DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 09/07/2023 and 01/27/2025 have been considered by the examiner.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 2, line 7, replace “data;” with “data; and”
Claim 4, line 2, replace “for training” with “for the training”
Claim 6, line 4, replace “receiving geological data of a material and processing data referring to a plurality” with “receiving the geological data of the material and the processing data referring to the plurality”
Claim 6, line 5, replace “an industrial” with “the industrial”, and replace “a product” with “the product”
Claim 6, line 9, replace “a prediction” with “the prediction”
Claim 6, line 10, replace “predicted” with “the predicted”
Claim 14, line 4, replace “processing data, and” with either “processing data,” or “processing data and”
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) 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.
Claims 10-11 and 14-15 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 feature “the respective product quality data”. There is insufficient antecedent basis for this limitation in the claim. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the feature will be interpreted as “a product quality data”.
Claim 11 recites the feature “the respective product quality data”. There is insufficient antecedent basis for this limitation in the claim. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the feature will be interpreted as “a product quality data”.
In addition, claim 11 recites the limitation “a quality indicator such as …”. It is unclear what Applicant means by “such as”. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the limitation will be interpreted as “a quality indication for …”.
Claim 14 recites the feature “typically further comprising”. It is unclear what Applicant means by “typically”. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the limitation will be interpreted as “further comprising”.
Claim 15 is a dependent claim of claim 14. The claim 14 is rejected under 35 U.S.C. 112(b), and therefore, claim 15 is rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-13, 16-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by RUNKANA et al. (US 2020/0132882 A1) (“Runkana”). Runkana is a reference cited in the information disclosure statement submitted on 01/27/2025.
Regarding independent claim 1, Runkana teaches:
A computer-implemented method comprising: (Runkana: [0025] “FIG. 1A and FIG. 1B illustrate an exemplary block diagram of a system 100 for online monitoring and optimization of mining and mineral processing operations, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 may be one or more software processing modules and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the device 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.”)
receiving geological data of a material and processing data referring to a plurality of processing stations of an industrial process for manufacturing a product from the material; receiving, for the geological data and the processing data, corresponding product quality data of the manufactured product; and (Runkana: [0029] “At step 202 of the method, the sensor management module 110 is configured to communicate and fetch data for a set of variables, corresponding to a set of sensors from the plurality of data sources 116. This fetched data provides wide variety of data associated with and required for digital monitoring and optimization of the mining and mineral processing operations. The set of sensors are associated with and provide data for equipment and instrumentation installed for the mining operations (that includes hauling and stockpiling operation) required for mining operations or equipment and instrumentation installed for comminution circuits or the flotation and concentration circuits of mineral processing operations. The sensors are also associated with quality parameters of a plurality of streams of the plant comprising raw material, intermediate and final product. Further, the sensors also comprise data corresponding to source and quantity of different raw material processed and data corresponding to equipment breakdown and maintenance history, environmental and weather conditions, and rock and terrain properties.”) (Runkana: [0030] “The fetched data includes data of different nature such as geological, material, fleet management, LIMS (Laboratory information management system) data, manufacturing execution system data as well as drill and blast design data generated from the various tools such as Vulcan, Geovia, JKsimBlast, and the like. Further, the data sources 116 may include material database, fleet management database, plant process data historian, manufacturing execution systems data, manufacturing operations management data, ERP (Enterprise Resource Planning), DCS (Digital Control System) and the like.”) [Fetching reads on “receiving”. The fetched data associated with the mining and mineral processing operations reads on “processing data”. The fetched data associated with the hauling, stockpiling, flotation and concentration operations reads on “… a plurality of processing stations of an industrial process for manufacturing a product from the material”.]
training or retraining a prediction model for the industrial process to determine predicted product quality data for the geological data and the processing data. (Runkana: [0023] “Embodiments of the present disclosure provide systems and methods for online monitoring and optimization of mining and mineral processing operations. The system disclosed provides an integrated approach utilizing short-term planning data and models of fragmentation, crushing, screening, grinding and flotation to arrive at optimized blast design. The method and system disclosed minimizes cost of production, maximizes yield of desired particle size to be used in downstream units to arrive at optimized grade and recovery of desired mineral. Further, the system provides simulation models to predict the key outputs of individual unit operations such as average particle size in the cyclone overflow, grade and recovery of metals in by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations.”) (Runkana: [0046] “At step 514, KPIs of interest of mineral processing operations namely comminution and flotation and concentration are estimated. Crusher model can predict the power requirements and product size distribution based on estimated fragment size distribution. Models proposed in the literature such as Whiten crusher model and Csoke model or else machine learning model developed using the data from data sources 116 can be used. The screening model further predicts the size distribution of the lumps and fines as well as the efficiency of screening operations. Models developed in the literature previously such as Karra's model, and Hatch and Mular model can be calibrated and used in the application. Grinding model will predict power requirements to break down the screened ore or the bigger particles collected in the cyclone underflow to size required for further processing in the concentrator, as well as the final product particle size distribution. Mineral recovery and grade models predict the recoveries and grades respectively of the mineral of interest in concentrates or tails in the different flotation equipment used in the mineral processing operations.”) (Runkana: [0074] “Either online or historical data corresponding to the variables used for building models such as pH of flotation cell, solids throughputs through comminution circuit, collector flow rate stored in sensor management module 110 is used to train ML models for mineral grade and recovery in Modeling and Simulation module 112.”) [The ML model that is trained to predict mineral grade by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations reads on “training or retraining a prediction model … to determine predicted product quality data …”.]
Regarding claim 2, Runkana teaches all the claimed features of claim 1. Runkana further teaches:
wherein training or retraining comprises at least one of: using the geological data and the processing data as input of the prediction model to determine intermediate predicted product quality data; comparing the intermediate predicted product quality data with the product quality data; using the intermediate predicted product quality data and the product quality data for changing at least one parameter of the prediction model. (Runkana: [0046] as discussed in claim 1) [The crusher model predicting the size distribution reads on “the prediction model to determine intermediate predicted product quality data”.]
Regarding claim 3, Runkana teaches all the claimed features of claim 1. Runkana further teaches:
at least one of: validating the trained or retrained prediction model; and testing the trained or retrained prediction model. (Runkana: [0077] FIG. 9A and FIG. 9B illustrate predictive performance of a soft sensor of a particle size and metal grade, in accordance with an embodiment of the present disclosure. FIGS. 9A and 9B compare the predictions made by ML models of P80 from comminution circuit (80% passing particle size) and rougher grade of mineral A 112 with the actual plant data. Reasonable level of accuracy is obtained by these models considering only decision and disturbance variable as features.”) [Comparing the predictions with the actual reads on “validating …” or “testing …”.]
Regarding independent claim 5, Runkana teaches:
A computer-implemented method comprising: (Runkana: [0025] “FIG. 1A and FIG. 1B illustrate an exemplary block diagram of a system 100 for online monitoring and optimization of mining and mineral processing operations, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 may be one or more software processing modules and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the device 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.”)
receiving geological data of a material and processing data referring to a plurality of processing stations of an industrial process for manufacturing a product from the material; and (Runkana: [0029] “At step 202 of the method, the sensor management module 110 is configured to communicate and fetch data for a set of variables, corresponding to a set of sensors from the plurality of data sources 116. This fetched data provides wide variety of data associated with and required for digital monitoring and optimization of the mining and mineral processing operations. The set of sensors are associated with and provide data for equipment and instrumentation installed for the mining operations (that includes hauling and stockpiling operation) required for mining operations or equipment and instrumentation installed for comminution circuits or the flotation and concentration circuits of mineral processing operations. The sensors are also associated with quality parameters of a plurality of streams of the plant comprising raw material, intermediate and final product. Further, the sensors also comprise data corresponding to source and quantity of different raw material processed and data corresponding to equipment breakdown and maintenance history, environmental and weather conditions, and rock and terrain properties.”) (Runkana: [0030] “The fetched data includes data of different nature such as geological, material, fleet management, LIMS (Laboratory information management system) data, manufacturing execution system data as well as drill and blast design data generated from the various tools such as Vulcan, Geovia, JKsimBlast, and the like. Further, the data sources 116 may include material database, fleet management database, plant process data historian, manufacturing execution systems data, manufacturing operations management data, ERP (Enterprise Resource Planning), DCS (Digital Control System) and the like.”) [Fetching reads on “receiving”. The fetched data associated with the mining and mineral processing operations reads on “processing data”. The fetched data associated with the hauling, stockpiling, flotation and concentration operations reads on “… a plurality of processing stations of an industrial process for manufacturing a product from the material”. The fetched sensor data associated with quality parameters of raw material, intermediate and final product reads on “… corresponding product quality data of the manufactured product”.]
using the geological data and the processing data as input of a trained prediction model to output predicted product quality data. (Runkana: [0023] “Embodiments of the present disclosure provide systems and methods for online monitoring and optimization of mining and mineral processing operations. The system disclosed provides an integrated approach utilizing short-term planning data and models of fragmentation, crushing, screening, grinding and flotation to arrive at optimized blast design. The method and system disclosed minimizes cost of production, maximizes yield of desired particle size to be used in downstream units to arrive at optimized grade and recovery of desired mineral. Further, the system provides simulation models to predict the key outputs of individual unit operations such as average particle size in the cyclone overflow, grade and recovery of metals in by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations.”) (Runkana: [0046] “At step 514, KPIs of interest of mineral processing operations namely comminution and flotation and concentration are estimated. Crusher model can predict the power requirements and product size distribution based on estimated fragment size distribution. Models proposed in the literature such as Whiten crusher model and Csoke model or else machine learning model developed using the data from data sources 116 can be used. The screening model further predicts the size distribution of the lumps and fines as well as the efficiency of screening operations. Models developed in the literature previously such as Karra's model, and Hatch and Mular model can be calibrated and used in the application. Grinding model will predict power requirements to break down the screened ore or the bigger particles collected in the cyclone underflow to size required for further processing in the concentrator, as well as the final product particle size distribution. Mineral recovery and grade models predict the recoveries and grades respectively of the mineral of interest in concentrates or tails in the different flotation equipment used in the mineral processing operations.”) (Runkana: [0074] “Either online or historical data corresponding to the variables used for building models such as pH of flotation cell, solids throughputs through comminution circuit, collector flow rate stored in sensor management module 110 is used to train ML models for mineral grade and recovery in Modeling and Simulation module 112.”) [The ML model that is trained to predict mineral grade by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations reads on “… a trained prediction model to output predicted product quality data …”.]
Regarding claim 6, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the trained prediction model is obtained by a computer-implemented method comprising: (Runkana: [0023] “Embodiments of the present disclosure provide systems and methods for online monitoring and optimization of mining and mineral processing operations. The system disclosed provides an integrated approach utilizing short-term planning data and models of fragmentation, crushing, screening, grinding and flotation to arrive at optimized blast design. The method and system disclosed minimizes cost of production, maximizes yield of desired particle size to be used in downstream units to arrive at optimized grade and recovery of desired mineral. Further, the system provides simulation models to predict the key outputs of individual unit operations such as average particle size in the cyclone overflow, grade and recovery of metals in by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations.”) (Runkana: [0046] “At step 514, KPIs of interest of mineral processing operations namely comminution and flotation and concentration are estimated. Crusher model can predict the power requirements and product size distribution based on estimated fragment size distribution. Models proposed in the literature such as Whiten crusher model and Csoke model or else machine learning model developed using the data from data sources 116 can be used. The screening model further predicts the size distribution of the lumps and fines as well as the efficiency of screening operations. Models developed in the literature previously such as Karra's model, and Hatch and Mular model can be calibrated and used in the application. Grinding model will predict power requirements to break down the screened ore or the bigger particles collected in the cyclone underflow to size required for further processing in the concentrator, as well as the final product particle size distribution. Mineral recovery and grade models predict the recoveries and grades respectively of the mineral of interest in concentrates or tails in the different flotation equipment used in the mineral processing operations.”) (Runkana: [0074] “Either online or historical data corresponding to the variables used for building models such as pH of flotation cell, solids throughputs through comminution circuit, collector flow rate stored in sensor management module 110 is used to train ML models for mineral grade and recovery in Modeling and Simulation module 112.”) [The ML model that is trained to predict mineral grade by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations reads on “training or retraining a prediction model … to determine predicted product quality data …”.]
receiving geological data of a material and processing data referring to a plurality of processing stations of an industrial process for manufacturing a product from the material; receiving, for the geological data and the processing data, corresponding product quality data of the manufactured product; and (Runkana: [0029] “At step 202 of the method, the sensor management module 110 is configured to communicate and fetch data for a set of variables, corresponding to a set of sensors from the plurality of data sources 116. This fetched data provides wide variety of data associated with and required for digital monitoring and optimization of the mining and mineral processing operations. The set of sensors are associated with and provide data for equipment and instrumentation installed for the mining operations (that includes hauling and stockpiling operation) required for mining operations or equipment and instrumentation installed for comminution circuits or the flotation and concentration circuits of mineral processing operations. The sensors are also associated with quality parameters of a plurality of streams of the plant comprising raw material, intermediate and final product. Further, the sensors also comprise data corresponding to source and quantity of different raw material processed and data corresponding to equipment breakdown and maintenance history, environmental and weather conditions, and rock and terrain properties.”) (Runkana: [0030] “The fetched data includes data of different nature such as geological, material, fleet management, LIMS (Laboratory information management system) data, manufacturing execution system data as well as drill and blast design data generated from the various tools such as Vulcan, Geovia, JKsimBlast, and the like. Further, the data sources 116 may include material database, fleet management database, plant process data historian, manufacturing execution systems data, manufacturing operations management data, ERP (Enterprise Resource Planning), DCS (Digital Control System) and the like.”) [Fetching reads on “receiving”. The fetched data associated with the mining and mineral processing operations reads on “processing data”. The fetched data associated with the hauling, stockpiling, flotation and concentration operations reads on “… a plurality of processing stations of an industrial process for manufacturing a product from the material”.]
training or retraining a prediction model for the industrial process to determine predicted product quality data for the geological data and the processing data. (Runkana: [0023] “Embodiments of the present disclosure provide systems and methods for online monitoring and optimization of mining and mineral processing operations. The system disclosed provides an integrated approach utilizing short-term planning data and models of fragmentation, crushing, screening, grinding and flotation to arrive at optimized blast design. The method and system disclosed minimizes cost of production, maximizes yield of desired particle size to be used in downstream units to arrive at optimized grade and recovery of desired mineral. Further, the system provides simulation models to predict the key outputs of individual unit operations such as average particle size in the cyclone overflow, grade and recovery of metals in by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations.”) (Runkana: [0046] “At step 514, KPIs of interest of mineral processing operations namely comminution and flotation and concentration are estimated. Crusher model can predict the power requirements and product size distribution based on estimated fragment size distribution. Models proposed in the literature such as Whiten crusher model and Csoke model or else machine learning model developed using the data from data sources 116 can be used. The screening model further predicts the size distribution of the lumps and fines as well as the efficiency of screening operations. Models developed in the literature previously such as Karra's model, and Hatch and Mular model can be calibrated and used in the application. Grinding model will predict power requirements to break down the screened ore or the bigger particles collected in the cyclone underflow to size required for further processing in the concentrator, as well as the final product particle size distribution. Mineral recovery and grade models predict the recoveries and grades respectively of the mineral of interest in concentrates or tails in the different flotation equipment used in the mineral processing operations.”) (Runkana: [0074] “Either online or historical data corresponding to the variables used for building models such as pH of flotation cell, solids throughputs through comminution circuit, collector flow rate stored in sensor management module 110 is used to train ML models for mineral grade and recovery in Modeling and Simulation module 112.”) [The ML model that is trained to predict mineral grade by utilizing both historical data and online data from a plurality of data sources associated with the mining and mineral processing operations reads on “training or retraining a prediction model … to determine predicted product quality data …”.]
Regarding claim 7, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the geological data comprise a respective source location of the material in a mine, (Runkana: [0042] “The steps above are further explained in conjunction with the example in FIG. 5. As depicted in FIG. 5, the user is directed to design drilling pattern and charge pattern using short-term mining plan data, rock properties, and geological data coming from the databases such as GBIS, ArcGIS.”) [The geological data for mining from the databases such as GBIS and ArcGIS reads on “… comprise a respective source location …”.]
wherein the material is a geological material, and/or wherein the material is an ore. (Runkana: [0029]-[0030] as discussed in claim 1) (Runkana: [0003] “…For example, take the case of comminution circuit operations that comprise processing of ore coming from stockpile through crusher and grinding mills that perform crushing, and grinding operations.”) [The raw material or the ore reads on “a geological material”.]
Regarding claim 8, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the industrial process is a mine process, and/or wherein processing data refer to and/or are obtained from at least one of the following processing stations: planning, blasting, hauling, storage, ore processing, and shipping. (Runkana: Abstract “Monitoring and analysis of plurality of operations in mining and mineral processing is critical to achieve optimized performance. Existing tools are specific to one or other individual operations and this individuality introduces limitations for end to end monitoring of entire mining to mineral processing operations. Method and system for online monitoring and optimization of mining and mineral processing operations providing an integrated approach utilizing short-term mining plan data, information generated using established drill and blast design software, simulation models of fragmentation, crushing, screening, grinding and flotation to arrive at an optimized charge plan and set points for controllers is disclosed. The proposed method and system improves key performance indicators such as cost of mining operations, specific energy consumption in comminution circuit, maximizes yield of desired particle size, and maximizes grade and recovery of mineral of interest while considering operational constraints.”) (Runkana: [0072] “Either online or historical data corresponding to the variables used for building models such as process water, feed rate to SAG mill, ore grade, cyclone cluster pressure stored in sensor management module 110 is used to train ML models of specific power consumption in comminution circuit, particle size distribution in cyclone overflow using Modeling and Simulation module 112. The models are used to frame a user customized optimization problem and is solved for in optimization module 114. The solution obtained as values of decision variables is pushed to sensor management module and through there to DCS/SCADA or to plant operators to be used as set points by lower level controllers.”)
Regarding claim 9, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the respective processing data comprise at least one of a processing point of a processing station, a parameter of a processing station, and a processing configuration of a processing station. (Runkana: [0029] “… The set of sensors are associated with and provide data for equipment and instrumentation installed for the mining operations (that includes hauling and stockpiling operation) required for mining operations or equipment and instrumentation installed for comminution circuits or the flotation and concentration circuits of mineral processing operations. …”)
Regarding claim 10, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the respective product quality data comprise and/or refer to an end quality of the product. (Runkana: [0029] “… The sensors are also associated with quality parameters of a plurality of streams of the plant comprising raw material, intermediate and final product. …”)
Regarding claim 11, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the respective product quality data comprise a quality indicator such as a product purity, an ore content, a lead time, an energy consumption per product unit and/or an ecological foot print per product unit such as a carbon dioxide production per product unit or a water consumption per unit. (Runkana: [0003] “… Online optimization of KPIs in response to changes sensed through monitoring of ore hardness and size of the ore fed which are disturbances affecting the KPIs will help improve the performance.”)
Regarding claim 12, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein the prediction model is based on machine learning, in particular regression and/or deep learning. (Runkana: [0033] “… ML techniques such as linear regression, random forest, support vector machines, neural networks and the like can be used to develop the ML models from the processed historical data taken from the sensor management module 110. …”)
Regarding claim 13, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
using the output predicted product quality data for determining a recommendation for changing the process for manufacturing the product and/or for changing a planning for manufacturing the product, in particular with respect to a planned mining location. (Runkana: [0046] “At step 514, KPIs of interest of mineral processing operations namely comminution and flotation and concentration are estimated. … Mineral recovery and grade models predict the recoveries and grades respectively of the mineral of interest in concentrates or tails in the different flotation equipment used in the mineral processing operations.”) (Runkana: [0049] “At step 520, charge plan and the set points of manipulated variables estimated at the previous step are sent to DCS/SCADA through sensor management module and/or hand held devices used by the operators to execute the recommendations in the field. …”)
Regarding claim 16, Runkana teaches all the claimed features of claim 5. Runkana further teaches:
wherein at least one of the output predicted product quality data the recommendation (R), and the reasoning is used for short-term production planning, mid-term production planning and/or long-term production planning. (Runkana: [0042] “The steps above are further explained in conjunction with the example in FIG. 5. As depicted in FIG. 5, the user is directed to design drilling pattern and charge pattern using short-term mining plan data, rock properties, and geological data coming from the databases such as GBIS, ArcGIS. The short term mining plan (502) is provided from data source 116. …”)
Regarding claim 17, Runkana teaches all the claimed features of claim 1. Runkana further teaches:
wherein each processing station is configured to dynamically provide processing data representing a state of the processing station, wherein the industrial process comprises a respective material flow between the processing stations, and/or wherein at least one of the geological data and the processing data are provided by a monitoring method of the industrial process for manufacturing the product, the monitoring method including at least one of: providing, for each processing station, a processing station layout of the processing station, wherein the processing station layout includes: a representation of a physical layout of the processing station, and a representation of material flow-paths to and from the processing station, wherein the processing station layout is configured for enabling a mapping of the material flow to and from the processing station; providing, for each processing station, an interface model of the processing station, wherein the interface model includes: a representation of data input ports and data output ports of the processing station, wherein the interface model is configured for enabling a mapping of a data flow to the data input ports and from the data output ports of the processing station; generating an information metamodel from the processing station layout and the interface model of the processing stations, wherein the information metamodel is based on a markup language, in particular the international standard automation markup language and/or includes: a process layout model, the process layout model including the processing station layouts of the processing stations, and a process interface model, the process interface model including the interface models of the processing stations, generating an adaptive simulation model of the industrial process by importing the data representing the state of the processing station provided by the of processing stations into the adaptive simulation model via the information metamodel; storing the imported data; and outputting respective processing data, in particular in a predefined format suitable as input of the prediction model. (Runkana: [0029] as discussed in claim 1) (Runkana: Abstract “Monitoring and analysis of plurality of operations in mining and mineral processing is critical to achieve optimized performance. Existing tools are specific to one or other individual operations and this individuality introduces limitations for end to end monitoring of entire mining to mineral processing operations.”) (Runkana: [0036] “… The system 100 provides a digital replica of entire chain of operations involved in mining and mineral processing namely drilling, blasting, hauling, crushing, screening, grinding, flotation, and thickening. The current running status of each of the units is updated using data received from disparate sources distributed across the plant. The current running status of a unit consists of the present measured value of the sensors employed in the field, soft sensors developed using simulation models developed using sensor data, lab measurements and other such measurements fetched from the updated plant databases. Data fetched from these sources is used to either tune or develop models for section-wise KPIs and overall KPIs as function of the several manipulated/decision variables and important disturbance variables. These models are utilized to find the set points of decision variables that result in the improvement in the KPIs. The estimated decision variables are communicated to the lower level controllers as set points (in case of automatic control) or to hand-held devices with the operators (in case of manual control) so that these can be implemented in the field. Thus the system 100 provides end to end solution to improve existing mining and mineral processing operations. …”) [The sensor management module providing data related to each equipment or instrumentation for the mining operations, drilling-blasting, comminution, flotation and concentration reads on “each processing station is configured to dynamically provide processing data representing a state …”. End to end monitoring of entire mining to mineral processing operations read on “respective material flow between the processing stations”.]
Regarding independent claim 20:
The claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Runkana, in view of Moore (Eavan Moore, How to simulate an entire operation before production, October 16, 2018) (“Moore”). Moore is a reference cited in the information disclosure statement submitted on 09/07/2023.
Regarding claim 4, Runkana teaches all the claimed features of claim 1. Runkana further teaches:
wherein a plurality of corresponding geological data, processing data and quality data are used for training or retraining the prediction model, wherein the training or retraining is performed iteratively and/or at least once. (Runkana: [0023], [0029]-[0030], [0046] and [0074] as discussed in claim 1)
Runkana does not expressly teach: wherein the geological data are obtained from a 3d mining model and/or are at least in part based on exploration.
Moore teaches:
wherein the geological data are obtained from a 3d mining model and/or are at least in part based on exploration. (Moore: Page 3, Section A “Over the past few years, Petra has developed machine learning models for simulating how geology affects a processing plant. The digital twin integrates 3D geological data with time series processing data. Each new accumulation of data is fed back into the model, building its depth and breadth over time.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Runkana and Moore before them, to modify the fetching of the geological data for the machine learning models in mining and mineral processing, to incorporate acquiring 3D geological data.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for simulation of how geology affects a processing plant. (Moore: Page 3, Section A)
Regarding claim 18, Runkana teaches all the claimed features of claims 1 and 17. Runkana does not expressly teach the recitation of claim 18.
Moore teaches:
wherein the information metamodel and the adaptive simulation model are comprised in a digital twin of the industrial process. (Moore: Page 1 and Page 2 first five lines “Since then, the term has found its way into manufacturing and, eventually, into mining. Sohail Nazari, business development manager at Andritz Automation, explained that there is no official definition of digital twins for process industries. “However, there are three important aspects that are common throughout various descriptions,” he said. “When simulation is a core functionality of the process, along the entire life cycle of the plant, with direct linkage to operation, that process has a digital twin.” Some of the data that might be used to model a process plant during the design phase, for example, include the pump specs, plant elevation, the length of the pipes in the plant, and mineral characteristics sourced from a licensed database. How these variables will affect operations is predicted by models that rely on first principles of physics and chemistry. Project consultant William Thomas, who has used the technology in project design for Hudbay Minerals and other clients, said an iterative process adds more detail from the initial project assessment through the detailed design. Once the plant is running, operating data is fed back into the model. Some mines that have been operating for long enough to have years of historical data – ranging from weather conditions to drill and blast settings – can use that to build a simulation using machine learning.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Runkana and Moore before them, to modify the digital replica of entire chain of operations involved in mining and mineral processing namely drilling, blasting, hauling, crushing, screening, grinding, flotation, and thickening, to incorporate use of the digital twin.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for linkage of the process operations along the entire life cycle of the chain of operations involved in mining and mineral processing. (Moore: Page 1 and Page 2 first five lines)
Regarding claim 19, Runkana and Moore teach all the claimed features of claims 1 and 17-18. Moore further teaches:
providing feedback to the digital twin. (Moore: Page 3, Section A “Over the past few years, Petra has developed machine learning models for simulating how geology affects a processing plant. The digital twin integrates 3D geological data with time series processing data. Each new accumulation of data is fed back into the model, building its depth and breadth over time.”) [The data being fed back into the model reads on “providing feedback …”.]
The motivation to combine Runkana and Moore as described in claim 18 is incorporated herein.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Runkana, in view of Zillner (US 2022/0172146 A1) (“Zillner”).
Regarding claim 14, Runkana teaches all the claimed features of claims 5 and 13. Runkana further teaches:
wherein determining the recommendation comprises using an … AI method, typically further comprising at least one of: using corresponding geological data processing data, and output predicted product quality data, and a characterizing parameter set of the trained prediction model as input of the … AI method. (Runkana: [0048] “At step 518, the formulated optimization problem is solved using optimization algorithms which are mostly metaheuristics such as particle swarm optimization, genetic algorithm, Tabu-search, simulated annealing, and the like. The optimization system runs in an iterative mode to determine charge plan along with the set points of important manipulated variables of the mining and mineral processing operations such as process water addition to the grinding mills, rpm of the ball mill, cyclone cluster pressure, pH of the flotation equipment, collector and frother addition rate to the flotation equipment.”) (Runkana: [0049] “At step 520, charge plan and the set points of manipulated variables estimated at the previous step are sent to DCS/SCADA through sensor management module and/or hand held devices used by the operators to execute the recommendations in the field. …”)
Runkana does not expressly teach: the AI … method is an explainable AI method; and providing a reasoning for the recommendation.
Zillner teaches:
AI … method is an explainable AI method; and providing a reasoning for the recommendation. (Zillner: Abstract “Provided is a system for feedback-improved automatic solving of production facility related tasks, including: an input interface being adapted to receive production facility related data; a semantic data enhancement module being adapted to generate semantically enhanced data based on the production facility related data; a semantic-based reasoning module being adapted to automatically provide an explainable artificial intelligence model, using the semantically enhanced data, wherein the artificial intelligence model relates to a predetermined production facility related task; a user interaction interface being adapted to output explanation data regarding the explainable artificial intelligence model to a user; and a feedback module adapted to receive feedback from the user in response to the outputted explanation data and adapted to adjust the semantic-based reasoning module and/or the user interaction interface based on the received feedback.”) (Zillner: [0040] “:By assessing the impact or relevance of the explanation data, improvement can be achieved as to which information is to be presented to the user. The improvement may consist in omitting unnecessary information which the user does not need to understand or implement the solution. The improvement may additionally or alternatively consist in providing additional data which may be helpful for the user to understand the solution of the production facility related task.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Runkana and Zillner before them, to modify the machine learning models for the mining and mineral processing operations, to incorporate use of the explainable AI methods.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for having the semantically enhanced data that can help users understand the solutions obtained from the machine learning even without detailed understanding of the underlying machine learning or AI concepts.. (Zillner: [0024])
Regarding claim 15, Runkana and Zillner teach all the claimed features of claims 5 and 13-14. Zillner further teaches:
wherein providing the reasoning comprises at least one of feature attribution, visualization, natural language processing and textual justification. (Zillner: [0029] “In some advantageous embodiments of the system, the semantic data enhancement module is adapted to generate the semantically enhanced data by semantically labeling the production facility related data using knowledge graphs and/or domain ontologies and/or context models.”) (Zillner: [0037] “In some advantageous embodiments of the system, the user interaction interface is adapted to provide the explanation data using natural language. Therefore, human decision-making process is augmented by providing explanations on the right level of understanding while continuously learning from users' interaction and feedback.”)
The motivation to combine Runkana and Zillner as described in claim 14 is incorporated herein.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/MICHAEL W CHOI/Primary Examiner, Art Unit 2116