DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 25 February 2025. Currently, claims 1-24 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Eligible Subject Matter
The claims are considered eligible subject matter because the abstract is integrated into practical implementation by necessarily being rooted in technology and the deploying of the material loading systems and haul trucks.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Humphrey (US 2016/0342915 A1) in view of Hill et al. (2021/0174279 A1) (hereinafter Hill) in view of Torkoly (US 2019/0265684 A1)
Claim 1:
Humphrey, as shown, discloses the following limitations of claims 1, 5, 22-23:
A method of operating a material handling system to meet a defined production target, the material handling system including a plurality of deployable shovels, a plurality of deployable haul trucks, and a plurality of material processing systems, comprising/ method of deploying a plurality of shovels and a plurality of haul trucks to deliver sufficient excavated material to each of a plurality of material processing systems to meet defined production targets for each of the plurality of material processing systems (see para [0015], "The work site 100 comprises a plurality of shovels 108 and a plurality of processing sites 110, such as crushers. Vehicles, both autonomous and non-autonomous, transport material from the plurality of shovels 108 to the plurality of processing sites"), comprising:
generating at least one production constraint for each of the plurality of shovels and for each of the plurality of material processing systems (see para [0027], where the safety threshold is a constraint and see para [0041], where the threshold for tons is a constraint);
estimating an effect of entropy on a cycle time…based on historical cycle times to produce a future cycle time estimate for the at least one haul truck (see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100."/ [and for claim 5 specifically] estimating the effect of entropy on the cycle time ... based on historical cycle times when at least one haul truck is operating in a loaded state and when the at least one haul truck is operating in an empty state to produce a future cycle time estimate for the at least one haul truck when the at least one haul truck is operating in the loaded state and in the empty state(see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100." And see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110.");
estimating an effect of entropy on a material processing time associated with the material processing system…based on historical material processing times to produce a future material processing time estimate (see para [0030]-[0033], showing measuring shoveling and unloading which can be considered material processing based on historical data where the rate of the shovel performing slowly because mining a harder block can be considered entropy of the material processing time and see para [0071] showing estimated time impacted by processing site);
predicting whether a delay will occur during the operation of each of the plurality of shovels, haul trucks, and material processing systems based on historical delay data (see para [0062]-[0066], determining whether vehicle bunching will occur);
determining a number of shovels and a number of haul trucks required to meet the defined production target for each of the plurality of material processing systems based on the estimated production constraint, the future cycle time estimate, the future material processing time estimate, and the estimated duration of the predicted delay (see para [0022], showing demand rate for shovels and see para [0057], "computing system 300 determines the availability of the shovels 108 and the processing sites 110 to accept autonomous vehicles 108 or non-autonomous vehicles 110. For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110. For example, the processor 302 may calculate that the shovel 108 loads an autonomous vehicle 104 or a non-autonomous vehicle 106 every 1.5 minutes. The processor 302 may make a similar calculation regarding the unloading time of a processing site 110.”); and
deploying the determined number of shovels and the determined number of haul trucks (see para [0015], "To prevent the shovel 108 or processing site 110 from being underserved, the computing system 300 at the central site 102 may deploy one or more autonomous vehicles 104 to the underserved shovel 108 or processing site 110. Alternatively, if the computing system 300 determines that there are too many vehicles 104 and 106 within the work site 100, the computing system 300 may remove one or more autonomous vehicles 104 from the work site 100." and see para [0059], "Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved").
Humphrey, however, does not specifically disclose estimating a duration of the predicted delay by performing a stochastic simulation based on the historical delay data. In analogous art, Hill discloses the following limitations:
identifying specific shovel and material processing system combinations (see para [0191], "In some embodiments the estimator 110 utilises conditioning parameters in the form of performance or operational characteristics that describe an individual asset and/or an asset class, such as time taken to travel between locations or perform a particular task. This allows the estimator 110 to ‘condition’ a data query from the flow planner 106 or dispatcher 108 with respect to a particular conditioning parameter on particular subsets of the data, such as a model of haul truck, a specific vehicle (or combination of vehicles), operator, operational mode (manual vs. autonomous), current load, etc. depending on the data that is available. Accordingly, the estimator 110 is able to more precisely predict or measure the performance of a specific use-case, by estimating the performance based on similar historical situations." and see para [0186], "The estimator 110 uses the historical data and/or the current system operational data for a detailed statistical analysis of operation, such as the distribution of travel or operation times for a specific vehicle, associated with one or more specific locations, and at a specific time." );
for each identified specific shovel and material processing system combination (see para [0186], showing detailed analysis for specific vehicles, location, etc. and see para [0191])
estimating a duration of the predicted delay based on the historical delay data (see para [0358], "For the purposes of predicting queue times, a bottleneck flow rate at which vehicles can enter an edge with respect to the performance bottlenecks the edge leads to is defined. Thus, for an edge representing an activity station (or any other bottleneck), the bottleneck flow rate is equal to the unloaded flow rate." and see para [0372]-[0374] and see para [0376], "The estimator 110 uses current and historical data about the mine operation 104 as well as data describing assets that can be used in the mine operation in order to determine and provide future estimates to the system 100. As used herein, “current data” is used to describe substantially current data, for example pertaining to the current mine shift, even if there may be some time lag, for example relating to data acquisition and communication. The future estimates include estimates regarding the expected performance of assets, referred to herein as “estimated asset parameters” (e.g. how long a truck is expected to take to perform a certain task), as well as future conditions of the mine operation 104 (e.g. what will the traffic be on a route in the mine that the truck will traverse, and that would therefore impact how long the truck is expected to take).")
It would have been obvious to a person of ordinary skill in the before the effective filing date of the claimed invention to combine the teachings of Humphrey with Hill because including a duration enables a system to take into consideration of constraints when to be more effective at deploying assets (see Hill, para[0002]-[0005]).
Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Hill, however, does not specifically performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
estimating an effect of entropy on a cycle time by performing a stochastic simulation based on historical cycle times to produce a future cycle time estimate for the at least one haul truck (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”);
estimating an effect of entropy on a material processing time associated with the material processing system by performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”)
estimating an effect of entropy on a cycle time by performing a stochastic simulation based on historical cycle times to produce a future cycle time estimate for the at least one haul truck (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”)
estimating a duration of the predicted delay by performing a stochastic simulation based on the historical delay data. (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”)
It would have been obvious to a person of ordinary skill in the before the effective filing date of the claimed invention to combine the teachings of Humphrey and Hill with Torkoly because using a stochastic simulation in the estimates can enable production entities to operate at production levels more integrated with input materials (see Torkoly, para [0009]-[0020]).
Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the Humphrey and Hill combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2-3:
Further, Humphrey disclose the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises …an average of one or more of historical load, spot, travel, and dump times for the haul trucks (see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110."); and
Humphrey, however, does not specifically disclose a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks. In analogous art, Hill discloses the following limitations:
a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined.").wherein the variability of one or more of the historical load, spot, travel, and dump times for the haul trucks comprise a standard deviation of one or more of the historical load, spot, travel, and dump times for the haul trucks (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Hill, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the Humphrey and Hill combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 4:
Further, Humphrey discloses the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises: …cycle times for loaded haul trucks (see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0059], showing determination of waiting and idle time for such vehicles) based on: an average of respective historical load, spot, travel, and dump times for loaded haul trucks (see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110.");
cycle times for empty haul trucks (see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0059], showing determination of waiting and idle time for such vehicles) based on: an average of respective historical load, spot, travel, and dump times for empty haul trucks (see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110.”); and
Humphrey, however, does not specifically disclose a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks. In analogous art, Hill discloses the following limitations:
a variability of the respective historical load, spot, travel, and dump times for the empty haul trucks (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined." where it is obvious to one of ordinary skill in the art that the haul trucks could be empty or loaded as shown in Humphrey).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Hill, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the Humphrey and Hill combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6-9:
Further, Humphrey disclose the following limitations:
when the at least one haul truck is operating in the loaded/empty state is based on: an average of the historical cycle times for the at least one haul truck when the at least one haul truck is operating in the loaded /empty state (see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110." and see para [0025]); and
Humphrey, however, does not specifically disclose a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks. In analogous art, Hill discloses the following limitations:
a variability of the historical cycle times for the at least one haul truck when the at least one haul truck is operating in the loaded/empty state (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined.").
wherein the variability of the historical cycle times for the at least one haul truck operating in the loaded state comprises the standard deviation of the historical cycle times for the at least one haul truck operating in the loaded/empty state (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Hill, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the Humphrey and Hill combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10, 13, 18, 21-22, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Humphrey (US 2016/0342915 A1) in view of Torkoly (US 2019/0265684 A1)
Claims 10 and 24:
Humphrey, as shown, discloses the following limitations of claims 10 and 24:
A method of operating a material handling system to need a defined production target, the material handling system including a plurality of deployable shovels, a plurality of deployable haul trucks, and a material processing system/ A method of deploying shovels and haul trucks to deliver sufficient excavated material to a material processing system to meet a defined production target (see para [0015], "The work site 100 comprises a plurality of shovels 108 and a plurality of processing sites 110, such as crushers. Vehicles, both autonomous and non-autonomous, transport material from the plurality of shovels 108 to the plurality of processing sites" and see para [0022], showing demand rate for shovels), comprising: generating at least one production constraint for at least one of the shovels and the material processing system (see para [0027], where the safety threshold is a constraint and see para [0041], where the threshold for tons is a constraint);
estimating an effect of entropy on a cycle time of the haul trucks…based on historical cycle times to produce a future cycle time estimate for the haul trucks (see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100.");
estimating an effect of entropy on a material processing time…based on historical material processing times to produce a future material processing time estimate for the material processing system (see para [0030]-[0033], showing measuring shoveling and unloading which can be considered material processing based on historical data where the rate of the shovel performing slowly because mining a harder block can be considered entropy of the material processing time and see para [0071] showing estimated time impacted by processing site);
predicting whether a delay will occur during the operation of at least one of the shovels, the haul trucks and the material processing system based on historical delay data (see para [0062]-[0066], determining whether vehicle bunching will occur);
determining a number of shovels and a number of haul trucks required to meet the defined production target based on the estimated production constraint, the future cycle time estimate, the future material processing time estimate, and the estimated duration of the predicted delay (see para [0022], showing demand rate for shovels and see para [0057], "computing system 300 determines the availability of the shovels 108 and the processing sites 110 to accept autonomous vehicles 108 or non-autonomous vehicles 110. For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110. For example, the processor 302 may calculate that the shovel 108 loads an autonomous vehicle 104 or a non-autonomous vehicle 106 every 1.5 minutes. The processor 302 may make a similar calculation regarding the unloading time of a processing site 110); and
deploying the determined number of shovels and the determined number of haul trucks (see para [0015], "To prevent the shovel 108 or processing site 110 from being underserved, the computing system 300 at the central site 102 may deploy one or more autonomous vehicles 104 to the underserved shovel 108 or processing site 110. Alternatively, if the computing system 300 determines that there are too many vehicles 104 and 106 within the work site 100, the computing system 300 may remove one or more autonomous vehicles 104 from the work site 100." and see para [0059], "Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved").
Humphrey, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly disloses the following limitations:
estimating an effect of entropy on a cycle time of the haul trucks by performing a stochastic simulation based on historical cycle times to produce a future cycle time estimate for the haul trucks (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”);
estimating an effect of entropy on a material processing time associated with the material processing system by performing a stochastic simulation based on historical material processing times to produce a future material processing time estimate for the material processing system (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”)
estimating a duration of the predicted delay by performing a stochastic simulation based on the historical delay data (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation. Naturally, such production entity is also conceivable where every transportation device is a shared resource. Similarly the injection moulding machine 110 and the injection moulding machine 120 may also coincide, in this case a common injection mould behaving as a shared resource would belong to production agent 110A and production agent 120A. Naturally, it is also conceivable that other production is taking place in the given production entity 100, which also shares the resources indicated in FIG. 3 (machines 110, 120, fork lift trucks 150, 150′, assembly line 130, and the human and machine resources 102, 103 located there). Waiting times of this nature may be reduced with production optimisation within the given production entity, as will be discussed later on.”);
It would have been obvious to a person of ordinary skill in the before the effective filing date of the claimed invention to combine the teachings of Humphrey with Torkoly because using a stochastic simulation in the estimates can enable production entities to operate at production levels more integrated with input materials (see Torkoly, para [0009]-[0020]).
Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 13:
Further, Humphrey discloses the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises: … based on historical cycle times for loaded haul trucks (see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100." And see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110."); and
… based on historical cycle times for empty haul trucks (see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100." And see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110.")
Humphrey, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 18:
Further, Humphrey discloses the following limitations:
wherein said predicting whether a delay will occur and said estimating a duration of the predicted delay are performed for each individual shovel, each individual haul truck, and the material processing system (see para [0059], [0070], showing system makes calculations using information from each vehicle and see para [0014], showing system can have a plurality of shovels, processing sites, etc. and see para [0030], showing metrics for such assets and see para [0062]-[0066], showing determining bunching)
Claims 20-21:
Humphrey does not specifically disclose establishing an acceptable level of risk that the determined number of shovels and the determined number of haul trucks will fail to meet the defined production target. In analogous art, Torkoly discloses the following limitations:
establishing an acceptable level of risk that the determined number of shovels and the determined number of haul trucks will fail to meet the defined production target and wherein the determined number of shovels and the determined number of haul trucks are within the acceptable level of risk (see para [0045], where the probability stipulating the reliability can be considered risk and see para [0049]-[0052], showing probability of stoppage)
wherein establishing the acceptable level of risk comprises establishing a probability matrix (see para [0044]-[0052], where it would be obvious to one of ordinary skill in the art that a probability index could be represented as a matrix as it is another mathematical representation of the same type of data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 11-12, 14-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Humphrey and Torkoly, as applied above, and further in view of Hill.
Claims 11-12:
Further, Humphrey disclose the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises …an average of one or more of historical load, spot, travel, and dump times for the haul trucks (see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110."); and
Humphrey, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
wherein said estimating the effect of entropy on the cycle time further comprises performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Torkoly, however, do not specifically disclose a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks. In analogous art, Hill discloses the following limitations:
a variability of one or more of the historical load, spot, travel, and dump times for the haul trucks (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined.").wherein the variability of one or more of the historical load, spot, travel, and dump times for the haul trucks comprise a standard deviation of one or more of the historical load, spot, travel, and dump times for the haul trucks (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the Humphrey and Torkoly combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 14-17:
Further, Humphrey discloses the following limitations:
wherein…based on the historical cycle times for loaded haul trucks is based on: an average of respective historical load, spot, travel, and dump times for loaded haul trucks (see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110."); and
wherein…based on the historical cycle times for empty haul trucks is based on: an average of respective historical load, spot, travel, and dump times for empty haul trucks (see para [0059], "The processor 302 may use the travel time information for each of the vehicles 104 and 106 to determine whether a shovel 108 or a processing site 110 will be underserved or overserved by vehicles 104 and 106. For example, the processor 302 may calculate that, based on the travel time for all of the vehicles 104 and 106, twenty minutes from the present time, a shovel 108 will experience a gap in vehicle 104 or 106 arrival. For example, there may be a three minute window where no vehicles 104 and 106 will service the shovel 108. Therefore, the shovel 108 will be underserved and thus the work site 100 will not be operating as efficiently as it could. Thus, if, based on the performance metrics provided by the shovel 108 or processing site 110, the shovel 108 loads a vehicle 104 or 106 or if the processing site 110 unloads a vehicle 104 or 106 every 1.5 minutes, then the processor 302 may calculate that an additional autonomous truck may be sent to the underserved shovel 108 or processing site 110 during the three minute window. Therefore, the shovel 108 may load one autonomous vehicle 104 or the processing site 110 may unload one autonomous vehicle 104 during the three minute window instead of being idle. Additionally, or alternatively, the processor 302 may calculate that at least one vehicle is waiting to service the shovel 108 or processing site 110. In other words, the shovel 108 or the processing site 110 may be currently overserved. However, based on the state, location, speed, and direction of travel information of the vehicles 104 and 106 in the work site, the processor 302 may determine that, while the shovel 108 or the processing site 110 is currently overserved, the shovel 108 or processing site 110 will be underserved in the future. Thus, the processor 302 may dispatch an autonomous vehicle 104 to ensure that the shovel 108 or the processing site 110 will not be underserved in the future. If the processor 302 determines that a shovel 108 or a processing site 110 may be underserved, the method may proceed to 708." see para [0071], "Based on how frequently the shovels 108 and the processing sites 110 service vehicles 104 and 106 and the estimated time it takes for vehicles 104 and 106 to reach the shovels 108 and processing sites 110, the computing system 300 may determine that a given shovel 108 or a processing site 110 will experience a gap in servicing vehicles 104 and 106 in the future. To fill the gap, the computing system 300 may activate and deploy an autonomous vehicle 104. In contrast, if the computing system 300 determines that a given shovel 108 or a processing site 110 will experience vehicle bunching in the future, the computing system 300 may remove one or more autonomous trucks 104 from the work site 100." And see para [0025], "The autonomous vehicle 104 may transmit information about a state, location, travel, and health information regarding the autonomous vehicle 104. Information about the state of the autonomous vehicle 104 may include whether the autonomous vehicle 104 is loaded or empty. It may also include what type of load it is carrying, for example, ore or waste." and see para [0057], For example, the shovel 108 and the processing site 110 may transmit historical and real-time performance metrics. Based on these metrics, the processor 302 may calculate the average loading time of a shovel 108 or the average unloading time of a processing site 110.")
Humphrey, however, does not specifically disclose performing a stochastic simulation. In analogous art, Torkoly discloses the following limitations:
performing a stochastic simulation (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
a stochastic simulation of the material processing time (see Fig 4 and para [0052]-[0055], “FIG. 4 illustrates a schematic block diagram of an exemplary discrete event simulation used in the method according to the invention. Stochastic dynamic simulation is preferably used in the case of the method according to the invention, in other words at discrete intervals it is determined whether a given event (such as stoppage of a machine, starting of a production, an completion of an activity, etc.) will occur at a given time on the basis of the probability indices presented earlier, such as noncumulative distribution functions. The starting of the individual activities may be preceded by some degree of waiting time, which may be described, for example, with the conditional probabilities described in connection with the status transitions in the case of the stoppage of an activity. Waiting times may also occur if the individual production agents used shared resources. For example, in the case of the embodiment shown in FIG. 1, it is conceivable that the transportation from the machine 110 to the repository 140, the transportation from the machine 120, and the transportation from the repository 140 to the assembly line 130 are performed by the same fork lift truck 150. In this case production agents 150A use the fork lift truck 150 as a shared resource for the individual transportations, as schematically illustrated in FIG. 4. Only one of the production agents 150A can be active at any one time, with the activity being stopped at the other production agents 150A, as the single transportation device (fork lift truck 150) is only able to perform the transportation task of one production agent 150A at a time. In the case of the present example there is a separate fork lift truck 150′ for the transportation of the input material 290′ arriving from the external production entity 200, therefore the activity of the production agent 150′A associated with this fork lift truck 150′ does not depend on the production agents 150A modelling the other transportation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the method for integrating production process as taught Torkoly in the system of autonomous fleet size management of Humphrey, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Humphrey and Torkoly, however, do not specifically disclose a variability of the respective historical load, spot, travel, and dump times for the loaded haul trucks. In analogous art, Hill discloses the following limitations:
a variability of the respective historical load, spot, travel, and dump times for the loaded haul trucks (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined." where it is obvious to one of ordinary skill in the art that the haul trucks could be empty or loaded as shown in Humphrey );
a variability of the respective historical load, spot, travel, and dump times for the empty haul trucks (see para [0181], " The dispatch planning window stretches from a dispatch start time to the end of a dispatch planning horizon. The dispatch planning window is typically a moving window within the flow planning window. In some embodiments the dispatch planning horizon is a fixed time period, e.g. 10 minutes, 30 minutes or 1 hour. In other embodiments the dispatch planning horizon is dependent on the activities of the assets, for example the dispatch planning horizon may be approximately the same as a haul-cycle. Accordingly, the dispatch planning window is selected to be approximately equal to an “activity period”. The activity periods are time periods that are based on the expected time to complete a certain type of activity, for example an activity period may be selected to be approximately the same length as (or slightly longer than) the longest haul-cycle. In some embodiments the activity periods utilised by the system 100 are variable and/or configurable, and in other embodiments the activity periods are a predefined time, e.g. 30 minutes or 1 hour. The dispatch planning window may include one or more activity periods so that not just the next decision is determined, but multiple steps for multiple assets are determined." where it is obvious to one of ordinary skill in the art that the haul trucks could be empty or loaded as shown in Humphrey)
wherein the variability of the respective historical load, spot, travel, and dump times for the loaded haul trucks comprises standard deviations of the respective historical load, spot, travel, and dump times for the loaded haul trucks (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc."); and
wherein the variability of the respective historical load, spot, travel, and dump times for the empty haul trucks comprises standard deviations of the respective historical load, spot, travel, and dump times for the empty haul trucks (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc.")
wherein the material processing system comprises at least one crusher (see para [0193], "Similarly, using the global mine data allows the dispatcher 108 to consider the required planned flow rates and generate dispatch assignments according to a forward looking plan for how all assets (e.g. trucks, diggers, stockpiles, and crushers) will be utilised in order to achieve the required planned flow rates. For example, even though the road network may also be used by other vehicles that are not controlled by the dispatcher, whenever information about the planned path of such a vehicle is known, the dispatcher 108 can benefit from taking this into account in order to minimise delays. Because the dispatcher 108 takes the global mine data into consideration, the dispatcher 108 is able to proactively modify the dispatch assignments, as required, in order to achieve the required planned flow rates so that the plan is achieved.") and … on the material processing time further comprises performing based on an average of historical crush out times (see para [0260], "In one embodiment the planned flow rates are smoothed using averaging in which the magnitudes of each planned flow rate are averaged across multiple time periods. In an exemplary embodiment this is done using a moving average. However, using a moving average could result in planned flow rates that violate one or more of the constraints, particularly around points in time where shovels go down for maintenance." and see para [0258]-[0259], showing the flow rate can be from crusher); and
a variability of the historical crush out times (see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc." and see para [0258]-[0261])
wherein the variability of the historical crush out times comprises a standard deviation of the historical crush out times (see para [0192], where the buffer can be considered a type of standard deviation and see para [0200]-[0201], "The flow planner 106 starts off determining the one or more planned flow rates over an initial flow plan window, such as a 12-hour shift. Periodically over this time, the flow planner updates the planned flow rates based on the updated global mine data. The updated planned flow rates are determined over a replanning flow plan window. The replanning flow plan window may have a decreasing horizon (i.e. what is left of the 12-hour shift), or the replanning flow plan window may have a receding horizon (i.e. for a further 12 hours). Accordingly, the length of the initial flow plan window and the length of one or more of the replanning flow plan windows may be different. The flow planner 106 may replan and update the planned flow rates on a regular basis, e.g. every 5, 10 or 30 minutes. Alternatively, the flow planner 106 may be triggered to update the planned flow rates based on one or more trigger events, for example, at the end of one or more haul cycles, when deviations in mine operation occur (such as unexpected downtime and maintenance on equipment), when the dispatcher provides a trigger (e.g. if the current state of the mine becomes incompatible with the current dispatch assignments), etc.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the Humphrey and Torkoly combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19:
Humphrey and Torkoly do not specifically disclose receiving data relating to at least one of a planned shift change, a planned equipment downtime, and a planned production target. In analogous art, Hill discloses the following limitations:
receiving data relating to at least one of a planned shift change, a planned equipment downtime, and a planned production target (see para [0192], "planned events such as maintenance of shovels and trucks, road conditions or a blast which shuts down part of the mine, can be automatically accounted for by the system 100, for example by allowing automated planning for run-of-mine (ROM) stockpile levels to provide a buffer for expected downtimes. Planned events can therefore be accounted for without requiring a human operator to override the flow planner 106 when these events occur." and see para [0410], "The system 100 considers the mine plan (including e.g. a production plan) as well as expected disruptions so that non-steady-state behaviour can be incorporated into the planned flow rates and therefore handled by the dispatching system, reducing the burden on the operators."); and
wherein said generating one production constraint is based on the received data relating to the at least one of the planned shift change, the planned equipment downtime, and the planned production target (see para [0192], showing conditioning parameters are taken into consideration for planned events such as maintenance).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the mining system as taught by Hill in the Humphrey and Torkoly combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 2017/0185943 A1), a system for predictive scheduling optimization by where at least one machine resource sufficient to fulfill the production order may be retrieved from a machine resources database in which individual machine resources are characterized based on production level, machine availability, and cost and sufficient inventory materials to fulfill the production order may be retrieved from an inventory database in which inventory materials are characterized based on inventory availability and cost
Keutner (EP 2499545 B1), a method for setting up or updating routing tables for a modular conveyor system and to a modular conveyor system for transporting goods to be transported by receiving a cost message, if it is assigned to a conveyor module having at least two outputs, builds up or updates the transmitted cost values by a routing table in which all outputs of the respective conveyor module information can be stored which target points of the conveyor system can be reached from there at what cost
Liberopoulos et al. "Contributions to stochastic models of manufacturing and service operations", a paper discussing recent results in the development, analysis, and application of stochastic models and methods for the performance evaluation and optimization of manufacturing and service system operations
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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