggested by Domoto (Page 7, Correction of calorific value fluctuations).
Claim(s) 3-4, 7, 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. CN 116382371 A (hereinafter Chen) in view of Keeler US 5,353,207 A (hereinafter Keeler) and Domoto (Expectations for Changing Steam Power Plants and Supporting Technologies, 2019) (hereinafter Domoto), and further in view of Federspiel USPGPUB 2021/0073636 A1 (hereinafter Federspiel).
Regarding claim 3, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen further teaches wherein the fuel input information includes information on an input amount corresponding to each of different types of input fuels including at least one of the first recycled fuel, main fuel, and auxiliary fuel (Par. [n0012], “Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model”; Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace not only saves fuel and some raw materials for the cement industry but also processes large amounts of municipal solid waste” – coal feed rate and waste flow rate correspond to input amounts of different types of input fuels. The waste corresponds to the first recycled fuel, and the pulverized coal corresponds to the main fuel.),
the one or more processors are configured to (Par. [n0051], “The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method”):
a target temperature value of the first preheating chamber in the cement manufacturing apparatus (Par. [n0042], “ysp represents the expected value of the decomposition furnace outlet temperature” – the expected decomposition furnace outlet temperature is interpreted as the target temperature value of the first preheating chamber.);
identify the input amount corresponding to each of the different types of input fuels to ensure that the temperature value of the first preheating chamber reaches the target temperature value based on predicted temperature information and the target temperature value (Par. [n0087], “The predicted sequence of the outlet temperature is obtained through the prediction model, and the softened sequence of the outlet temperature setpoint is obtained through the reference trajectory. The difference between the two and the weighted sum of the intermediate vector constitute a quadratic performance index. The intermediate vector is obtained through the generalized predictive control algorithm. Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate.”; Par. [n0058], “The present invention adopts a two-step predictive control method based on the MISO Hammerstein model, which enables the decomposition furnace outlet temperature to have good tracking performance for the set value” – Chen determines a difference between the predicted outlet-temperature sequence and the target-temperature sequence, uses the difference to obtain the intermediate control vector, and substitutes the intermediate control vector into the nonlinear model to identify the coal-feed and waste-flow amounts. Thus, Chen identifies the respective fuel-input amounts based on the predicted temperature and target temperature so that the decomposition-furnace temperature tracks the target value.); and
obtain guidance information corresponding to the identified input amount (Par. [n0087], “Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s processor obtains calculated and constrained coal-feed and waste-flow values by solving the nonlinear model and applying the disclosed constraints. The calculated values constitute information specifying the identified input amounts and therefore correspond to the obtained guidance information.).
Chen does not explicitly teach a user interface,
calorific information corresponding to each input amount,
receive a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface,
identifying the input amount based on the obtained second predicted temperature information and the received target temperature value, and
provide a user interface (UI) including the obtained guidance information
However, Keeler teaches identifying the input amount based on the obtained second predicted temperature information and the received target temperature value (Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error. This iteration continues until the error is reduced below a predetermined value.” – Keeler teaches processing updated control input information through a neural network predictive model and iteratively identifying the control input based on the new predicted output and its error relative to the desired output. When applied to Chen, the control inputs correspond to the coal feed and waste flow amounts, and the predicted output corresponds to the obtained second predicted decomposition furnace temperature.).
Chen and Keeler do not explicitly teach a user interface,
calorific information corresponding to each input amount,
receive a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface, and
provide a user interface (UI) including the obtained guidance information.
However, Domoto teaches calorific information corresponding to each input amount (Page 7, Correction of calorific value fluctuations, “Since coal has various calorific values depending on the brand, coal-fired boilers cannot avoid fluctuations in the calorific value of coal … applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto’s calculated calorific value corresponds to calorific information associated with the fuel represented in the control logic. Thus, Chen supplies the respective coal feed and waste flow input amounts, while Domoto supplies the corresponding fuel calorific information maintained with the fuel related control information.).
Chen, Keeler, and Domoto do not explicitly teach a user interface
receive a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface, and
provide a user interface (UI) including the obtained guidance information.
However, Federspiel teaches a user interface (Par. [0007], “graphical user interface”),
receive a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface (Par. [0023] - Par. [0024], “The computer 100 may be communicatively coupled to a display device 108 that may display a user interface (UI) through which a user 118 may the setpoints associated with the environmental maintenance modules to best maintain desired physical conditions in the controlled space 120 … the user 118 may set the setpoint of a first environmental maintenance module 122 at 58° F.” – When incorporated into Chen, the temperature setpoint received through the user interface corresponds to Chen’s target decomposition furnace temperature.), and
provide a user interface (UI) including the obtained guidance information (Par. [0026], “The computer 100 may recommend a temperature and/or pressure setpoint that is slightly different than the user's desired setpoint, that would result in a lower cost function”; Par. [0027], “The computer 100 may then communicate the recommended change(s) to the user. For example, the computer 100 may generate, via visual representation generation module 107, a visual representation (e.g. a table, a graph, a map, a message including alphanumerical characters and/or symbols) of the determined recommended change(s)”).
Chen, Keeler, Domoto, and Federspiel are analogous art because they contain functional similarities. They all relate to controlling a thermal process based on a target temperature and providing information concerning recommended adjustments to operating inputs.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, Keeler, and Domoto, and incorporate a user interface through which a user provides a target temperature value and receives a visual representation of recommended modifications, as taught by Federspiel, such that the target temperature value would be Chen’s target decomposition furnace temperature and the recommended modifications would include Chen’s identified fuel-input guidance.
One of ordinary skill in the art would have been motivated to provide recommended operating modifications to the user to improve the operating efficiency of the thermal system by providing recommended setpoint modifications that result in a lower cost function, as suggested by Federspiel (Par. [0025] – [0026]).
Regarding claim 4, the combination of Chen, Keeler, Domoto, and Federspiel teaches all the limitations of the base claims as outlined above.
Chen further teaches wherein the one or more processors are configured (Par. [n0112], “processor is configured to execute the program stored in the memory”);
the input amount corresponding to each input fuel included in the fuel input information (Par. [n0030], “select the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1, and assign it to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.” – the coal feed and waste flow amounts correspond to respective input amounts for the coal and waste fuels.);
obtain guidance information on the input fuel (Par. [n0087], “Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate”); and
provide guide information including the obtained guidance information (Par. [n0087], “After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – the calculated coal feed and waste flow amounts correspond to the obtained guidance information, which is provided to the industrial field control system.).
Chen does not explicitly teach identify sub-fuel input information in which the input amount corresponding to each input fuel included in the fuel input information has changed;
input the sub-fuel input information into the trained first neural network model to obtain second-sub predicted temperature information: and
obtain guidance information on the input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information.
However, Chen and Keeler teach identify sub-fuel input information in which the input amount corresponding to each input fuel included in the fuel input information has changed, wherein Chen teaches the respective coal feed and waste flow amounts corresponding to the coal and waste fuels (Chen, Par. [n0030], “select the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1, and assign it to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.”), and Keeler teaches changing existing input information and identifying the resulting changed information (Keeler, Col. 10, lines 27-36, “the value △c(t+ 1) is added initially to the input value c(t) … The final value is then output as the new predicted control variables c(t+ 1).” – adding △c(t+ 1) changes the existing input information, and outputting the final value as the new control variables identifies the resulting changed input information. In the modified Chen system, Keeler’s input change operation would be applied to each of Chen’s coal feed and waste flow amounts. One of ordinary skill in the art would have been motivated to apply Keeler’s input change operation to each of Chen’s coal feed and waste flow amounts because Chen uses waste as a partial alternative to pulverized coal and jointly determines both fuel amounts to control the decomposition furnace temperature. Changing both amounts would permit evaluation of an alternative fuel mixture while preserving the desired thermal output.).
Keeler further teaches input the sub-fuel input information into the trained first neural network model to obtain second-sub predicted temperature information (Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error” – the changed input information is processed through the neural network model to obtain a new predicted output. In the modified Chen system, the changed coal feed and waste flow information corresponds to the sub-fuel input information, and the new predicted output corresponds to the second-sub predicted decomposition furnace temperature information.): and
obtain guidance information on the input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information (Col. 9, lines 58- 63, “a plant predictive model 74 is developed with a neural network … to provide an output oP(t), which represents the predicted output of plant predictive model 74.”; Col. 10, lines 27-55, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error … The final value is then output as the new predicted control variables c(t+ 1). This new c(t+1) comprises the control inputs that are required to achieve the desired actual output from the plant 72.” – the predicted output produced from the existing control inputs corresponds to the obtained second predicted temperature information, the new predicted output produced from the changed control inputs corresponds to the second sub-predicted temperature information, and the new predicted control variables obtained through the iterative prediction process correspond to guidance information on the input fuel. In the modified system, Keeler’s new predicted control variables would correspond to Chen’s coal feed and waste flow amounts.).
Regarding claim 7, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen, Keeler, and Domoto do not explicitly teach a user interface,
wherein the one or more processors are configured to:
identify control information, when a user input corresponding to the obtained guidance information is received through the user interface, corresponding to the received user input; and
transmit the identified control information to a control engine through the communication interface.
However, Federspiel teaches a user interface (Par. [0027], “the computer 100 may receive, for example via a user interface displayed on the display device 108, an input selecting one of the recommended change(s).”),
wherein the one or more processors are configured to (Par. [0061], “any of the embodiments of the present invention can be implemented in the form of control logic using hardware (e.g. an application specific integrated circuit or field programmable gate array) and/or using computer software with a generally programmable processor in a modular or integrated manner.”):
identify control information (Par. [0035], “Upon receiving the instruction to apply the recommended modification, the AI engine 224 may receive the new control setpoints as an input 230” – the new control setpoints correspond to the identified control information.), when a user input corresponding to the obtained guidance information is received through the user interface, corresponding to the received user input (Par. [0027], “the computer 100 may receive, for example via a user interface displayed on the display device 108, an input selecting one of the recommended change(s).”; Par. [0038], “The user may also select the recommended modification or any other modifications via the GUI.”).
The combination of Federspiel and Chen teaches transmit the identified control information (Federspiel, Par. [0027], “The computer 100 may then apply the selected recommended change to the environmentally controlled space 120”) to a control engine (Chen, [n0087], “After the calculated coal feed rate and waste flow rate are processed … they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s industrial field control system corresponds to the control engine) through the communication interface (Chen, Par. [n0060], “Real-time acquisition of field data from the decomposition furnace under waste co-processing”; Par. [n0061], “Due to the harsh industrial environment and numerous sources of interference, errors inevitably occur during data collection and transmission. Therefore, the data from Step 1 needs to be filtered to reduce the impact of interference.”; Par. [n0087], “After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s electronic device receives transmitted field data from the decomposition furnace and transmits calculated control information to the industrial field control system. Thus, Chen’s electronic device must include an input/output communication interface through which the field data are received and the calculated control information is transmitted.);
Chen, Keeler, Domoto, and Federspiel are analogous art because they contain functional similarities. They all relate to controlling a thermal process based on predicted process information and adjustments to operating inputs.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system having a residual neural-network architecture and calorific-value correction technique, as taught by Chen, Keeler, and Domoto, and incorporate a user interface through which a user selects obtained guidance information, as taught by Federspiel, such that the selected guidance is identified as control information and transmitted through the input/output communication interface of Chen’s electronic device to Chen’s industrial field decomposition-furnace control system.
One of ordinary skill in the art would have been motivated to improve the efficiency of the control system by allowing a user to select and apply a recommended modification that provides greater cost or resource savings while maintaining acceptable process control, as suggested by Federspiel (Par. [0042]).
Regarding claim 10, the combination of Chen, Keeler, and Domoto teaches all the limitations of the base claims as outlined above.
Chen further teaches wherein the fuel input information includes information on an input amount corresponding to each of different types of input fuels including at least one of the first recycled fuel, main fuel, and auxiliary fuel (Par. [n0012], “Use the coal feed rate u1(k) and the waste flow rate u2(k) at time k as the two inputs of the MISO Hammerstein model”; Par. [n0003], “Combustion of waste as a partial alternative fuel, along with pulverized coal, in a decomposition furnace not only saves fuel and some raw materials for the cement industry but also processes large amounts of municipal solid waste” – coal feed rate and waste flow rate correspond to input amounts of different types of input fuels. The waste corresponds to the first recycled fuel, and the pulverized coal corresponds to the main fuel.),
a target temperature value of the first preheating chamber in the cement manufacturing apparatus (Par. [n0042], “ysp represents the expected value of the decomposition furnace outlet temperature” – the expected decomposition furnace outlet temperature is interpreted as the target temperature value of the first preheating chamber.);
identifying the input amount corresponding to each of the different types of input fuels to ensure that the temperature value of the first preheating chamber reaches the target temperature value based on the obtained second predicted temperature information and the received target temperature value (Par. [n0087], “The predicted sequence of the outlet temperature is obtained through the prediction model, and the softened sequence of the outlet temperature setpoint is obtained through the reference trajectory. The difference between the two and the weighted sum of the intermediate vector constitute a quadratic performance index. The intermediate vector is obtained through the generalized predictive control algorithm. Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate.”; Par. [n0058], “The present invention adopts a two-step predictive control method based on the MISO Hammerstein model, which enables the decomposition furnace outlet temperature to have good tracking performance for the set value” – Chen determines a difference between the predicted outlet-temperature sequence and the target-temperature sequence, uses the difference to obtain the intermediate control vector, and substitutes the intermediate control vector into the nonlinear model to identify the coal-feed and waste-flow amounts. Thus, Chen identifies the respective fuel-input amounts based on the predicted temperature and target temperature so that the decomposition-furnace temperature tracks the target value.); and
obtaining guidance information corresponding to the identified input amount (Par. [n0087], “Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate. After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – Chen’s processor obtains calculated and constrained coal-feed and waste-flow values by solving the nonlinear model and applying the disclosed constraints. The calculated values constitute information specifying the identified input amounts and therefore correspond to the obtained guidance information.).
Chen does not explicitly teach calorific information corresponding to each input amount,
receiving a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface, and
identifying the input amount based on the obtained second predicted temperature information and the received target temperature value, and
providing a user interface (UI) including the obtained guidance information.
However, Keeler teaches identifying the input amount based on the obtained second predicted temperature information and the received target temperature value (Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error. This iteration continues until the error is reduced below a predetermined value.” – Keeler teaches processing updated control input information through a neural network predictive model and iteratively identifying the control input based on the new predicted output and its error relative to the desired output. When applied to Chen, the control inputs correspond to the coal feed and waste flow amounts, and the predicted output corresponds to the obtained second predicted decomposition furnace temperature.).
Chen and Keeler do not explicitly teach calorific information corresponding to each input amount,
receiving a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface, and
providing a user interface (UI) including the obtained guidance information.
However, Domoto teaches calorific information corresponding to each input amount (Page 7, Correction of calorific value fluctuations, “Since coal has various calorific values depending on the brand, coal-fired boilers cannot avoid fluctuations in the calorific value of coal … applies calorific value correction when there is a difference between the “calorific value in the control logic” and the “calculated calorific value.”” – Domoto’s calculated calorific value corresponds to calorific information associated with the fuel represented in the control logic. Thus, Chen supplies the respective coal feed and waste flow input amounts, while Domoto supplies the corresponding fuel calorific information maintained with the fuel related control information.).
Chen, Keeler, and Domoto do not explicitly teach receiving a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface, and
providing a user interface (UI) including the obtained guidance information.
However, Federspiel teaches receiving a target temperature value of the first preheating chamber in the cement manufacturing apparatus through the user interface (Par. [0023] - Par. [0024], “The computer 100 may be communicatively coupled to a display device 108 that may display a user interface (UI) through which a user 118 may the setpoints associated with the environmental maintenance modules to best maintain desired physical conditions in the controlled space 120 … the user 118 may set the setpoint of a first environmental maintenance module 122 at 58° F.” – When incorporated into Chen, the temperature setpoint received through the user interface corresponds to Chen’s target decomposition furnace temperature.), and
providing a user interface (UI) including the obtained guidance information (Par. [0026], “The computer 100 may recommend a temperature and/or pressure setpoint that is slightly different than the user's desired setpoint, that would result in a lower cost function”; Par. [0027], “The computer 100 may then communicate the recommended change(s) to the user. For example, the computer 100 may generate, via visual representation generation module 107, a visual representation (e.g. a table, a graph, a map, a message including alphanumerical characters and/or symbols) of the determined recommended change(s)”).
Chen, Keeler, Domoto, and Federspiel are analogous art because they contain functional similarities. They all relate to controlling a thermal process based on a target temperature and providing information concerning recommended adjustments to operating inputs.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, Keeler, and Domoto, and incorporate a user interface through which a user provides a target temperature value and receives a visual representation of recommended modifications, as taught by Federspiel, such that the target temperature value would be Chen’s target decomposition furnace temperature and the recommended modifications would include Chen’s identified fuel-input guidance.
One of ordinary skill in the art would have been motivated to provide recommended operating modifications to the user to improve the operating efficiency of the thermal system by providing recommended setpoint modifications that result in a lower cost function, as suggested by Federspiel (Par. [0025] – [0026]).
Regarding claim 11, the combination of Chen, Keeler, Domoto, and Federspiel teaches all the limitations of the base claims as outlined above.
Chen further teaches the input amount corresponding to each input fuel included in the fuel input information (Par. [n0030], “select the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1, and assign it to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.” – the coal feed and waste flow amounts correspond to respective input amounts for the coal and waste fuels.);
obtaining guidance information on the input fuel (Par. [n0087], “Then, the intermediate vector is substituted back into the nonlinear model to solve for the coal feed rate and waste flow rate”); and
providing guide information including the obtained guidance information (Par. [n0087], “After the calculated coal feed rate and waste flow rate are processed by upper and lower limit constraints and incremental constraints, they are sent to the industrial field control system to control the outlet temperature of the decomposition furnace” – the calculated coal feed and waste flow amounts correspond to the obtained guidance information, which is provided to the industrial field control system.).
Chen does not explicitly teach identifying sub-fuel input information in which the input amount corresponding to each input fuel included in the fuel input information has changed;
inputting the sub-fuel input information into the trained first neural network model to obtain second-sub predicted temperature information; and
obtaining guidance information on the input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information.
However, Chen and Keeler teach identifying sub-fuel input information in which the input amount corresponding to each input fuel included in the fuel input information has changed, wherein Chen teaches the respective coal feed and waste flow amounts corresponding to the coal and waste fuels (Chen, Par. [n0030], “select the corresponding real root that is closest to the coal feed amount u1(k-1) and the waste flow rate u2(k-1) at time k-1, and assign it to the coal feed amount u1(k) and the waste flow rate u2(k) at time k.”), and Keeler teaches changing existing input information and identifying the resulting changed information (Keeler, Col. 10, lines 27-36, “the value △c(t+ 1) is added initially to the input value c(t) … The final value is then output as the new predicted control variables c(t+ 1).” – adding △c(t+ 1) changes the existing input information, and outputting the final value as the new control variables identifies the resulting changed input information. In the modified Chen system, Keeler’s input change operation would be applied to each of Chen’s coal feed and waste flow amounts. One of ordinary skill in the art would have been motivated to apply Keeler’s input change operation to each of Chen’s coal feed and waste flow amounts because Chen uses waste as a partial alternative to pulverized coal and jointly determines both fuel amounts to control the decomposition furnace temperature. Changing both amounts would permit evaluation of an alternative fuel mixture while preserving the desired thermal output.).
Keeler further teaches inputting the sub-fuel input information into the trained first neural network model to obtain second-sub predicted temperature information (Col. 10, lines 27-31, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error” – the changed input information is processed through the neural network model to obtain a new predicted output. In the modified Chen system, the changed coal feed and waste flow information corresponds to the sub-fuel input information, and the new predicted output corresponds to the second-sub predicted decomposition furnace temperature information.): and
obtaining guidance information on the input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information (Col. 9, lines 58- 63, “a plant predictive model 74 is developed with a neural network … to provide an output oP(t), which represents the predicted output of plant predictive model 74.”; Col. 10, lines 27-55, “the value △c(t+ 1) is added initially to the input value c(t) and this sum then processed through plant predictive model 74 to provide a new predicted output oP(t) and a new error … The final value is then output as the new predicted control variables c(t+ 1). This new c(t+1) comprises the control inputs that are required to achieve the desired actual output from the plant 72.” – the predicted output produced from the existing control inputs corresponds to the obtained second predicted temperature information, the new predicted output produced from the changed control inputs corresponds to the second sub-predicted temperature information, and the new predicted control variables obtained through the iterative prediction process correspond to guidance information on the input fuel. In the modified system, Keeler’s new predicted control variables would correspond to Chen’s coal feed and waste flow amounts.).
Claim(s) 5 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. CN 116382371 A (hereinafter Chen) in view of Keeler US 5,353,207 A (hereinafter Keeler), Domoto (Expectations for Changing Steam Power Plants and Supporting Technologies, 2019) (hereinafter Domoto), and Federspiel USPGPUB 2021/0073636 A1 (hereinafter Federspiel), and further in view of Merk et al. USPGPUB 2025/0004429 A1 (hereinafter Merk).
Regarding claim 5, the combination of Chen, Keeler, Domoto, and Federspiel teaches all the limitations of the base claims as outlined above.
Chen further teaches wherein the preprocessed process information includes temperature history information of the first preheating chamber and history information of the input fuel (Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing {T(k), Fc(k), FR(k)|k=1,…,N}”; Par. [n0010], “Use the moving average filtering method to preprocess the field data” – the sequence T(k) corresponds to the temperature history information of the decomposition furnace, and the sequences Fc(k) and FR(k) correspond to the history information of the coal and waste fuels included in the preprocessed process information.).
Chen, Keeler, Domoto, and Federspiel do not explicitly teach the one or more processors provide a UI including the received temperature history information of the first preheating chamber, the history information of the input fuel, and the obtained second predicted temperature information.
However, Merk teaches the one or more processors provide a UI including the received temperature history information of the first preheating chamber, the history information of the input fuel, and the obtained second predicted temperature information (Par. [0014], “the artificial intelligence model is trained on historical data from a historian of a kiln control system, wherein the data include at least ten, in particular all, of the following sensor signals: …, kiln inlet temperature, calciner head temperature, main burner coal feed, main burner refuse-derived-fuel feed, calciner refuse-derived-fuel feed”; Par. [0021], “a user interface for displaying a forecast of a variable, wherein the variable depends on the kiln process.”; Fig. 3, Par. [0073], “Displayed is a rotary kiln AI prediction screen shot 27 … Kiln inlet temperature 32, Sintering zone temperature 33, … Calciner outlet after temperature 39, … 15 minutes value 47, 30 minutes value 48 … On the left side of actual values 63 the past is displayed and on the right of the actual values 63 the forecast is shown” – In the modified system, Merk’s displayed historical process data would include Chen’s decomposition furnace temperature history and coal feed and waste flow history, and Merk’s displayed forecast would include the second predicted decomposition furnace temperature information.).
Chen, Keeler, Domoto, Federspiel, and Merk are analogous art because they contain functional similarities. They all relate to monitoring or controlling an industrial thermal process using measured process information and predicted temperature information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, Keeler, Domoto, and Federspiel, and incorporate a user interface that presents historical temperature and fuel related process information together with predicted temperature information, as taught by Merk.
One of ordinary skill in the art would have been motivated to display the forecast information to an operator to build trust in the reliability of the system and allow the operator to make the final decision, as suggested by Merk (Par. [0053]).
Regarding claim 12, the combination of Chen, Keeler, Domoto, and Federspiel teaches all the limitations of the base claims as outlined above.
Chen further teaches wherein the preprocessed process information includes temperature history information of the first preheating chamber and history information of the input fuel (Par. [n0009], “Real-time acquisition of field data of the decomposer under waste coprocessing {T(k), Fc(k), FR(k)|k=1,…,N}”; Par. [n0010], “Use the moving average filtering method to preprocess the field data” – the sequence T(k) corresponds to the temperature history information of the decomposition furnace, and the sequences Fc(k) and FR(k) correspond to the history information of the coal and waste fuels included in the preprocessed process information.).
Chen, Keeler, Domoto, and Federspiel do not explicitly teach the providing of the UI includes providing a UI including the received temperature history information of the first preheating chamber, the history information of the input fuel, and the obtained second predicted temperature information.
However, Merk teaches the providing of the UI includes providing a UI including the received temperature history information of the first preheating chamber, the history information of the input fuel, and the obtained second predicted temperature information (Par. [0014], “the artificial intelligence model is trained on historical data from a historian of a kiln control system, wherein the data include at least ten, in particular all, of the following sensor signals: …, kiln inlet temperature, calciner head temperature, main burner coal feed, main burner refuse-derived-fuel feed, calciner refuse-derived-fuel feed”; Par. [0021], “a user interface for displaying a forecast of a variable, wherein the variable depends on the kiln process.”; Fig. 3, Par. [0073], “Displayed is a rotary kiln AI prediction screen shot 27 … Kiln inlet temperature 32, Sintering zone temperature 33, … Calciner outlet after temperature 39, … 15 minutes value 47, 30 minutes value 48 … On the left side of actual values 63 the past is displayed and on the right of the actual values 63 the forecast is shown” – In the modified system, Merk’s displayed historical process data would include Chen’s decomposition furnace temperature history and coal feed and waste flow history, and Merk’s displayed forecast would include the second predicted decomposition furnace temperature information.).
Chen, Keeler, Domoto, Federspiel, and Merk are analogous art because they contain functional similarities. They all relate to monitoring or controlling an industrial thermal process using measured process information and predicted temperature information.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above decomposition furnace temperature prediction and control system, as taught by Chen, Keeler, Domoto, and Federspiel, and incorporate a user interface that presents historical temperature and fuel related process information together with predicted temperature information, as taught by Merk.
One of ordinary skill in the art would have been motivated to display the forecast information to an operator to build trust in the reliability of the system and allow the operator to make the final decision, as suggested by Merk (Par. [0053]).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ko et al. [US 11,568,310 B2] teaches an apparatus for generating a temperature prediction model.
Schockaert et al. [USPGPUB 2023/0359155 A1] teaches a computer system, computer-implemented method and computer program product for training a reinforcement learning model to provide operating instructions for thermal control of a blast furnace.
Conclusion
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/PETER XU/ Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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