DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
Claims 1, 4, 8, 11 and 15 are presently amended.
Claims 2-3, 5, 7, 9-10, 13-14 and 18-19 are cancelled.
Claims 21-25 are newly added.
Claims 1, 4, 6, 8, 11-12, 15-17 and 20-25 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant' s arguments filed 3/2/2026 have been fully considered in view of further consideration of statutory law, Office policy, precedential common law, and the cited prior art as necessitated by the amendments to the claims, and are persuasive in-part for the reasons set forth below.
35 USC § 101 Rejections
First, Applicant argues that “Applicant's independent claims are not directed to any of a mathematical concept, a mental process, or a method of organizing human activity. Furthermore, at least the above recitations of changing a number of hauling work machines that service a loading work machine in order to change a current production rate for the loading work machine for a task initially assigned to an initial set of the hauling work machines servicing the loading work machine for the same task clearly show that Applicant's claims implement the alleged judicial exception in a manner that imposes meaningful limits on the judicial exception, for instance, such that the claim is more than a drafting effort designed to monopolize the exception. Thus, dynamically adjusting production rates for a loading work machine based on number of hauling work machines allocated to the loading work machine per task can allow for a maximum number of 'available' tasks to be completed on time. This can reduce or eliminate a requirement for constant monitoring and adjustment of static rates and can improve compliance to plan over a short-interval control (SIC) window ” [Arguments, pages 16-17].
In response, Applicant’s arguments are considered and are persuasive. Examiner observes that the amended claims, when considered as a whole, are considered to demonstrate a practical application of the observed abstract idea (certain methods of organizing human activity). In particular, Examiner observes that present limitations including at least, “and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine at the mine site as part of a corresponding one of the tasks, to change a current production rate of the loading work machine, during the shift, toward a predetermined production rate threshold” (Claim 1), in combination with further amendments which specify particular machines including hauling machines at a mine site goes beyond generally linking the use of the judicial exception to a particular technological environment (e.g. operations at a mine site).
Specifically, the additional elements including the hauling machines, individually specialized operator graphical user interfaces, management operator graphical user interfaces, active work engines and assignment engines impose a meaningful limit on the judicial exception and demonstrate an improvement to the field of mining operations and production management. As such, the amended claims, when considered as a whole, are considered to demonstrate a practical application of the observed abstract idea.
35 USC § 103 Rejections
First, Applicant argues that “…the applied references are not understood to disclose or suggest changing a number of hauling machines for a loading work machine to change a current production rate for the loading work machine for a task initially assigned to a current set of the hauling machines servicing the loading work machine for the particular task.
As such, Applicant's independent claims, at least as presently amended, patentably distinguish over the applied references, together with their respective dependent claims…” [Arguments, pages 15-16].
In response, Applicant’s arguments are considered but are not persuasive. Examiner respectfully disagrees and directs the Applicant to (Megannon , ¶ 230, FIG. 10—Optimizing from sequences of SOPs means selecting, not seeking, an option: In case of optimization, in terms of the prior art, a miner searches for the best option that fits certain selected or available criteria, but if the miner knows all the possible options he selects the optimal one from the generated ones. At present and in the prior art, mine operations are multi-faceted enterprises where each operational unit has its own KPIs, guiding the relevant mine planning, and each specialist is planning to maximize or minimise their KPIs. It is further unrealistic to expect any unit or specialist to plan for substandard KPIs, as it is not only contrary to their operational or professional obligations, but they also do not have sufficient information to be able to do so, i.e. to perform system planning, but only to plan for their own operations). The rate of production in one part of the mine is therefore typically not synchronized with that of other parts in the mine, causing bottlenecks or delays (FIG. 10 shows, for example, how rates of production have to be adjusted, not maximised, in order to keep the production going). Synchronizing multiple mine plans from multiple data sets in order to guarantee uninterrupted mine operations is simply too complex for human operation. As in the case of scheduling and simulating, the paradigm of optimizing is changed—the miner in the present invention does not generate an option and then assesses whether it is the best, the miner only assesses whether it is the best (since all options have already been calculated). The invention accordingly makes optimization faster. Moreover and in prior art, as soon as miner has an option that satisfies some threshold (discloses threshold production rate), the optimisation is typically terminated, while in the present invention, the miner is able to obtain and assess all (possible) options that satisfy the conditions (again offering awareness of what else is possible), placing the level of optimization well beyond human capability), and to (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all possible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6) (discloses adjusting a number of haulers to achieve a desired production rate). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)).
Here, and in Fig. 10, Megannon discloses a process wherein production rates are adjusted for optimal (not maximum) throughput to avoid bottlenecks at certain operations within the mining process. At Figure 11, a plan is selected based, in part, on a number of miners, in order to achieve the desired production rate. Thus, Examiner respectfully maintains that at least the Megannon reference renders obvious the above-argued limitation of the present invention. As such, Examiner remains unpersuaded.
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Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
a forecasting engine… an active work engine… an assignment engine… [Claims 1, 8 and 15].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 6, 8, 11-12, 15-17 and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Megannon et al., U.S. Publication No. 2024/0249226 [hereinafter Megannon] in view of Cervinka et al., U.S. Publication No. 2020/0364632 [hereinafter Cervinka].
Regarding claim 1, Megannon discloses …A management engine system to perform managing operations for a mine site according to dynamic task-based short-term planning comprising: a plurality of hauling work machines at the mine site, each of the plurality of hauling work machines including one or more sensors and a controller configured to output data from the one or more sensors via a wireless communication network (Megannon, ¶ 170, The mining technical activities 7 of a typical mining cycle in a tunnel 3 include drilling, blasting, cleaning and support-based activities, with each of these activities requiring disparate mining technical equipment such as drill rigs, explosive loaders, excavators or loaders, trucks or trains, (discloses hauling machines) roof bolters, etc. In addition, various experts and operators are typically required to operate the equipment effectively and synchronize their use with other mining technical activities. Tunnels 3 are built as fit-for purpose and will include further exploration tunnels, airways and in particular, access tunnels to stopping areas for routine mine production activities 7. Stopes 4 vary to suit the rock type as well as the size and shape of the ore body 2, for example, each stope can be left open or backfilled according to excavation stability requirements. It is essential for a mine to position stopes 4, shafts 5 and tunnels 3 in such a way that future developments of possible stopes, levels and shafts are not prohibited and therefore not losing opportunity to excavate), (Id., ¶ 5, The mining technical activities and the associated mining technical equipment depend on, use and generate vast amounts of mining technical data that has its origins in a variety of information toolsets available to and used by substantially disparate, professional mining technical disciplines. These toolsets and disciplines include geological & mineralogical surveys, mining & metallurgical sampling and analyses, electronic sensors connected to associated and 3.sup.rd party communication networks (discloses sensors to output data to a communication network), computer simulations and weather prognoses), (Id., ¶ 22, The Lee method, apparatus (discloses controller) and system collect real-time condition data indicative of conditions from at least one sensor at a particular location or from a plurality of sensors at different locations over time, wherein the collected data is time-synchronized and analyzed to determine conditions at the one or more locations over time. The Lee system (and method and apparatus) is particularly directed at collecting and analyzing real-time data from sensors);
While suggested in at least Fig. 1 and related text, Megannon does not explicitly disclose … and a processor and a display communicably coupled thereto, the processor configured to receive the data from the plurality of hauling work machines via the wireless communication network, transmit control signaling to the plurality of hauling work machines via the wireless communication network, and perform the following: generate a plurality of individually specialized operator graphical user interfaces (GUIs), selectively and individually display, on the display, the plurality of individually specialized operator graphical user interfaces (GUIs), electronically receive production ready tasks and production available tasks associated with the mine site responsive to input to one or more of the plurality of individually specialized operator GUIs, operate as each of a forecasting engine and an active work engine to perform short- interval control (SIC) processing of the production ready tasks and the production available tasks, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks for operation as the forecasting engine performing the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site for operation as the active work engine performing the SIC processing, and output the control signaling to the plurality of hauling work machines at the mine site, via the wireless communication network, to control operations of the hauling work machines at the mine site according to the production rates and the completion times, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, wherein the processor is configured to dynamically adjust planned tasks over a first short-interval timeline responsive to an input to the specialized management operator graphical user interface, wherein the specialized management operator graphical user interface includes the first short-interval timeline divided into a first amount of time within a shift relative to a current time, task indications for a plurality of tasks having respectively allocated thereto one or more hauling work machines of the plurality of hauling work machines, and production indications for production-related data for each of the tasks, wherein the specialized active work operator graphical user interface includes a second short-interval timeline from a second amount of time prior to the current time and variation indications regarding variations in a current state of tasks at the mine site, wherein the processor is configured to determine whether historical data is available and determine future rates and times using the historical data when the historical data is available, otherwise determining future rates and end times using work machine capacity and planned start and end times, for the operation as the forecasting engine, and wherein the processor is configured to match cycles to the production ready tasks and the production available tasks, determine material moved for each of the production ready tasks and the production available tasks, and use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein a ranked list of the production ready and production available tasks is generated, using the processor, at least partially automatically from one or more jobs of the imported short-term plan having therein the production ready tasks and the production available tasks, wherein one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface on the display is configured to display the ranked list of production ready and production available tasks, and wherein the ranked list of the production ready and production available tasks is output to an assignment engine to set prioritization regarding completion of task targets associated with the ranked list of production ready and production available tasks, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine at the mine site as part of a corresponding one of the tasks, to change a current production rate of the loading work machine, during the shift, toward a predetermined production rate threshold.
However, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Megannon and Cervinka discloses… and a processor and a display communicably coupled thereto, the processor configured to receive the data from the plurality of hauling work machines via the wireless communication network, transmit control signaling to the plurality of hauling work machines via the wireless communication network, and perform the following: generate a plurality of individually specialized operator graphical user interfaces (GUIs), selectively and individually display, on the display, the plurality of individually specialized operator graphical user interfaces (GUIs), electronically receive production ready tasks and production available tasks associated with the mine site responsive to input to one or more of the plurality of individually specialized operator GUIs, operate as each of a forecasting engine and an active work engine to perform short- interval control (SIC) processing of the production ready tasks and the production available tasks, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks for operation as the forecasting engine performing the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site for operation as the active work engine performing the SIC processing, and output the control signaling to the plurality of hauling work machines at the mine site, via the wireless communication network, to control operations of the hauling work machines at the mine site according to the production rates and the completion times, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, wherein the processor is configured to dynamically adjust planned tasks over a first short-interval timeline responsive to an input to the specialized management operator graphical user interface, wherein the specialized management operator graphical user interface includes the first short-interval timeline divided into a first amount of time within a shift relative to a current time, task indications for a plurality of tasks having respectively allocated thereto one or more hauling work machines of the plurality of hauling work machines, and production indications for production-related data for each of the tasks, wherein the specialized active work operator graphical user interface includes a second short-interval timeline from a second amount of time prior to the current time and variation indications regarding variations in a current state of tasks at the mine site, wherein the processor is configured to determine whether historical data is available and determine future rates and times using the historical data when the historical data is available, otherwise determining future rates and end times using work machine capacity and planned start and end times, for the operation as the forecasting engine, and wherein the processor is configured to match cycles to the production ready tasks and the production available tasks, determine material moved for each of the production ready tasks and the production available tasks, and use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein a ranked list of the production ready and production available tasks is generated, using the processor, at least partially automatically from one or more jobs of the imported short-term plan having therein the production ready tasks and the production available tasks, wherein one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface on the display is configured to display the ranked list of production ready and production available tasks, and wherein the ranked list of the production ready and production available tasks is output to an assignment engine to set prioritization regarding completion of task targets associated with the ranked list of production ready and production available tasks, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine at the mine site as part of a corresponding one of the tasks, to change a current production rate of the loading work machine, during the shift, toward a predetermined production rate threshold.
First, Megannon discloses display outputs of production rates and completion times for mining tasks, as well as display outputs for a current state of the mine compared to a planned state of the mine. Megannon further discloses dynamically adjusting planned tasks based on user input, as well as an interface depicting a timeline divided into segments including machine allocations, forecasting start and end times based on available historical data, and generating a ranked list of tasks and adjusting the number of miners to achieve a desired production rate (Megannon, ¶ 222, FIG. 5—A mine plan is made up of a sequence of SOPs and the corresponding schedules. Once created, the sequence of SOPs can be quantified with attributes representing specific operational context. For example, an SOP named dig could require 4 operators, 3 hours to complete at an estimated budget of $21 000 (see FIG. 5). Aggregating all information on operators needed to execute SOP along the sequence would produce a schedule of activities, including those illustrated in Figure I. Similarly, a miner can obtain other business schedules such as maintenance, survey, etc. schedules by aggregating other attributes with time. A specific combination of SOPs in a specific sequence, the corresponding schedules and a trajectory of excavation in 3D space constitute a mine plan), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 189, The first sub-system I (discloses active work engine) as illustrated in Figure B1 includes a non-transitory computer readable storage medium (not shown), storing computer-executable instructions, capable of extracting, amalgamating, translating and integrating big volumes of multi-disciplinary mining technical information/data from the disparate expert technical systems and applications, including computer developed or generated data, into the common, spatially-referenced database for use by the inventory management application to provide integrated mineral asset management in the mineral resource and reserve inventory of the preferred embodiment), (Id., ¶ 215, The second sub-system II (discloses forecast engine) automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary (discloses ranked list of tasks), essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 168, FIG. 58 shows the dashboard for all information at the mine site (the so-called “single source of truth” (SST)), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 65, Figure depicts a specialized management operator graphical user interface), ¶ 246, FIG. 18—ADS, complex models and EKG: EKG is a further level of data abstraction where data is collected from ADS and relationships among data are collected from models into a graph, not a table, FIG. 17 and FIG. 18. Each node in a graph represents a column in ADS and a link represents a relationship between the columns (which is typically not explicitly given in ADS). Both, nodes and links, can have attributes which can contain information about other nodes or links (for example derived is an attribute for node representing yield). Each node is referred to as an entity, while a group of entities connected into a sub-graph are referred to as ontologies. Ontologies represent a logical sub-unit, in other words, logical relationships among columns (for example three connected nodes yield, tonnage and time would be an ontology, FIG. 17), which can be defined by a human or a computer. (discloses adjusting tasks based on an input) A library of simple relationships (for example A=B/C) can be created by a human, while a computer can create very complex relationships (for example using neural networks, where such human does not have the necessary model). Models are stored as strings (simple equations or ontologies) or as binary files (complex equations from set of differential equations, K means, decision trees or neural networks). Finally ontologies can be populated with data from ADS and thus create EKG, where EKG contains all the data in ADS but also all the relationships among data in ADS (ADS contains values of a model such as yield but does not contain information about the relationships among features that constitute the model, i.e. how to calculate yield, ontologies do). Concepts such as columns, features and entities can therefore be used interchangeably since it refers to the same data but consumed in a different aspect (see FIGS. 17 and 18), while classical models and ontology models would be referred to as models), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 227, FIG. 8—Scheduling and synchronization of multiple schedules naturally based on sequence of SOPs: In case of scheduling, synchronizing schedules is a time consuming process, but if a miner has all the applicable SOP sequences, the creation of schedules derived from such sequences would be substantially faster. In accordance with the prior art, today, scheduling is a complex process done manually, through an iterative and interactive process between many specialists, such as Geo Engineers, Rock Engineers, Environmental Scientists, Laboratory Chemists, Data Analysts, Financial Managers, Accountants, Business Directors, etc. These specialists typically schedule the best they can with the most updated information they have at the time of mine planning. It is therefore a time-consuming process, since disparate data is not amalgamated into one place with one timeline, in terms of unified model management (UMM), prior to the invention as described in PCT Patent Application PCT/ZA2014/000036), (Id., ¶ 228, b. In accordance with the prior art, schedules are typically transferred back and forth among the specialists and often redone based on feedback and changing circumstances. Therefore, all mine plans—and therefore the integration of such mine plans, also as per PCT Patent Application PCT/ZA2015/000058, are time consuming and done over a considerable period of time. The consequence is that miners cannot change their mine plans often and quickly enough to respond to changing circumstances, and economic cycles are therefore inevitable. Moreover, specialists create schedules that are best for them, which is acceptable to some extent, but they do not remain with such schedules, due to the need to meet the material requirements of other specialists in order to synchronize schedules. In turn, a computer can schedule not only faster but better since it can synchronize schedules naturally and without conflict using the sequence of SOPs as a guidance—the system in accordance with the present invention therefore does not synchronize schedules per se, but generates all (possible) schedules, from all mine plans, and with ranking, overcomes any conflict automatically. Synchronizing multiple mine plans from multiple data sets (such as with multiple asset, logistics, maintenance, reserves and financial breakdown structures) is a complex process practically impossible for human endeavour above certain levels of complexity. While there are numerous software products in the prior art that attempt to solve such synchronization in principle, the applicant is not aware of any in the complex world of mining and mineralogy. The present invention not only would enable automated mine scheduling, but would also enable many specialists to respond faster and better at all levels of mine operations), (Id., Fig. 8, Figure depicts interface a timeline divided into segments including machine allocations), (Id., ¶ 215, The second sub-system II (discloses forecast engine) automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary, essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage (discloses available historical data), and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively), … (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage, and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., Fig. 58, Figure depicts an interface displaying a ranked list of tasks), (Id., ¶ 47, means for automatically ranking the plans within a specific extraction operational context; [0048] means for automatically integrating the ranked plans as an essential part of the technical mining and production process; and [0049] means for automatically implementing such plans (discloses setting prioritization for ranked plans) as an essential, intermediate and integrated part of the mining and production process with the use of associated mining technical equipment), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 34, In addition to the above terms, for purposes of this specification and within the context of the mining industry specifically, the term, with cognate terms having related meanings:
a. “all possible sequences” means all sequences of standard operating procedures (SOPs) that are possible within a specific context or by means of a specific algorithm, analytical data set and/or computing arrangement, and “all possible schedules” and “all possible mine plans” have corresponding meanings;
b. “attributes” for SOP” means an attribute pertaining to a SOP such as time of duration, number of people necessary, equipment being used or its cost;
c. “capabilities, activities, time and environment” (“CATE” (“CATEs”)) means the first principles or the known elements in a mining operation;
d. “code of practice” (“COP” (“COPs”)) means a set of rules which explains how people working in the mining industry should behave typically but not necessarily from a regulatory perspective, with the term “policies” having a similar meaning;
e. “computer-controlled” (mining technical equipment) comprises mining technical equipment operated with the control, assistance and/or aid of a computer; (discloses outputting control signals to hauling machines)
f. “current state of mine” means the snapshot of current state of: CATEs usage, activities, budget, risk, planned vs. actual and audit;
g. “dashboard” means a single, computer-generated canvas dynamically and interactively visualizing past, current and future mining technical and mining supporting information, thereby displaying current state of mine, data health checks, point of action and timeline of plans chosen to execute as well as other qualifications for ROM and LOM, such as risk evaluation, stress test, planned vs. actual and recommendation of improved KPIs), (Id., ¶ 230, FIG. 10—Optimizing from sequences of SOPs means selecting, not seeking, an option: In case of optimization, in terms of the prior art, a miner searches for the best option that fits certain selected or available criteria, but if the miner knows all the possible options he selects the optimal one from the generated ones. At present and in the prior art, mine operations are multi-faceted enterprises where each operational unit has its own KPIs, guiding the relevant mine planning, and each specialist is planning to maximize or minimise their KPIs. It is further unrealistic to expect any unit or specialist to plan for substandard KPIs, as it is not only contrary to their operational or professional obligations, but they also do not have sufficient information to be able to do so, i.e. to perform system planning, but only to plan for their own operations). The rate of production in one part of the mine is therefore typically not synchronized with that of other parts in the mine, causing bottlenecks or delays (FIG. 10 shows, for example, how rates of production have to be adjusted, not maximised, in order to keep the production going). Synchronizing multiple mine plans from multiple data sets in order to guarantee uninterrupted mine operations is simply too complex for human operation. As in the case of scheduling and simulating, the paradigm of optimizing is changed—the miner in the present invention does not generate an option and then assesses whether it is the best, the miner only assesses whether it is the best (since all options have already been calculated). The invention accordingly makes optimization faster. Moreover and in prior art, as soon as miner has an option that satisfies some threshold (discloses threshold production rate), the optimisation is typically terminated, while in the present invention, the miner is able to obtain and assess all (possible) options that satisfy the conditions (again offering awareness of what else is possible), placing the level of optimization well beyond human capability), and to (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all ossible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6) (discloses adjusting a number of haulers to achieve a desired production rate). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)).
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Further, Cervinka discloses short interval control optimization of tasks in a mining environment, as well as matching cycles to production ready tasks and production available tasks, and determining material moved for each of the production ready tasks and the production available tasks (Cervinka, ¶ 21, the method and system generally comprise automatically capturing data with minimal manual data entry or without any manual data entry, such system aiming at eliminating human errors or at least reducing to a minimum such errors. The system is generally configured to capture at least information in relation of to the location of loading/dumping of a vehicle and the net payload that was dumped by the vehicle), (Id., ¶ 94, real-time visibility on the material movement enables short interval control optimization of the mine plan, as discussed in academic papers such as: https://www.gerad.ca/en/papers/G-2016-26/view), (Id., ¶ 75, Referring to FIG. 5, the truck payload monitoring sub-system typically comprises a plurality of load cells 114 configured to capture data about the weight of the bin and/or one or more load pin cell 112 configured to detect the load at the pivot location of the bin. The sub-system may further comprise a display unit or scoreboard indicator 116 adapted to display the number of tons of material that was loaded and unloaded in the truck 12 bin. The sub-system may further comprise a payload monitor and/or encoder 106. All components (such as sensors, display unit and/or monitor encoder) are generally connected to a central processing unit which capture and analyze the data received in real time), (Id., ¶ 78, similarly to LHD embodiments, additional sensors may be installed on the truck 12 to simultaneously monitor the truck activities. Such data is generally communicated to the central data logger and/or to the signal processing unit. The additional sensors may comprise one or more of the following sensors: [0079] inclination sensor of the truck frame. Such sensor generally aims at providing context for the load cells and therefore to increase payload measurement accuracy; [0080] wheel-based vehicle speed sensor. Such sensor typically aims at measuring haulage intensity KPI, since in underground mines the route may significantly vary significantly from one load to the next), (Id., ¶ 51, The tracking of the vehicle location may be done using any type of positioning technologies, such as RFID or LiDAR positioning technologies combined with store-and-forward or real-time wireless communications. Understandably, any known type of positioning technologies may be used without restricting the scope of the present invention).
One of ordinary skill in the art would have recognized that applying the known technique of Cervinka would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the short-interval control optimization technique of Cervinka to the specialized mining interface display teachings of Megannon would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such optimization features into similar mining management systems. Further, applying the short-interval control optimization technique of Cervinka to the specialized mining interface displays of Megannon, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed reports of ongoing mining operations according to specific parameters.
Thus, through KSR Rationale D, the combination of Megannon and Cervinka discloses … and a processor and a display communicably coupled thereto, the processor configured to receive the data from the plurality of hauling work machines via the wireless communication network, transmit control signaling to the plurality of hauling work machines via the wireless communication network, and perform the following: generate a plurality of individually specialized operator graphical user interfaces (GUIs), selectively and individually display, on the display, the plurality of individually specialized operator graphical user interfaces (GUIs), electronically receive production ready tasks and production available tasks associated with the mine site responsive to input to one or more of the plurality of individually specialized operator GUIs, operate as each of a forecasting engine and an active work engine to perform short- interval control (SIC) processing of the production ready tasks and the production available tasks, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks for operation as the forecasting engine performing the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site for operation as the active work engine performing the SIC processing, and output the control signaling to the plurality of hauling work machines at the mine site, via the wireless communication network, to control operations of the hauling work machines at the mine site according to the production rates and the completion times, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, wherein the processor is configured to dynamically adjust planned tasks over a first short-interval timeline responsive to an input to the specialized management operator graphical user interface, wherein the specialized management operator graphical user interface includes the first short-interval timeline divided into a first amount of time within a shift relative to a current time, task indications for a plurality of tasks having respectively allocated thereto one or more hauling work machines of the plurality of hauling work machines, and production indications for production-related data for each of the tasks, wherein the specialized active work operator graphical user interface includes a second short-interval timeline from a second amount of time prior to the current time and variation indications regarding variations in a current state of tasks at the mine site, wherein the processor is configured to determine whether historical data is available and determine future rates and times using the historical data when the historical data is available, otherwise determining future rates and end times using work machine capacity and planned start and end times, for the operation as the forecasting engine, and wherein the processor is configured to match cycles to the production ready tasks and the production available tasks, determine material moved for each of the production ready tasks and the production available tasks, and use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein a ranked list of the production ready and production available tasks is generated, using the processor, at least partially automatically from one or more jobs of the imported short-term plan having therein the production ready tasks and the production available tasks, wherein one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface on the display is configured to display the ranked list of production ready and production available tasks, and wherein the ranked list of the production ready and production available tasks is output to an assignment engine to set prioritization regarding completion of task targets associated with the ranked list of production ready and production available tasks, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine at the mine site as part of a corresponding one of the tasks, to change a current production rate of the loading work machine, during the shift, toward a predetermined production rate threshold.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the specialized mining interface elements of Megannon to include the short-interval control (SIC) processing elements of Cervinka in the analogous art of automated monitoring of material movement.
The motivation for doing so would have been to provide an improved method for “automatically capturing data with minimal manual data entry or without any manual data entry, such system aiming at eliminating human errors or at least reducing to a minimum such errors. The system is generally configured to capture at least information in relation of to the location of loading/dumping of a vehicle and the net payload that was dumped by the vehicle” [Cervinka, ¶ 21], wherein such improvements would benefit Megannon’s method of “Improving on scheduling, simulating, optimizing and mine planning tasks” [Cervinka, ¶ 21; Megannon, ¶ 224].
Regarding claim 4, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …wherein the variation indications regarding variations in the current state of tasks at the mine site of the specialized active work operator graphical user interface includes indications for material arcs and flow rates, and wherein the task indications for the plurality of tasks having respectively allocated thereto the one or more work machines, and the production indications for production-related data for each of the tasks are generated based on processing of the forecasting engine (Id., ¶ 280, FIG. 33—Integrating new columns (features, entities) and models (ontologies): The biggest reason for using ADS and EKG (beside performing analyses) is that building blocks of ADS and EKG can be computer-generated. An algorithm can systematically create new columns in ADS by combining the existing columns (such as the ratio between two values in two columns) or it can create new relationships by testing for high correlations among columns. For example, FIG. 33, an algorithm can divide all pairs of columns in an ADS and discover for example, that a pair of columns tonnes and time correlate with a name of a specific equipment operator. This new column can be named yield and integrated into ADS and into EKG. New columns in ADS will be reflected as new features available for models or new entities for ontologies. New features and new entities will result in new relationships. New feature will be connected to the existing features in ADS by new relationships in the form of a mathematical expression (model). New entity will be connected to the existing entities in ontology (and therefore in EKG) by new relationships as new links between nodes in a graph), (Id., ¶ 246, FIG. 18—ADS, complex models and EKG: EKG is a further level of data abstraction where data is collected from ADS and relationships among data are collected from models into a graph, not a table, FIG. 17 and FIG. 18. Each node in a graph represents a column in ADS and a link represents a relationship between the columns (which is typically not explicitly given in ADS). Both, nodes and links, can have attributes which can contain information about other nodes or links (for example derived is an attribute for node representing yield). Each node is referred to as an entity, while a group of entities connected into a sub-graph are referred to as ontologies. Ontologies represent a logical sub-unit, in other words, logical relationships among columns (for example three connected nodes yield, tonnage and time would be an ontology, FIG. 17), which can be defined by a human or a computer. A library of simple relationships (for example A=B/C) can be created by a human, while a computer can create very complex relationships (for example using neural networks, where such human does not have the necessary model). Models are stored as strings (simple equations or ontologies) or as binary files (complex equations from set of differential equations, K means, decision trees or neural networks). Finally ontologies can be populated with data from ADS and thus create EKG, where EKG contains all the data in ADS but also all the relationships among data in ADS (ADS contains values of a model such as yield but does not contain information about the relationships among features that constitute the model, i.e. how to calculate yield, ontologies do). Concepts such as columns, features and entities can therefore be used interchangeably since it refers to the same data but consumed in a different aspect (see FIGS. 17 and 18), while classical models and ontology models would be referred to as models), (Id., Fig. 33, Figure depicts material arcs and flow rates as well as allocated machines and production data).
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Regarding claim 6, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses … wherein the processor is configured to switch from displaying one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface to displaying the other of the specialized management operator graphical user interface or the specialized active work operator graphical user interface responsive to a single input to the displayed one of specialized management operator graphical user interface or the specialized active work operator graphical user interface (Id., ¶ 120, FIG. 10 shows the optimizing from sequences of SOPs means selecting, not seeking, an option), (Id., ¶ 121, FIG. 11 shows the planning from sequences of SOPs means selecting a group of mine plans), (Id., ¶ 239, FIG. 14—Representation of a two-step process of extending the variations: Libraries of SOPs and rules applied to them can be numerous, but the repeating two step method of creating variations remains the same, namely when choosing the next SOP to extend the sequence then first, a miner selects available SOPs and then second, goes through them to choose those that are eligible—and the process repeats (see FIG. 14). In a case that there are multiple eligible options, all options are applied and sequence creation continues in parallel for multiple sequences (see FIG. 14, option B). Multiple sequences further can be independent of each other or dependent on each other. Multiple sequence can split (fork) in two or more streams which still belong to a same schedule, such as shown in FIG. 14 option C, which subsequently might or might not join into a single sequence or be split into independent sequences thereafter. In this way, sequences can represent different aspects of mine planning, such as equipment operator tasks, engineering tasks, accounting tasks, IT tasks, etc. Therefore, a mine planning sequence of SOPs can be a single sequence or multiple sequences of SOPs that are dependent or independent of each other, and which occur in parallel at the same space or at different locations, yet due to construction of SOP, the method of selecting the next SOP is the same for a single or multiple sequences (also referred to as invariant) (discloses selecting particular GUIs)), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 56, Figure depicts a specialized management operator graphical user interface).
Regarding claim 8, Megannon discloses …A method of managing a mine site, the method comprising: selectively and individually displaying, on a display, a plurality of individually specialized operator graphical user interfaces (GUIs) (Megannon, ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: …e. “computer” means a digital computer or a digital computer controlled hardware and software arrangement; … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 34, for purposes of this specification and within the context of the mining industry specifically, the term, with cognate terms having related meanings:… g. “dashboard” means a single, computer-generated canvas dynamically and interactively visualizing past, current and future mining technical and mining supporting information, thereby displaying current state of mine, data health checks, point of action and timeline of plans chosen to execute as well as other qualifications for ROM and LOM, such as risk evaluation, stress test, planned vs. actual and recommendation of improved KPIs)
electronically receiving production ready tasks and production available tasks associated with the mine site responsive to input to one or more of the plurality of individually specialized operator GUIs (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 65, The automated means for creating, ranking, integrating and implementing mine plans may comprise: [0065] a variational engine (discloses assignment engine), for generating numerous schedule variations automatically);
determining, using the processor, whether historical data is available; determining, using the processor, future rates and times using the historical data when the historical data is available, otherwise determining future rates and end times using work machine capacity and planned start and end times, for the operation as the forecasting engine (Id., ¶ 215, The second sub-system II (discloses forecast engine) automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary, essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage (discloses historical data), and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively);
and responsive to an input to the one of the specialized management operator interface or the specialized active work operator interface, of the plurality of individually specialized operator GUIs, dynamically varying, using the processor, visual indications on the one of the specialized management operator interface or the specialized active work operator interface based on processing of the input by one of the forecasting engine or the active work engine, respectively (Id., ¶ 120, FIG. 10 shows the optimizing from sequences of SOPs means selecting, not seeking, an option), (Id., ¶ 121, FIG. 11 shows the planning from sequences of SOPs means selecting a group of mine plans), (Id., ¶ 239, FIG. 14—Representation of a two-step process of extending the variations: Libraries of SOPs and rules applied to them can be numerous, but the repeating two step method of creating variations remains the same, namely when choosing the next SOP to extend the sequence then first, a miner selects available SOPs and then second, goes through them to choose those that are eligible—and the process repeats (see FIG. 14). In a case that there are multiple eligible options, all options are applied and sequence creation continues in parallel for multiple sequences (see FIG. 14, option B). Multiple sequences further can be independent of each other or dependent on each other. Multiple sequence can split (fork) in two or more streams which still belong to a same schedule, such as shown in FIG. 14 option C, which subsequently might or might not join into a single sequence or be split into independent sequences thereafter. In this way, sequences can represent different aspects of mine planning, such as equipment operator tasks, engineering tasks, accounting tasks, IT tasks, etc. Therefore, a mine planning sequence of SOPs can be a single sequence or multiple sequences of SOPs that are dependent or independent of each other, and which occur in parallel at the same space or at different locations, yet due to construction of SOP, the method of selecting the next SOP is the same for a single or multiple sequences (also referred to as invariant) (discloses selecting particular GUIs)), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 65, Figure depicts a specialized management operator graphical user interface);
matching, using the processor, cycles to the production ready tasks and the production available tasks (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage, and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control);
generating, using the processor, a ranked list of the production ready and production available tasks automatically from one or more jobs of an imported short- term plan having therein the production ready tasks and the production available tasks (Id., ¶ 215, The second sub-system II automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary (discloses generating a ranked list of tasks), essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … i. “integrated mine plan” means the mine plan across various timelines, such as over short, medium and long term, from a strategic, business and operational perspective, composed with one tool at any chosen level of granularity; … l. “mine plan” means a digital record of a sequence of SOPs, its corresponding schedules representing time layout of actions within a specific operational context (discloses short-term mine plan), and its corresponding space layout of SOPs executed, typically showing the workings of a mining operation, such as excavations and shafts, including any previous workings (legacy), copied and revised from time to time to show any significant changes to the mine workings, some of which are to be produced, on request, in electronic or hard copy format, to a regulatory body or representative such as a mining inspector and certified and signed off by a duly authorised mine surveyor or like professional person), (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer);
outputting, using the processor, the ranked list of the production ready and production available tasks to an assignment engine to set prioritization regarding completion of task targets associated with the ranked list of production ready and production available tasks (Id., ¶ 47, means for automatically ranking the plans within a specific extraction operational context; [0048] means for automatically integrating the ranked plans as an essential part of the technical mining and production process; and [0049] means for automatically implementing such plans (discloses setting prioritization for ranked plans) as an essential, intermediate and integrated part of the mining and production process with the use of associated mining technical equipment), (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer), (Id., ¶ 65, The automated means for creating, ranking, integrating and implementing mine plans may comprise: [0065] a variational engine (discloses assignment engine), for generating numerous schedule variations automatically);
…and outputting, using the processor, control signaling to a plurality of hauling work machines at the mine site to control operations of the hauling work machines at the mine site according to the production rates, wherein one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface on the display is configured to display the ranked list of production ready and production available tasks (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 34, In addition to the above terms, for purposes of this specification and within the context of the mining industry specifically, the term, with cognate terms having related meanings:
a. “all possible sequences” means all sequences of standard operating procedures (SOPs) that are possible within a specific context or by means of a specific algorithm, analytical data set and/or computing arrangement, and “all possible schedules” and “all possible mine plans” have corresponding meanings;
b. “attributes” for SOP” means an attribute pertaining to a SOP such as time of duration, number of people necessary, equipment being used or its cost;
c. “capabilities, activities, time and environment” (“CATE” (“CATEs”)) means the first principles or the known elements in a mining operation;
d. “code of practice” (“COP” (“COPs”)) means a set of rules which explains how people working in the mining industry should behave typically but not necessarily from a regulatory perspective, with the term “policies” having a similar meaning;
e. “computer-controlled” (mining technical equipment) comprises mining technical equipment operated with the control, assistance and/or aid of a computer; (discloses outputting control signals to hauling machines)
f. “current state of mine” means the snapshot of current state of: CATEs usage, activities, budget, risk, planned vs. actual and audit;
g. “dashboard” means a single, computer-generated canvas dynamically and interactively visualizing past, current and future mining technical and mining supporting information, thereby displaying current state of mine, data health checks, point of action and timeline of plans chosen to execute as well as other qualifications for ROM and LOM, such as risk evaluation, stress test, planned vs. actual and recommendation of improved KPIs), (Id., Fig. 58, Figure depicts an interface displaying a ranked list of tasks);
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While suggested in at least Fig. A and related text, Megannon does not explicitly disclose …performing, using a processor, short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and/or an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; determining, using the processor, material moved for each of the production ready tasks and the production available tasks, and using the historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, wherein said dynamically varying includes dynamically adjusting planned tasks over a short-interval timeline responsive to the input to the specialized management operator interface, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine as part of one of the planned tasks, to change a current production rate of the loading work machine, during the short-interval timeline, toward a predetermined production rate threshold.
However, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Megannon and Cervinka discloses …performing, using a processor, short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and/or an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; determining, using the processor, material moved for each of the production ready tasks and the production available tasks, and using the historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, wherein said dynamically varying includes dynamically adjusting planned tasks over a short-interval timeline responsive to the input to the specialized management operator interface, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from a loading work machine as part of one of the planned tasks, to change a current production rate of the loading work machine, during the short-interval timeline, toward a predetermined production rate threshold.
First, Megannon discloses display outputs of production rates and completion times for mining tasks, as well as display outputs for a current state of the mine compared to a planned state of the mine, as well as adjusting planned tasks in response to an input, as well as an interface depicting a timeline divided into segments including machine allocations, forecasting start and end times based on available historical data, and generating a ranked list of tasks and adjusting the number of miners to achieve a desired production rate (Megannon, ¶ 222, FIG. 5—A mine plan is made up of a sequence of SOPs and the corresponding schedules. Once created, the sequence of SOPs can be quantified with attributes representing specific operational context. For example, an SOP named dig could require 4 operators, 3 hours to complete at an estimated budget of $21 000 (see FIG. 5). Aggregating all information on operators needed to execute SOP along the sequence would produce a schedule of activities, including those illustrated in Figure I. Similarly, a miner can obtain other business schedules such as maintenance, survey, etc. schedules by aggregating other attributes with time. A specific combination of SOPs in a specific sequence, the corresponding schedules and a trajectory of excavation in 3D space constitute a mine plan), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 189, The first sub-system I (discloses active work engine) as illustrated in Figure B1 includes a non-transitory computer readable storage medium (not shown), storing computer-executable instructions, capable of extracting, amalgamating, translating and integrating big volumes of multi-disciplinary mining technical information/data from the disparate expert technical systems and applications, including computer developed or generated data, into the common, spatially-referenced database for use by the inventory management application to provide integrated mineral asset management in the mineral resource and reserve inventory of the preferred embodiment), (Id., ¶ 215, The second sub-system II (discloses forecast engine) automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary, essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 168, FIG. 58 shows the dashboard for all information at the mine site (the so-called “single source of truth” (SST)), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 65, Figure depicts a specialized management operator graphical user interface), (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage, and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 230, FIG. 10—Optimizing from sequences of SOPs means selecting, not seeking, an option: In case of optimization, in terms of the prior art, a miner searches for the best option that fits certain selected or available criteria, but if the miner knows all the possible options he selects the optimal one from the generated ones. At present and in the prior art, mine operations are multi-faceted enterprises where each operational unit has its own KPIs, guiding the relevant mine planning, and each specialist is planning to maximize or minimise their KPIs. It is further unrealistic to expect any unit or specialist to plan for substandard KPIs, as it is not only contrary to their operational or professional obligations, but they also do not have sufficient information to be able to do so, i.e. to perform system planning, but only to plan for their own operations). The rate of production in one part of the mine is therefore typically not synchronized with that of other parts in the mine, causing bottlenecks or delays (FIG. 10 shows, for example, how rates of production have to be adjusted, not maximised, in order to keep the production going). Synchronizing multiple mine plans from multiple data sets in order to guarantee uninterrupted mine operations is simply too complex for human operation. As in the case of scheduling and simulating, the paradigm of optimizing is changed—the miner in the present invention does not generate an option and then assesses whether it is the best, the miner only assesses whether it is the best (since all options have already been calculated). The invention accordingly makes optimization faster. Moreover and in prior art, as soon as miner has an option that satisfies some threshold (discloses threshold production rate), the optimisation is typically terminated, while in the present invention, the miner is able to obtain and assess all (possible) options that satisfy the conditions (again offering awareness of what else is possible), placing the level of optimization well beyond human capability), and to (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all ossible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6) (discloses adjusting a number of haulers to achieve a desired production rate). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)).
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Further, Cervinka discloses short interval control optimization of tasks in a mining environment as well as determining amounts of material moved for production ready tasks (Cervinka, ¶ 21, the method and system generally comprise automatically capturing data with minimal manual data entry or without any manual data entry, such system aiming at eliminating human errors or at least reducing to a minimum such errors. The system is generally configured to capture at least information in relation of to the location of loading/dumping of a vehicle and the net payload that was dumped by the vehicle), (Id., ¶ 94, real-time visibility on the material movement enables short interval control optimization of the mine plan, as discussed in academic papers such as: https://www.gerad.ca/en/papers/G-2016-26/view), (Id., ¶ 78, similarly to LHD embodiments, additional sensors may be installed on the truck 12 to simultaneously monitor the truck activities. Such data is generally communicated to the central data logger and/or to the signal processing unit. The additional sensors may comprise one or more of the following sensors: [0079] inclination sensor of the truck frame. Such sensor generally aims at providing context for the load cells and therefore to increase payload measurement accuracy; [0080] wheel-based vehicle speed sensor. Such sensor typically aims at measuring haulage intensity KPI, since in underground mines the route may significantly vary significantly from one load to the next), (Id., ¶ 75, Referring to FIG. 5, the truck payload monitoring sub-system typically comprises a plurality of load cells 114 configured to capture data about the weight of the bin and/or one or more load pin cell 112 configured to detect the load at the pivot location of the bin. The sub-system may further comprise a display unit or scoreboard indicator 116 adapted to display the number of tons of material that was loaded and unloaded in the truck 12 bin. The sub-system may further comprise a payload monitor and/or encoder 106. All components (such as sensors, display unit and/or monitor encoder) are generally connected to a central processing unit which capture and analyze the data received in real time).
One of ordinary skill in the art would have recognized that applying the known technique of Cervinka would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the short-interval control optimization technique of Cervinka to the specialized mining interface display teachings of Megannon would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such optimization features into similar mining management systems. Further, applying the short-interval control optimization technique of Cervinka to the specialized mining interface displays of Megannon, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed reports of ongoing mining operations according to specific parameters.
Thus, through KSR Rationale D, the combination of Megannon and Cervinka discloses …performing, using a processor, short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and/or an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; using the processor, determining, using the processor, material moved for each of the production ready tasks and the production available tasks, and using the historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on the display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively, and wherein said dynamically varying includes dynamically adjusting planned tasks over the first short-interval timeline responsive to the input to the specialized management operator interface.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the specialized mining interface elements of Megannon to include the short-interval control (SIC) processing elements of Cervinka in the analogous art of automated monitoring of material movement for the same reasons as stated for claim 1.
Regarding claim 11, the combination of Megannon and Cervinka discloses …The method according to Claim 8…
Megannon further discloses … wherein the specialized management operator graphical user interface includes a first short-interval timeline divided into a first amount of time within a shift relative to a current time, and production indications for production-related data for the task for the loading work machine, and wherein the specialized active work operator graphical user interface includes a second short-interval timeline from a second amount of time prior to the current time and variation indications regarding variations in the current state of tasks at the mine site, including the task for the loading work machine (Id., ¶ 227, FIG. 8—Scheduling and synchronization of multiple schedules naturally based on sequence of SOPs: In case of scheduling, synchronizing schedules is a time consuming process, but if a miner has all the applicable SOP sequences, the creation of schedules derived from such sequences would be substantially faster. In accordance with the prior art, today, scheduling is a complex process done manually, through an iterative and interactive process between many specialists, such as Geo Engineers, Rock Engineers, Environmental Scientists, Laboratory Chemists, Data Analysts, Financial Managers, Accountants, Business Directors, etc. These specialists typically schedule the best they can with the most updated information they have at the time of mine planning. It is therefore a time-consuming process, since disparate data is not amalgamated into one place with one timeline, in terms of unified model management (UMM), prior to the invention as described in PCT Patent Application PCT/ZA2014/000036), (Id., ¶ 228, b. In accordance with the prior art, schedules are typically transferred back and forth among the specialists and often redone based on feedback and changing circumstances. Therefore, all mine plans—and therefore the integration of such mine plans, also as per PCT Patent Application PCT/ZA2015/000058, are time consuming and done over a considerable period of time. The consequence is that miners cannot change their mine plans often and quickly enough to respond to changing circumstances, and economic cycles are therefore inevitable. Moreover, specialists create schedules that are best for them, which is acceptable to some extent, but they do not remain with such schedules, due to the need to meet the material requirements of other specialists in order to synchronize schedules. In turn, a computer can schedule not only faster but better since it can synchronize schedules naturally and without conflict using the sequence of SOPs as a guidance—the system in accordance with the present invention therefore does not synchronize schedules per se, but generates all (possible) schedules, from all mine plans, and with ranking, overcomes any conflict automatically. Synchronizing multiple mine plans from multiple data sets (such as with multiple asset, logistics, maintenance, reserves and financial breakdown structures) is a complex process practically impossible for human endeavour above certain levels of complexity. While there are numerous software products in the prior art that attempt to solve such synchronization in principle, the applicant is not aware of any in the complex world of mining and mineralogy. The present invention not only would enable automated mine scheduling, but would also enable many specialists to respond faster and better at all levels of mine operations), (Id., Fig. 8, Figure depicts interface a timeline divided into segments including machine allocations).
Regarding claim 12, the combination of Megannon and Cervinka discloses …The method according to Claim 8…
Megannon further discloses … wherein the variation indications regarding variations in the current state of tasks at the mine site of the specialized active work operator graphical user interface includes indications for material arcs and flow rates, and wherein the task indications for the plurality of tasks having allocated thereto one or more work machines, and the production indications for production-related data for each of the tasks are generated based on processing of the forecasting engine (Id., ¶ 280, FIG. 33—Integrating new columns (features, entities) and models (ontologies): The biggest reason for using ADS and EKG (beside performing analyses) is that building blocks of ADS and EKG can be computer-generated. An algorithm can systematically create new columns in ADS by combining the existing columns (such as the ratio between two values in two columns) or it can create new relationships by testing for high correlations among columns. For example, FIG. 33, an algorithm can divide all pairs of columns in an ADS and discover for example, that a pair of columns tonnes and time correlate with a name of a specific equipment operator. This new column can be named yield and integrated into ADS and into EKG. New columns in ADS will be reflected as new features available for models or new entities for ontologies. New features and new entities will result in new relationships. New feature will be connected to the existing features in ADS by new relationships in the form of a mathematical expression (model). New entity will be connected to the existing entities in ontology (and therefore in EKG) by new relationships as new links between nodes in a graph), (Id., ¶ 246, FIG. 18—ADS, complex models and EKG: EKG is a further level of data abstraction where data is collected from ADS and relationships among data are collected from models into a graph, not a table, FIG. 17 and FIG. 18. Each node in a graph represents a column in ADS and a link represents a relationship between the columns (which is typically not explicitly given in ADS). Both, nodes and links, can have attributes which can contain information about other nodes or links (for example derived is an attribute for node representing yield). Each node is referred to as an entity, while a group of entities connected into a sub-graph are referred to as ontologies. Ontologies represent a logical sub-unit, in other words, logical relationships among columns (for example three connected nodes yield, tonnage and time would be an ontology, FIG. 17), which can be defined by a human or a computer. A library of simple relationships (for example A=B/C) can be created by a human, while a computer can create very complex relationships (for example using neural networks, where such human does not have the necessary model). Models are stored as strings (simple equations or ontologies) or as binary files (complex equations from set of differential equations, K means, decision trees or neural networks). Finally ontologies can be populated with data from ADS and thus create EKG, where EKG contains all the data in ADS but also all the relationships among data in ADS (ADS contains values of a model such as yield but does not contain information about the relationships among features that constitute the model, i.e. how to calculate yield, ontologies do). Concepts such as columns, features and entities can therefore be used interchangeably since it refers to the same data but consumed in a different aspect (see FIGS. 17 and 18), while classical models and ontology models would be referred to as models), (Id., Fig. 33, Figure depicts material arcs and flow rates as well as allocated machines and production data).
Regarding claim 15, Megannon discloses … A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by one or more processors, causes the one or more processors to: electronically receive production ready tasks and production available tasks associated with the mine site responsive to input to one or more of the plurality of individually specialized operator GUIs (Megannon, ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: …e. “computer” means a digital computer or a digital computer controlled hardware and software arrangement; … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 34, for purposes of this specification and within the context of the mining industry specifically, the term, with cognate terms having related meanings:… g. “dashboard” means a single, computer-generated canvas dynamically and interactively visualizing past, current and future mining technical and mining supporting information, thereby displaying current state of mine, data health checks, point of action and timeline of plans chosen to execute as well as other qualifications for ROM and LOM, such as risk evaluation, stress test, planned vs. actual and recommendation of improved KPIs), (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer), (Id., ¶ 65, The automated means for creating, ranking, integrating and implementing mine plans may comprise: [0065] a variational engine (discloses assignment engine), for generating numerous schedule variations automatically); determine whether historical data is available; determine future rates and times using the historical data when the historical data is available, otherwise determining future rates and end times using work machine capacity and planned start and end times, for the operation as the forecasting engine
match cycles to the production ready tasks and the production available tasks; (Id., ¶ 265, FIG. 29—Variational engine for creating all possible SOP sequences applicable to an ore body: Schematically, the engine is shown as a cyclic series of queries that selects the next SOP in a sequence of SOPs by pulling SOPs from a library of SOPs based on some constraints, which are checked against libraries of trajectories and relationships. However, SOPs are not directly considered. Recall that the knowledge about relationships among CATE elements in a SOP is stored in the library of relationships. Choosing the next SOP is based on availabilities, historical usage, and current state of attributes of SOPs, not SOPs themselves, FIG. 29. Attributes are elements of CATE referred to as first principles. For this reason, this type of mine planning is referred to as from the first principles. The series of questions ensure that relevant information is considered and that relevant information produces the annihilation effect. Selecting the next SOP starts with assembling SOPs that are deemed as available to follow up the latest SOP. Once these are gathered they will offer information on what is required to implement those SOPs (using their attributes). The next few steps check if the attributes are eligible for the particular three-dimensional trajectory of excavation and if capacitance of the mine is considered it will select only attributes that satisfy the constraints. Once the attributes are finalized, the process matches those attributes in the library of SOP. There can be a single SOP, multiple SOPs or no matching SOP so the sequence of SOP extends, or a parallel sequence (or new tree branch) is created (if there are multiple SOPs available) or the sequence terminates, respectively), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control);
generate a ranked list of the production ready and production available tasks automatically from one or more jobs of an imported short- term plan having therein the production ready tasks and the production available tasks (Id., ¶ 215, The second sub-system II automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary (discloses generating a ranked list of tasks), essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … i. “integrated mine plan” means the mine plan across various timelines, such as over short, medium and long term, from a strategic, business and operational perspective, composed with one tool at any chosen level of granularity; … l. “mine plan” means a digital record of a sequence of SOPs, its corresponding schedules representing time layout of actions within a specific operational context (discloses short-term mine plan), and its corresponding space layout of SOPs executed, typically showing the workings of a mining operation, such as excavations and shafts, including any previous workings (legacy), copied and revised from time to time to show any significant changes to the mine workings, some of which are to be produced, on request, in electronic or hard copy format, to a regulatory body or representative such as a mining inspector and certified and signed off by a duly authorised mine surveyor or like professional person), (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer);
output the ranked list of the production ready and production available tasks to an assignment engine to set prioritization regarding completion of task targets associated with the ranked list of production ready and production available tasks (Id., ¶ 47, means for automatically ranking the plans within a specific extraction operational context; [0048] means for automatically integrating the ranked plans as an essential part of the technical mining and production process; and [0049] means for automatically implementing such plans (discloses setting prioritization for ranked plans) as an essential, intermediate and integrated part of the mining and production process with the use of associated mining technical equipment), (Id., ¶ 218, as illustrated in FIG. 3, an automatic process of generating sequences of SOPs from a library, without the need to predict the budget for operations in the future, enables mine planning without considering the budget. More particularly, a planning practice in accordance with the present invention creates the opportunity for a library of standard operating procedures (SOPs) for a particular ore body in the mining operation and constructing all possible sequences of SOPs that can be applied to that ore body. (discloses receiving ready and available tasks associated with an ore body (i.e. mine site)) The advantage of such practice is that mine planning can be prescribed unambiguously (e.g. needing A amount of kWh to dig through B cubic meters of limestone with a particular drill will be the same now and in ten years' time)—in other words, mine plan and practice will not diverge much over time and since creating sequences of SOPs is a computationally finite problem, i.e. a complex problem but with finite solutions, namely a finite number of sequences nonetheless, the mine planning process can be automated by means of a computer), (Id., ¶ 65, The automated means for creating, ranking, integrating and implementing mine plans may comprise: [0065] a variational engine (discloses assignment engine), for generating numerous schedule variations automatically);
and output control signaling to a plurality of hauling work machines at the mine site to control operations of the hauling work machines at the mine site relative to a loading work machine according to varying production rates…wherein one of the specialized management operator graphical user interface or the specialized active work operator graphical user interface on the display is configured to display the ranked list of production ready and production available tasks, and wherein the control signaling to the plurality of hauling work machines to control operations of the hauling work machines includes either increasing or decreasing a number of the hauling machines that receive material from the loading work machine as part of a task, to change a current production rate of the loading work machine during a short-interval timeline toward a predetermined production rate threshold (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., Fig. 58, Figure depicts an interface displaying a ranked list of tasks), (Id., ¶ 34, In addition to the above terms, for purposes of this specification and within the context of the mining industry specifically, the term, with cognate terms having related meanings:
a. “all possible sequences” means all sequences of standard operating procedures (SOPs) that are possible within a specific context or by means of a specific algorithm, analytical data set and/or computing arrangement, and “all possible schedules” and “all possible mine plans” have corresponding meanings;
b. “attributes” for SOP” means an attribute pertaining to a SOP such as time of duration, number of people necessary, equipment being used or its cost;
c. “capabilities, activities, time and environment” (“CATE” (“CATEs”)) means the first principles or the known elements in a mining operation;
d. “code of practice” (“COP” (“COPs”)) means a set of rules which explains how people working in the mining industry should behave typically but not necessarily from a regulatory perspective, with the term “policies” having a similar meaning;
e. “computer-controlled” (mining technical equipment) comprises mining technical equipment operated with the control, assistance and/or aid of a computer; (discloses outputting control signals to hauling machines)
f. “current state of mine” means the snapshot of current state of: CATEs usage, activities, budget, risk, planned vs. actual and audit;
g. “dashboard” means a single, computer-generated canvas dynamically and interactively visualizing past, current and future mining technical and mining supporting information, thereby displaying current state of mine, data health checks, point of action and timeline of plans chosen to execute as well as other qualifications for ROM and LOM, such as risk evaluation, stress test, planned vs. actual and recommendation of improved KPIs), (Id., ¶ 230, FIG. 10—Optimizing from sequences of SOPs means selecting, not seeking, an option: In case of optimization, in terms of the prior art, a miner searches for the best option that fits certain selected or available criteria, but if the miner knows all the possible options he selects the optimal one from the generated ones. At present and in the prior art, mine operations are multi-faceted enterprises where each operational unit has its own KPIs, guiding the relevant mine planning, and each specialist is planning to maximize or minimise their KPIs. It is further unrealistic to expect any unit or specialist to plan for substandard KPIs, as it is not only contrary to their operational or professional obligations, but they also do not have sufficient information to be able to do so, i.e. to perform system planning, but only to plan for their own operations). The rate of production in one part of the mine is therefore typically not synchronized with that of other parts in the mine, causing bottlenecks or delays (FIG. 10 shows, for example, how rates of production have to be adjusted, not maximised, in order to keep the production going). Synchronizing multiple mine plans from multiple data sets in order to guarantee uninterrupted mine operations is simply too complex for human operation. As in the case of scheduling and simulating, the paradigm of optimizing is changed—the miner in the present invention does not generate an option and then assesses whether it is the best, the miner only assesses whether it is the best (since all options have already been calculated). The invention accordingly makes optimization faster. Moreover and in prior art, as soon as miner has an option that satisfies some threshold (discloses threshold production rate), the optimisation is typically terminated, while in the present invention, the miner is able to obtain and assess all (possible) options that satisfy the conditions (again offering awareness of what else is possible), placing the level of optimization well beyond human capability), and to (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all ossible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6) (discloses adjusting a number of haulers to achieve a desired production rate). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)).
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While suggested in at least Fig. 1 and related text, Megannon does not explicitly disclose …perform short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; determine material moved for each of the production ready tasks and the production available tasks; use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on a display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively.
However, through KSR Rationale D (See MPEP 2141(III)(D)), the combination of Megannon and Cervinka discloses …perform short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; determine material moved for each of the production ready tasks and the production available tasks; use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on a display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively.
First, Megannon discloses display outputs of production rates and completion times for mining tasks, as well as display outputs for a current state of the mine compared to a planned state of the mine (Megannon, ¶ 222, FIG. 5—A mine plan is made up of a sequence of SOPs and the corresponding schedules. Once created, the sequence of SOPs can be quantified with attributes representing specific operational context. For example, an SOP named dig could require 4 operators, 3 hours to complete at an estimated budget of $21 000 (see FIG. 5). Aggregating all information on operators needed to execute SOP along the sequence would produce a schedule of activities, including those illustrated in Figure I. Similarly, a miner can obtain other business schedules such as maintenance, survey, etc. schedules by aggregating other attributes with time. A specific combination of SOPs in a specific sequence, the corresponding schedules and a trajectory of excavation in 3D space constitute a mine plan), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., ¶ 189, The first sub-system I (discloses active work engine) as illustrated in Figure B1 includes a non-transitory computer readable storage medium (not shown), storing computer-executable instructions, capable of extracting, amalgamating, translating and integrating big volumes of multi-disciplinary mining technical information/data from the disparate expert technical systems and applications, including computer developed or generated data, into the common, spatially-referenced database for use by the inventory management application to provide integrated mineral asset management in the mineral resource and reserve inventory of the preferred embodiment), (Id., ¶ 215, The second sub-system II (discloses forecast engine) automatically creates all possible mine plans for a given ore body and ranking the plans within a specific mine operational context, for integration and implementation of the ranked plans as an intermediary, essential and integrated part of a computer-assisted mining technical process in the mining and production of minerals on a commercial basis, as illustrated in FIG. 1), (Id., ¶ 216, The conventional standard mine planning practice as applied in the prior art is illustrated in FIG. 2. In contrast and also as illustrated in FIG. 2, is the direction of an automatic mine planning process, in accordance with the invention, with mine planning starting with information available about a specific, preselected ore body. This information, including mining technical information such as shape and grade of mineral deposits, determines possible methods of excavation and therefore a necessary infrastructure to execute such excavation. The information about needed infrastructure allows a miner to create schedules of commissioning, building, and using that infrastructure. The information about schedules allows the miner to plan for the personnel, maintenance, logistics and ultimately the budget necessary for operating a mine. Knowing the applicable schedules possible allows the miner to be aware of what is doable, even though only some of these plans would be implemented), (Id., ¶ 168, FIG. 58 shows the dashboard for all information at the mine site (the so-called “single source of truth” (SST)), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 65, Figure depicts a specialized management operator graphical user interface)
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Further, Cervinka discloses short interval control optimization of tasks in a mining environment as well as determining amounts of material moved for each production ready task (Cervinka, ¶ 21, the method and system generally comprise automatically capturing data with minimal manual data entry or without any manual data entry, such system aiming at eliminating human errors or at least reducing to a minimum such errors. The system is generally configured to capture at least information in relation of to the location of loading/dumping of a vehicle and the net payload that was dumped by the vehicle), (Id., ¶ 94, real-time visibility on the material movement enables short interval control optimization of the mine plan, as discussed in academic papers such as: https://www.gerad.ca/en/papers/G-2016-26/view), (Id., ¶ 78, similarly to LHD embodiments, additional sensors may be installed on the truck 12 to simultaneously monitor the truck activities. Such data is generally communicated to the central data logger and/or to the signal processing unit. The additional sensors may comprise one or more of the following sensors: [0079] inclination sensor of the truck frame. Such sensor generally aims at providing context for the load cells and therefore to increase payload measurement accuracy; [0080] wheel-based vehicle speed sensor. Such sensor typically aims at measuring haulage intensity KPI, since in underground mines the route may significantly vary significantly from one load to the next).
One of ordinary skill in the art would have recognized that applying the known technique of Cervinka would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the short-interval control optimization technique of Cervinka to the specialized mining interface display teachings of Megannon would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such optimization features into similar mining management systems. Further, applying the short-interval control optimization technique of Cervinka to the specialized mining interface displays of Megannon, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed reports of ongoing mining operations according to specific parameters.
Thus, through KSR Rationale D, the combination of Megannon and Cervinka discloses …perform short-interval control (SIC) processing of the production ready tasks and the production available tasks using a forecasting engine and an active work engine, the SIC processing including determining and outputting on the one or more of the plurality of individually specialized operator GUIs production rates and completion times for the production ready tasks and production available tasks using the forecasting engine to perform the SIC processing and/or outputting variations in a current state of the mine site compared to a planned state of the mine site using the active work engine to perform the SIC processing; determine material moved for each of the production ready tasks and the production available tasks; and use historical data to populate one of a specialized short interval control (SIC) operator interface of the plurality of individually specialized operator graphical user interfaces (GUIs), for the operation as the active work engine, wherein the selective and individual display on a display of the plurality of individually specialized operator GUIs includes generating one of a specialized management operator graphical user interface or a specialized active work operator graphical user interface on the display using the forecasting engine and the active work engine, respectively.
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have modified the specialized mining interface elements of Megannon to include the short-interval control (SIC) processing elements of Cervinka in the analogous art of automated monitoring of material movement for the same reasons as stated for claim 1.
Regarding Claim 16, the combination of Megannon and Cervinka discloses …The non-transitory computer-readable storage medium according to Claim 15…
Megannon further discloses …further causing the one or more processors to: responsive to an input to the one of the specialized management operator interface or the specialized active work operator interface, of the plurality of individually specialized operator GUIs, dynamically vary visual indications on the one of the specialized management operator interface or the specialized active work operator interface based on processing of the input by one of the forecasting engine or the active work engine, respectively (Megannon, ¶ 120, FIG. 10 shows the optimizing from sequences of SOPs means selecting, not seeking, an option), (Id., ¶ 121, FIG. 11 shows the planning from sequences of SOPs means selecting a group of mine plans), (Id., ¶ 239, FIG. 14—Representation of a two-step process of extending the variations: Libraries of SOPs and rules applied to them can be numerous, but the repeating two step method of creating variations remains the same, namely when choosing the next SOP to extend the sequence then first, a miner selects available SOPs and then second, goes through them to choose those that are eligible—and the process repeats (see FIG. 14). In a case that there are multiple eligible options, all options are applied and sequence creation continues in parallel for multiple sequences (see FIG. 14, option B). Multiple sequences further can be independent of each other or dependent on each other. Multiple sequence can split (fork) in two or more streams which still belong to a same schedule, such as shown in FIG. 14 option C, which subsequently might or might not join into a single sequence or be split into independent sequences thereafter. In this way, sequences can represent different aspects of mine planning, such as equipment operator tasks, engineering tasks, accounting tasks, IT tasks, etc. Therefore, a mine planning sequence of SOPs can be a single sequence or multiple sequences of SOPs that are dependent or independent of each other, and which occur in parallel at the same space or at different locations, yet due to construction of SOP, the method of selecting the next SOP is the same for a single or multiple sequences (also referred to as invariant) (discloses selecting particular GUIs)), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control), (Id., Figure 2, Figure depicts a display output of a current state of the mine compared to a planned state of the mine), (Id., Fig. 5, Figure depicts a display output of production rates and completion times for tasks), (Id., Fig. 65, Figure depicts a specialized management operator graphical user interface);
Regarding claim 17, the combination of Megannon and Cervinka discloses …The non-transitory computer-readable storage medium according to Claim 16…
Megannon further discloses … wherein said dynamically varying includes dynamically adjusting planned tasks over the first short-interval timeline responsive to the input to the specialized management operator interface (Megannon, ¶ 246, FIG. 18—ADS, complex models and EKG: EKG is a further level of data abstraction where data is collected from ADS and relationships among data are collected from models into a graph, not a table, FIG. 17 and FIG. 18. Each node in a graph represents a column in ADS and a link represents a relationship between the columns (which is typically not explicitly given in ADS). Both, nodes and links, can have attributes which can contain information about other nodes or links (for example derived is an attribute for node representing yield). Each node is referred to as an entity, while a group of entities connected into a sub-graph are referred to as ontologies. Ontologies represent a logical sub-unit, in other words, logical relationships among columns (for example three connected nodes yield, tonnage and time would be an ontology, FIG. 17), which can be defined by a human or a computer. (discloses adjusting tasks based on an input) A library of simple relationships (for example A=B/C) can be created by a human, while a computer can create very complex relationships (for example using neural networks, where such human does not have the necessary model). Models are stored as strings (simple equations or ontologies) or as binary files (complex equations from set of differential equations, K means, decision trees or neural networks). Finally ontologies can be populated with data from ADS and thus create EKG, where EKG contains all the data in ADS but also all the relationships among data in ADS (ADS contains values of a model such as yield but does not contain information about the relationships among features that constitute the model, i.e. how to calculate yield, ontologies do). Concepts such as columns, features and entities can therefore be used interchangeably since it refers to the same data but consumed in a different aspect (see FIGS. 17 and 18), while classical models and ontology models would be referred to as models), (Id., ¶ 30, For purposes of this specification and within the context of computer science and technology, the term, with cognate terms having related meanings: … k. “job scheduler” means software that enables the scheduling and often tracking of specific tasks or units of work (collectively referred to as “jobs”), typically with the ability to start and control such jobs automatically by means of prepared “job-control-language statements”, alternatively by means of similar communication with a human operator, and where such software typically includes a graphical user interface (GUI) and a single point of control).
Regarding claim 20, this claim recites limitations substantially similar to those in claim 6, and is rejected for the same reasons as stated above.
Regarding Claim 21, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …wherein only one production arc is associated with the loading work machine and a specific material processor regardless of how many jobs and tasks are assigned to the pair of the loading work tool and the specific material processor, and wherein the production arc is a component of a production plan that indicates a rate at which the material is to be moved from the loading work machine by the plurality of hauling machines to the specific material processor (Megannon, ¶ 230, FIG. 10—Optimizing from sequences of SOPs means selecting, not seeking, an option: In case of optimization, in terms of the prior art, a miner searches for the best option that fits certain selected or available criteria, but if the miner knows all the possible options he selects the optimal one from the generated ones. At present and in the prior art, mine operations are multi-faceted enterprises where each operational unit has its own KPIs, guiding the relevant mine planning, and each specialist is planning to maximize or minimise their KPIs. It is further unrealistic to expect any unit or specialist to plan for substandard KPIs, as it is not only contrary to their operational or professional obligations, but they also do not have sufficient information to be able to do so, i.e. to perform system planning, but only to plan for their own operations). The rate of production in one part of the mine is therefore typically not synchronized with that of other parts in the mine, causing bottlenecks or delays (FIG. 10 shows, for example, how rates of production have to be adjusted, not maximised, in order to keep the production going). (discloses production arc indicating a rate at which the material is to be moved) Synchronizing multiple mine plans from multiple data sets in order to guarantee uninterrupted mine operations is simply too complex for human operation. As in the case of scheduling and simulating, the paradigm of optimizing is changed—the miner in the present invention does not generate an option and then assesses whether it is the best, the miner only assesses whether it is the best (since all options have already been calculated). The invention accordingly makes optimization faster. Moreover and in prior art, as soon as miner has an option that satisfies some threshold (discloses threshold production rate), the optimisation is typically terminated, while in the present invention, the miner is able to obtain and assess all (possible) options that satisfy the conditions (again offering awareness of what else is possible), placing the level of optimization well beyond human capability), (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all ossible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6) (discloses adjusting a number of haulers to achieve a desired production rate). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)), (Id., ¶ 312, FIG. 49—Supply chain in the regime of knowing all possible mine plans: Similarly for the supply chain, the entire process is prescribed, not predicted. Each plan explicitly states how many people, trucks, bolts, kWh, water, etc. are needed. (discloses a single production arc associated with loading and processing equipment) When a miner looks at the chosen group of plans, the miner can get a sense of what, how much and when is needed).
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Regarding Claim 22, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …wherein none of the tasks have a predetermined rate associated therewith for any given point in time (Id., ¶ 231, FIG. 11—Mine planning from sequences of SOPs means selecting a group of plans: Mine planning in accordance with the invention and with a digital computer or computer-controlled system or method enables the miner to devise all ossible plans, including those that are humanly possible. Having all applicable sequence of SOPs that can be executed allows a miner to rank the sequences based on some common business context. Each sequence has corresponding schedules. Therefore, and if a miner chooses to find the most cost effective mine plan, the miner would simultaneously choose a sequence of SOPs that has a budget schedule resulting in the lowest cost (discloses tasks based on lowest cost (i.e. without a predetermined production rate)) (for example, in FIG. 11, the most cost effective plan results from sequence #11). But the advantage really lies in case a miner wants to repeat that and find the least time consuming plan (for example, in FIG. 11, that would be #4), then find a mine plan that uses the fewest workers (for example, in FIG. 11, that would be #6). This approach would be much faster, as the miner does not need to re-optimize mine plans, only select plans from the existing list. Finally, in case a miner is looking for a mine plan that optimizes time, budget, workers and equipment, then this approach would accelerate the process further (for example, in FIG. 11, the miner would choose #14, since on average it ranks the best)).
Regarding Claim 23, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …wherein the processor is configured to convert a variation task into a planned task for said dynamically adjusting the planned tasks over the first short-interval timeline, and wherein the variation task indicates work performed outside of the short-term plan (Id., ¶ 79, According to a second aspect of the invention there is provided an automated, computer-assisted technical system for creating and exploring all possible mine plans and executing at least one specific technical activity with associated computer-controlled mining technical equipment in accordance with such mine plans, for a given information about a specific ore body and within a job scheduler during mineral mining and production on a commercial basis, the system having automated means for: [0080] generating all applicable trajectories of excavation in three dimensions for a specific ore body with the use of a library of possible trajectories; [0081] generating all possible variations of SOP sequences for each trajectory with the use of a library, containing possible SOPs applicable to such ore body, and a library, containing information pertaining to possible relationships among people, assets, cost and materials available; [0082] generating all possible schedules for such SOP sequences, using attributes of SOPs, for such ore body; [0083] generating all possible mine plans, comprising such SOP sequences with the associated schedules and trajectories of excavation, for such ore body; and [0084] ranking such mine plans by ranking the associated schedules according to a predetermined set of common attributes, such set comprising at least one common attribute and a combination of at least two common attributes), (Id., ¶ 238, A mine plan is defined as a sequence of SOPs with the accompanying set of schedules built from business attributes (assets, resources, cost, safety, risk and so on). Strictly speaking, mine planning sequences of SOPs are variations of SOPs, in particular, variations with repetitions. Variations, as used in the present context, are concepts from combinatorics—variations are permutations of combinations of elements from a set (see FIG. 13). An example of permutations is “12” and “21” (if order matters) for elements in the set (1, 2). An example of combinations of the same size for a set of elements (1, 2, 3) are “12”, “13” and “23” (if order does not matter) (i.e. 12 and 21 are the same combination; but two different permutations). Variations of size 2 from a set (1, 2, 3) are defined as permutations of each combination: 12, 21, 13, 31, 23 and 32. Finally, an example of variations with repetitions from a set (1, 2, 3) would be 12, 21, 13, 31, 23, 32, (as regular variations and) 11, 22, and 33. Total number of variations from an even small set of elements can accordingly result in a lot of different variations. But in practice, constraints for creating variations will always be present, and not all variations can therefore be created. For example, a constraint which requires that the last element is 3, will results in variations: 13, 23 and 33 variations, while other 6 possible variations will be ignored. Existence of such constraints, in the context of this invention, for example, such as possible rules of engagement or implementation restrictions, automatically lead to a significant reduction in the number of possible variations), (Id., ¶ 239, FIG. 14—Representation of a two-step process of extending the variations: Libraries of SOPs and rules applied to them can be numerous, but the repeating two step method of creating variations remains the same, namely when choosing the next SOP to extend the sequence then first, a miner selects available SOPs and then second, goes through them to choose those that are eligible—and the process repeats (see FIG. 14). In a case that there are multiple eligible options, all options are applied and sequence creation continues in parallel for multiple sequences (see FIG. 14, option B). Multiple sequences further can be independent of each other or dependent on each other. Multiple sequence can split (fork) in two or more streams which still belong to a same schedule, such as shown in FIG. 14 option C, which subsequently might or might not join into a single sequence or be split into independent sequences thereafter. In this way, sequences can represent different aspects of mine planning, such as equipment operator tasks, engineering tasks, accounting tasks, IT tasks, etc. Therefore, a mine planning sequence of SOPs can be a single sequence or multiple sequences of SOPs that are dependent or independent of each other, and which occur in parallel at the same space or at different locations, yet due to construction of SOP, the method of selecting the next SOP is the same for a single or multiple sequences (also referred to as invariant)).
Regarding Claim 24, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …wherein a first short-interval control (SIC) window shown on the display shows only available tasks, and wherein the processor is configured to convert all ready tasks to available tasks for a second short-interval control (SIC) window that occurs immediately after the first short-interval control (SIC) window such that the converted tasks are regarded as active work tasks for the plurality of hauling work machines (Megannon, ¶ 308, FIG. 45—Learning and improving mine plans by planned vs. actual analysis: Mine plans are automatically generated but they can be automatically corrected as well. Over time, the difference between planned and actual execution time, budget or need for people for any SOP can be quantified and the discrepancy can be integrated in the next use of the SOP, thus improving the planning), (Id., Fig. 45, Figure depicts converting ready tasks to available/active tasks).
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Regarding Claim 25, the combination of Megannon and Cervinka discloses …The management engine system according to Claim 1…
Megannon further discloses …mining block locks are directly encoded in jobs and the tasks (Id., ¶ 183, Continuous real-time monitoring of the attributes within the amalgamated database, at a level of granularity where a relevant attribute changes within a single geo-x block, initiates a re-intersection of the related/affected blocks and an update to the database through the process of journal processing 17), (Id., ¶ 184, Based on the processing of journal transactions within the inventory management application, the mineral resource inventory is populated, and one can report 21 on mineral asset status at any level of granularity. Reporting principles are subject to the relevant regulatory reporting code and company analysis requirements 18. Geo-x blocks are stored within inventory/stock storage bin locations in a hierarchy that reflects the resource and reserve code based classifications from lowest confidence to highest, and lowest grade to highest in any combination dependent on the configured reporting code. Reporting takes into consideration current and historical status records 20 and is aware of the latest reporting block updates 19, which have been processed as journal transactions 17. The mineral asset status 21 is updated by calling on each affected reporting block update 19 to provide the current and historical status records 20 with regard to changes in the mineral asset state and status).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Olson, U.S. Patent No. 8,190,173 disclose a computerized mine production system.
Watkins, U.S. Publication No. 2016/0314421 discloses market-driven mining optimization.
Oyarzun Gonzalez et al., 2022/0335345 discloses a method, device and system for managing mining facilities.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS D BOLEN whose telephone number is (408)918-7631. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM PST.
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/NICHOLAS D BOLEN/ Examiner, Art Unit 3624
/HAMZEH OBAID/ Primary Examiner, Art Unit 3624 May 28, 2026.
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