CTFR 18/549,428 CTFR 93092 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-24 are pending. Response to Amendment Applicant’s amendments to the claims have overcome each and every objections previously set forth. The objections of the claims have been withdrawn. Applicant’s amendments to the claims 10-11 have not overcome the 112(b) rejections previously set forth. The 112(b) rejections of the claims 10-11 have been maintained. See Claim Rejections - 35 USC § 112 section below. Applicant’s amendments to the claims 14-15 have overcome each and every 112(b) rejections previously set forth. The 112(b) rejections of the claims 14-15 have been withdrawn. Response to Arguments 07-38 Applicant’s arguments with respect to the 102 rejections of the claims have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection. 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. 07-34-01 Claims 10-11 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 product quality data”. Claim 5, which claim 10 depends on, recites the feature “predicted product quality data” and “the output predicted product quality data”. There is insufficient antecedent basis for this feature in the claim. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the feature will be interpreted as “the output predicted product quality data”. Claim 11 recites the feature “the product quality data”. Claim 5, which claim 10 depends on, recites the feature “predicted product quality data” and “the output predicted 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 “the output predicted product quality data”. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-13, 17-18, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over RUNKANA et al. (US 2020/0132882 A1) (“Runkana”), in view of Brockhurst (US 2021/0318666 A1) (“Brockhurst”) . 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 …”.] Runkana does not expressly teach: wherein the geological data comprise a source location of the material used for manufacturing the product and refer to at least one of a physical property of the material at the source location, a chemical property of the material at the source location, and a composition of the material at the source location. Brockhurst teaches: wherein the geological data comprise a source location of the material used for manufacturing the product and refer to at least one of a physical property of the material at the source location, a chemical property of the material at the source location, and a composition of the material at the source location. (Brockhurst: Abstract “Industrial machines at a mine site or other worksite are monitored and controlled using a multi-phase implementation, based on data received from multiple data sources at different times. Machine telemetry data or other sensor data received from a first machine, in combination with additional sensor data from other machines at the site, detailed material attribute data received from external data sources, and/or outputs from trained machine-learned models, are used to determine characteristics of a material load and/or material blend, and to control machines at the mine site. A control system performs a multi-phase implementation for monitoring machines and/or load data in response to data received at different times, updating the material load and blend characteristics, and controlling machines at the site based on the characteristics of the material load or material blend.”) (Brockhurst: [0041] “Additionally or alternatively, the control system 110 receives data from external data sources 116, in addition to the sensor data received from machine(s) 102-108. For instance, an external data source 116 provides more detailed material attribute data for the environment 100. In an example, the control system 110 receives an updated material attribute information which is specific to a location (e.g., a particular location within a mining block) from which materials have been extracted by a machine 102-108 and is more detailed with respect to the composition of materials, impurities, etc. at the location . In some examples, the updated material attribute information includes more detailed material grade data collected during exploratory drilling. During exploratory drilling, a pattern of holes are drilled across the mining block and an assay is performed on the material extracted from each drill hole. The data received from external data sources 116 includes information about desirable grades (e.g., gold, iron, silver, copper, etc.) and/or undesirable grades (e.g., impurities). In some examples, the data also includes material modifier data and/or other characteristics of the material (e.g., hard, soft, wet, dry, clay, sand, etc.) addition to information derived from exploratory testing, the data from external data sources 116 also includes weight measurements received from weigh stations that have weighed trucks 104, wheel loaders 108, or other machines prior to dumping materials into a crusher 106. In such examples, the data from the external data source 116 (e.g., the weigh station) provides additional payload measurements for trucks 104, wheel loaders 108, and other hauling machines. In some cases, data from external data sources 116 modifies or replaces the material data and/or mining block data stored by the control system 110. For instance, control system 110 may maintain a block model mapping the locations of materials in a mining block . However, blasting at the mining block changes the material locations, and certain external data sources 116 include model systems to determine how and where materials move as a result of the blasting. In this example, the external data sources 116 provide improved identification of the material attributes and different locations of the mining block following the blasting . In still other examples, external data sources 116 include video systems used to inspect the beds of trucks 104 or other machines and estimate the amount of “carry back” material (e.g., material stuck to the floor of the truck bed that was not dumped into the crusher 106) Using the additional material grade data, the control system 110 determines and/or updates the material characteristics of the load and/or blend extracted by the machines 102-108. For instance, the control system 110 performs a first determination of a material load or blend grade based on the machine sensor data (e.g., mining block locations, amounts of materials extracted from each location, etc. and then a second updated determination for the material load or blend grade based on the additional detailed material grade data received from the external data source 116.”) [The material mining block location, as well as, the mapped material locations of the mining block read on “a source location of the material”. The updated material attribute information which is specific to a location reads on “at least one of a physical property of the material at the source location, a chemical property of the material at the source location, and a composition of the material at the source location”.] 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 Brockhurst before them, to modify the geological data for the machine learning models in mining and mineral processing, to incorporate source location data and physical property data of the material at the source location. 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 achieving a target blend material when the attributes and quality of the mined source materials differ at various locations across the mine site. (Brockhurst: [0003]) Regarding claim 2, Runkana and Brockhurst teach 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; and 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 and Brockhurst teach 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 claim 4, Runkana and Brockhurst teach 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. Brockhurst further teaches: wherein the geological data are obtained from a 3d mining model and/or are at least in part based on exploration. (Brockhurst: [0041] “Additionally or alternatively, the control system 110 receives data from external data sources 116, in addition to the sensor data received from machine(s) 102-108. For instance, an external data source 116 provides more detailed material attribute data for the environment 100. In an example, the control system 110 receives an updated material attribute information which is specific to a location (e.g., a particular location within a mining block) from which materials have been extracted by a machine 102-108 and is more detailed with respect to the composition of materials, impurities, etc. at the location. In some examples, the updated material attribute information includes more detailed material grade data collected during exploratory drilling . During exploratory drilling, a pattern of holes are drilled across the mining block and an assay is performed on the material extracted from each drill hole. The data received from external data sources 116 includes information about desirable grades (e.g., gold, iron, silver, copper, etc.) and/or undesirable grades (e.g., impurities). In some examples, the data also includes material modifier data and/or other characteristics of the material (e.g., hard, soft, wet, dry, clay, sand, etc.) addition to information derived from exploratory testing, the data from external data sources 116 also includes weight measurements received from weigh stations that have weighed trucks 104, wheel loaders 108, or other machines prior to dumping materials into a crusher 106.”) The motivation to combine Runkana and Brockhurst as described in claim 1 is incorporated herein. 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 …”.] Runkana does not expressly teach: using the output predicted product quality data for at least one of: mid-term production planning for manufacturing the product, long-term production planning for manufacturing the product, and changing at least one of: a planning for manufacturing the product with respect to a planned mining location, where to mine next week, where to mine next month, where to mine next year, and how to exploit a specific area of a source location of the material in a mine . Brockhurst teaches: using the output predicted product quality data for at least one of: mid-term production planning for manufacturing the product, long-term production planning for manufacturing the product, and changing at least one of: a planning for manufacturing the product with respect to a planned mining location, where to mine next week, where to mine next month, where to mine next year, and how to exploit a specific area of a source location of the material in a mine . (Brockhurst: Abstract “Industrial machines at a mine site or other worksite are monitored and controlled using a multi-phase implementation, based on data received from multiple data sources at different times. Machine telemetry data or other sensor data received from a first machine, in combination with additional sensor data from other machines at the site, detailed material attribute data received from external data sources, and/or outputs from trained machine-learned models, are used to determine characteristics of a material load and/or material blend, and to control machines at the mine site . A control system performs a multi-phase implementation for monitoring machines and/or load data in response to data received at different times, updating the material load and blend characteristics, and controlling machines at the site based on the characteristics of the material load or material blend .”) (Brockhurst: [0062] “The data model(s) are trained utilizing training data including telemetry data and other sensor data, operating parameters associated with a machine, production data and associated times, and the like. In such examples, the prediction component 428 and/or training component 430 data model(s) are configured to receive as input sensor data associated with a machine 102-108 (e.g., or a location data, a material load, etc.) and output data regarding the characteristics of the material and/or recommendation(s) for controlling the machines 102-108 to manage the material load or a material blend in the environment 100 .”) (Brockhurst: [0064] In various examples, the prediction component 428 uses one or more trained models to surface predictions and/or recommendations regarding material characteristics and machine controls. In some examples, recommendations for controlling machines 102-108 are presented to an operator of the machine 102-108 via a user interface 414. For instance, the user interface 414 of the machine 102-108 surfaces a recommendation provided by the control system 110 (e.g., a specific dig location ), providing a means by which an operator of the machine 102-108 reviews and follows or does not follow the operating recommendation. In such examples, the functioning of the machines 102-108 may be improved, such as by reducing an amount of input that an operator must process via the user interface 414. In some examples, the machine 102-108 transmits a message back to the control system 110 reporting the operation of the machine 102-108 after receiving and surfacing the recommendations for the operator, to provide a report with data indicating whether the operation of the machine was consistent with the operational recommendations.”) (Brockhurst: [0066] “In some examples, the operating instructions (or operational control instructions) provided by the control system 110 to the machine 102-108 include an assignment, dispatch location, material extraction parameters (e.g., mining block, location, tools, extraction depth, etc.) and/or recommendation of a path for the machine 102-108 to follow in the environment 100. ”) 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 Brockhurst before them, to modify the machine learning models for mining and mineral processing, to incorporate a recommendation of operations regarding the assignment, dispatch location, material extraction parameters (e.g., mining block, location, tools, extraction depth, etc.). 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 improving efficiency of machine assignments for the mining and mineral processing. (Brockhurst: [0099]-[0100]) Regarding claim 6, Runkana and Brockhurst teach 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 the geological data of the material and the processing data referring to the plurality of processing stations of the industrial process for manufacturing the 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 the prediction model for the industrial process to determine the 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 and Brockhurst teach all the claimed features of claim 5. Brockhurst further teaches: wherein the geological data comprise a respective source location of the material in a mine and refer to at least one of a physical property of the material, a chemical property of the material and a composition of the material, wherein the material is a geological material, and/or wherein the material is an ore. (Brockhurst: [0041] “Additionally or alternatively, the control system 110 receives data from external data sources 116, in addition to the sensor data received from machine(s) 102-108. For instance, an external data source 116 provides more detailed material attribute data for the environment 100. In an example, the control system 110 receives an updated material attribute information which is specific to a location (e.g., a particular location within a mining block) from which materials have been extracted by a machine 102-108 and is more detailed with respect to the composition of materials, impurities, etc. at the location . In some examples, the updated material attribute information includes more detailed material grade data collected during exploratory drilling. During exploratory drilling, a pattern of holes are drilled across the mining block and an assay is performed on the material extracted from each drill hole. The data received from external data sources 116 includes information about desirable grades (e.g., gold, iron, silver, copper, etc.) and/or undesirable grades (e.g., impurities). In some examples, the data also includes material modifier data and/or other characteristics of the material (e.g., hard, soft, wet, dry, clay, sand, etc.) addition to information derived from exploratory testing, the data from external data sources 116 also includes weight measurements received from weigh stations that have weighed trucks 104, wheel loaders 108, or other machines prior to dumping materials into a crusher 106. In such examples, the data from the external data source 116 (e.g., the weigh station) provides additional payload measurements for trucks 104, wheel loaders 108, and other hauling machines. In some cases, data from external data sources 116 modifies or replaces the material data and/or mining block data stored by the control system 110. For instance, control system 110 may maintain a block model mapping the locations of materials in a mining block . However, blasting at the mining block changes the material locations, and certain external data sources 116 include model systems to determine how and where materials move as a result of the blasting. In this example, the external data sources 116 provide improved identification of the material attributes and different locations of the mining block following the blasting . In still other examples, external data sources 116 include video systems used to inspect the beds of trucks 104 or other machines and estimate the amount of “carry back” material (e.g., material stuck to the floor of the truck bed that was not dumped into the crusher 106) Using the additional material grade data, the control system 110 determines and/or updates the material characteristics of the load and/or blend extracted by the machines 102-108. For instance, the control system 110 performs a first determination of a material load or blend grade based on the machine sensor data (e.g., mining block locations, amounts of materials extracted from each location, etc. and then a second updated determination for the material load or blend grade based on the additional detailed material grade data received from the external data source 116.”) [The material mining block location, as well as, the mapped material locations of the mining block read on “a respective source location of the material in a mine”. The updated material attribute information which is specific to a location reads on “at least one of a physical property of the material, a chemical property of the material and a composition of the material”.] The motivation to combine Runkana and Brockhurst as described in claim 5 is incorporated herein. Regarding claim 8, Runkana and Brockhurst teach all the claimed features of claim 5. Runkana further teaches: wherein the industrial process is a mine process, and/or wherein the 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 and Brockhurst teach 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 and Brockhurst teach all the claimed features of claim 5. Runkana further teaches: wherein the 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 and Brockhurst teach all the claimed features of claim 5. Runkana further teaches: wherein the respective product quality data comprise a quality indicator including a product purity, an ore content, a lead time, an energy consumption per product unit and/or an ecological foot print per product unit including a carbon dioxide production per product unit or a water consumption per product 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 and Brockhurst teach all the claimed features of claim 5. Runkana further teaches: wherein the prediction model is based on at least one of machine learning, regression and 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 and Brockhurst teach 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. (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 17, Runkana and Brockhurst teach 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 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 claim 18, Runkana and Brockhurst all the claimed features of claims 1 and 17. Brockhurst further teaches: wherein the geological data and the processing data are provided by the monitoring method of the industrial process, wherein the information metamodel and the adaptive simulation model are comprised in a digital twin of the industrial process, or wherein the geological data are obtained from a 3d mining model and/or are at least in part based on exploration. (Brockhurst: [0041] “Additionally or alternatively, the control system 110 receives data from external data sources 116, in addition to the sensor data received from machine(s) 102-108. For instance, an external data source 116 provides more detailed material attribute data for the environment 100. In an example, the control system 110 receives an updated material attribute information which is specific to a location (e.g., a particular location within a mining block) from which materials have been extracted by a machine 102-108 and is more detailed with respect to the composition of materials, impurities, etc. at the location. In some examples, the updated material attribute information includes more detailed material grade data collected during exploratory drilling . During exploratory drilling, a pattern of holes are drilled across the mining block and an assay is performed on the material extracted from each drill hole. The data received from external data sources 116 includes information about desirable grades (e.g., gold, iron, silver, copper, etc.) and/or undesirable grades (e.g., impurities). In some examples, the data also includes material modifier data and/or other characteristics of the material (e.g., hard, soft, wet, dry, clay, sand, etc.) addition to information derived from exploratory testing, the data from external data sources 116 also includes weight measurements received from weigh stations that have weighed trucks 104, wheel loaders 108, or other machines prior to dumping materials into a crusher 106.”) The motivation to combine Runkana and Brockhurst as described in claim 1 is incorporated herein. Regarding independent claim 20: The claim recites similar limitations as corresponding claim 5 and is rejected using the same teachings and rationale. Regarding claim 22, Runkana and Brockhurst all the claimed features of claim 1. Brockhurst further teaches: wherein the geological data include a time stamp. (Brockhurst: [0044] “As shown in this example, the machine-learning engine 112 receives input data from different machines 102-108 at multiple times (e.g., t1 to t5), and outputs different predictions regarding the characteristics of material load(s) in response to the input data received at the different times. To illustrate, the machine-learning engine 112 receives input data at time t1, corresponding to first telemetry data captured by an excavator 102. The first telemetry data includes location data, timestamps, machine state data, and movement/activity data, for the excavator 102 during a time window prior to t1. …”) The motivation to combine Runkana and Brockhurst as described in claim 1 is incorporated herein . 07-21-aia AIA Claim s 14-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Runkana, in view of Brockhurst, further in view of Zillner (US 2022/0172146 A1) (“Zillner”) . Regarding claim 14, Runkana and Brockhurst teach all the claimed features of claims 5 and 13. Runkana further teaches: wherein determining the recommendation comprises using an … AI method, 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 and Brockhurst do 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, Brockhurst 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, Brockhurst and Zillner as described in claim 14 is incorporated herein. Regarding claim 16, Runkana, Brockhurst and Zillner teach all the claimed features of claims 5 and 13-14. Runkana further teaches: wherein at least one of the recommendation (R), and the reasoning is used for short-term production planning, the mid-term production planning and/or the 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. …”) The motivation to combine Runkana, Brockhurst and Zillner as described in claim 14 is incorporated herein. Regarding claim 19, Runkana and Brockhurst teach all the claimed features of claims 1 and 17. Runkana and Brockhurst do not expressly teach recitations of claim 19. Zillner teaches: providing feedback to the information metamodel. (Zillner: [0030] “The knowledge graphs, domain ontologies or context models may provide the corresponding labels for annotating the production facility related data and may provide the required background knowledge for enhancing the production facility related data by process, context and situational meta-labels. Knowledge graphs may be considered as representing a collection of interlinked descriptions of entities, the entities being real-world objects, events, situations or abstract concepts. Domain ontologies are modelling domain specific definitions of terms. Context models define the way context data is structured and maintained. The context is described in a formal way and simplifies the description of greater structures.”) (Zillner: [0042] “In some advantageous embodiments of the system, the semantic-based reasoning module is adapted to adjust the explainable artificial intelligence model by reinforcement learning, based on the feedback received by the feedback module from the user. Parameters of the explainable artificial intelligence model may be changed based on the feedback received by the feedback module from the user. The feedback module may also compute an error vector comparing the solution computed by the semantic-based reasoning module based on the explainable artificial intelligence model which solves the predetermined production facility related task with actual data describing the actual implementation in the production facility process. The data may be obtained from the user or may be automatically received by the input interface.”) 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, Brockhurst and Zillner before them, to modify the machine learning models for the mining and mineral processing operations, to incorporate use of the reinforcement learning. 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 providing feedback for improving the model. (Zillner: [0042]) Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 21 and 23-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 07-43-03 AIA As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHAEL W CHOI/Primary Examiner, Art Unit 2116 Application/Control Number: 18/549,428 Page 2 Art Unit: 2116 Application/Control Number: 18/549,428 Page 3 Art Unit: 2116 Application/Control Number: 18/549,428 Page 4 Art Unit: 2116 Application/Control Number: 18/549,428 Page 5 Art Unit: 2116 Application/Control Number: 18/549,428 Page 6 Art Unit: 2116 Application/Control Number: 18/549,428 Page 7 Art Unit: 2116 Application/Control Number: 18/549,428 Page 8 Art Unit: 2116 Application/Control Number: 18/549,428 Page 9 Art Unit: 2116 Application/Control Number: 18/549,428 Page 11 Art Unit: 2116 Application/Control Number: 18/549,428 Page 12 Art Unit: 2116 Application/Control Number: 18/549,428 Page 13 Art Unit: 2116 Application/Control Number: 18/549,428 Page 14 Art Unit: 2116 Application/Control Number: 18/549,428 Page 15 Art Unit: 2116 Application/Control Number: 18/549,428 Page 16 Art Unit: 2116 Application/Control Number: 18/549,428 Page 17 Art Unit: 2116 Application/Control Number: 18/549,428 Page 18 Art Unit: 2116 Application/Control Number: 18/549,428 Page 19 Art Unit: 2116 Application/Control Number: 18/549,428 Page 20 Art Unit: 2116 Application/Control Number: 18/549,428 Page 21 Art Unit: 2116 Application/Control Number: 18/549,428 Page 22 Art Unit: 2116 Application/Control Number: 18/549,428 Page 23 Art Unit: 2116 Application/Control Number: 18/549,428 Page 24 Art Unit: 2116 Application/Control Number: 18/549,428 Page 25 Art Unit: 2116 Application/Control Number: 18/549,428 Page 26 Art Unit: 2116 Application/Control Number: 18/549,428 Page 27 Art Unit: 2116 Application/Control Number: 18/549,428 Page 28 Art Unit: 2116 Application/Control Number: 18/549,428 Page 29 Art Unit: 2116 Application/Control Number: 18/549,428 Page 30 Art Unit: 2116 Application/Control Number: 18/549,428 Page 31 Art Unit: 2116 Application/Control Number: 18/549,428 Page 33 Art Unit: 2116 Application/Control Number: 18/549,428 Page 34 Art Unit: 2116 Application/Control Number: 18/549,428 Page 35 Art Unit: 2116 Application/Control Number: 18/549,428 Page 36 Art Unit: 2116
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