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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Status of Claims
This communication is a Non-Final Office Action in response to Applicant’s RCE for application number 18/400,920 received on 07/31/2026.
In accordance with Applicant’s amendment, claims 1-20 are amended, currently pending and have been examined.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Regarding the §112(f) claim interpretation and corresponding §112(b) and §112(a) rejections previously applied to the claims, upon review of the amended claims, both are withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pg. 9): “Step 2A, Prong Two: Applicant asserts that, even assuming for argument, that certain data-comparison limitations recite a mental process, the claim as a whole integrates any such process into a practical application. As amended, claim 1 recites that the activation signal controlling the physical auto-valve is "generated based on the reconciliation report". As such, the signal that controls the physical addition of acid to a raffinate directed at a specific, physically stacked lift of ore is derived directly from, and dependent upon, the computerized comparison of the forecast model block, district model block, and target block characteristics. This is not insignificant extra-solution activity. In fact, this is the specific mechanism by which the results of the block-model comparison are applied to control real-world leaching equipment in real time. See Specification paragraphs [0071]-[0072]. The Specification confirms that, absent this automation, mine-model reconciliation was historically performed manually, on paper, over a period of weeks, with attendant human calculation errors (Specification paragraphs [0002]-[0005]). Therefore, Applicant asserts that the claim recites a specific means of applying model-comparison output to a technology-based problem, integrating the claimed features into a practical application. See MPEP § 2106.04(d)(I); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). Step 2B: For similar reasons, the claim recites significantly more than any abstract idea.”. In response, Examiner respectfully disagrees and notes that the present claims do not provide an analogous improvement to the computer to that of McRO. The present claims are not directed to the automation of specific animation tasks that previously could only be performed subjectively by humans. Therefore, Examiner respectfully disagrees with Applicant's assertion that the present claims are directed to statutory subject matter in view of McRO.
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims are primarily raised in support of the amendments. The amendments and supporting arguments are believed to be fully addressed in the updated §103 rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, and 5-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1-3, and 5-21 are directed to a Method (i.e., Process). Therefore, the claims are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claim 1 recites a method for finding shovel load locations between a period of time based on shovel load data from truck load data from a truck load of material. As drafted, the limitations recited by claim 1 fall under the “Mental Processes” abstract idea grouping by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion, or with the help of pen and paper).
The abstract limitations recited by independent claim 1 are: “finding shovel load locations in a block between a period of time based on shovel load data from truck load data from a truck load of material; associating the shovel load locations with a forecast model block and a district model block; selecting a plurality of shovel loads that are associated with the forecast model block and the district model block, based on the shovel load locations; matching the plurality of shovel loads with the truck load; aggregating the plurality of shovel loads into the truck load, based on the truck load data, shovel load characteristics of the plurality of shovel loads being associated with forecast model block characteristics of the forecast model block and district model block characteristics of the district model block; comparing forecast model block characteristics of the forecast model block and district model block characteristics of the district model block with target block characteristics of a target block; creating a reconciliation report of the target block characteristics of the target block based on the forecast model block characteristics and the district model block characteristics;”. But for the additional elements recited in the claim limitations, the steps recited in the claim limitations could be accomplished mentally, such as by human observation, evaluation, judgement, opinion, or with the help of pen and paper.
Dependent claims 2, 3, and 5-21 further narrow the abstract idea and do not introduce further additional elements for consideration.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
With respect to the limitations for stacking ore from the plurality of shovel loads at a lift height, based on the reconciliation report; and performing percolating of at least a portion of leaching from the lift height, in response to an activation signal generated based on the reconciliation report, the activation signal activating an auto-valve to add acid to a raffinate directed to the lift height, to optimize metal production from independent claim 1, these limitations fail to integrate the abstract idea into a practical application because at most, they amount to insignificant extra-solution activity (e.g., insignificant application), which does not integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Dependent claims 2, 3, and 5-21 recite the same abstract ideas (“mental processes”) as the independent claim along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
With respect to the limitations for stacking ore from the plurality of shovel loads at a lift height, based on the reconciliation report; and performing percolating of at least a portion of leaching from the lift height, in response to an activation signal generated based on the reconciliation report, the activation signal activating an auto-valve to add acid to a raffinate directed to the lift height, to optimize metal production from independent claim 1, these limitations fail to add significantly more because at most, they amount to insignificant extra-solution activity (e.g., insignificant application), which does not add significantly more to the judicial exception. See MPEP 2106.05(g): Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.
Furthermore, the auto-valve is considered well-understood, routine and conventional, as recognized by Applicant’s own admission in the Remarks dated 07/15/2026 at page 7, where Applicant notes: “Applicant amends claim 1 to replace "a leaching device" with "an auto-valve," which is a specific, well-understood mechanical structure recited together with its specific function and connection.”. Therefore, the auto-valve fails to add significantly more to the abstract idea.
Dependent claims 2, 3, and 5-21 recite the same abstract ideas (“mental processes”) as the independent claim along with further steps/details falling under the scope of the abstract idea itself, along with the same or substantially same additional elements addressed.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Accordingly, claims 1-3, and 5-21 are rejected under 35 USC 101.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 of this title, 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, 2, 7-13, 15, and 20 are rejected under 35 U.S.C. §103 as unpatentable over Innes et al. (US 20130272829 A1, hereinafter “Innes”), in view of Geislinger et al. (US 11521138 B1, hereinafter “Geislinger”),
Regarding claim 1: Innes teaches a method ([0001] The present invention relates to methods and systems for tracking lumped masses of material.) comprising:
finding shovel load locations in a block between a period of time based on shovel load data from truck load data from a truck load of material; ([0057] The excavators 14 may include a mass sensor on the excavator bucket with GPS location of the end effector. On-board GPS of haul trucks 16 (e.g. from the Modular Mining dispatch system available from Modular Mining Systems Inc) may be used to determine where the material was unloaded. Examiner notes that one of ordinary skill in the art would consider a block to be a section of the mine, therefore, determining where the material was unloaded is equivalent to finding a shovel load location in a block, or section, of the mine.);
associating the shovel load locations with a forecast model block and a district model block; ([Fig. 1] Grade Block 1, Excavator 1, Grade Block 2, Excavator 2, Grade Block K, Excavator K; [0029] FIG. 1 is a schematic representation of a system in which material is excavated from a plurality of grade blocks and transported by haul trucks to a plurality of stockpiles; [0048] A plurality of grade blocks 12 is excavated by a set of excavators 14. The material from the excavators 14 is loaded onto one or more haul trucks 16, which then unload the material at one or more locations such as run of mine (ROM) stockpiles 18 or a crusher.)
selecting a plurality of shovel loads that are associated with the forecast model block and the district model block, based on the shovel load locations; ([0029] FIG. 1 is a schematic representation of a system in which material is excavated from a plurality of grade blocks and transported by haul trucks to a plurality of stockpiles;; [Fig. 1] Grade Block 1-K, Excavator 1-K, Haul Truck; [0003] An example of a production chain and an operational process chain is an open-pit iron-ore mine. In open-pit iron-ore mining, material is excavated from specific locations after being blasted. The amount excavated from each location is usually determined by production requirements to meet a certain level of quality and quantity of material. The excavated material is transported by haul trucks directly to dumping stations for primary crushing or to stockpiles, from which the material is removed for further processing. Material is also removed to enable the development of the pit to access future deposits. Such material is transported to dumping locations which may or may not be permanent. The material dumped may be used as fill for previous excavations.; [0007] In broad terms the methods described herein represent lumped masses of material probabilistically. A variety of sensors are used to measure different properties of the material at spatially distinct locations as the material is moved through a production chain or operational process chain. An appropriate framework for ensuring consistent data fusion is also described. Examiner notes that one of ordinary skill in the art would reasonably consider the forecast model block, and the district model block from Applicant’s claims as equivalent to different locations, also equivalent to the different locations in Inness (Grade Blocks 1-K.);
matching the plurality of shovel loads with the truck load; ([0048] FIG. 1 gives an example of an open pit production chain 10 in which material is tracked using the systematic approach outlined in this specification. A plurality of grade blocks 12 is excavated by a set of excavators 14. The material from the excavators 14 is loaded onto one or more haul trucks 16, which then unload the material at one or more locations such as run of mine (ROM) stockpiles 18 or a crusher.);
aggregating the plurality of shovel loads into the truck load, based on the truck load data, shovel load characteristics of the plurality of shovel loads being associated with forecast model block characteristics of the forecast model block and district model block characteristics of the district model block; ([0106] FIG. 8A is an example of how multiple Kalman filters may be used in the mining application. A shovel observation 420 of mass and volume, together with a shovel system estimate 421, is provided to Kalman filter 422, which integrates the observation with previous estimates and provides the updated estimate to a haul truck estimate 423. Kalman filter 425 uses the estimate 423 together with haul truck observations 424 to update the haul truck estimate and provides the update to a stockpile system estimate 426.);
comparing forecast model block characteristics of the forecast model block and district model block characteristics of the district model block with target block characteristics of a target block; (Fig. 1: Grade Block 1, Excavator 1, Grade Block 2, Excavator 2, Grade Block K, Excavator K; [0162] The mass and volume states at each unique location are correlated through density.);
creating a reconciliation report of the target block characteristics of the target block based on the forecast model block characteristics and the district model block characteristics; ([Fig.14]; [0163] FIG. 14 gives a numerical example of how the process of fusing information at later stages improves estimates at earlier locations. When the excavator update occurs (fusion of new mass and volume data at that location), the amount of material estimated to be in the excavator bucket increases (ie bucket mass increases from 348 to 393). The variance on these estimates also decreases dramatically, which suggests the update was of high quality data. This update subsequently decreases the amount of material estimated to remain in the grade block (ie from 3994 to 3949) as well as improving the quality of this estimate. This effect propagates through the filter as the material is transferred from state to state.; [0155] By means of the correlations developed during this process, an algorithm may be developed which can reconcile material at later stages of the mining process back to the reconciled stage. The reconciliation may apply both to extensive properties such as mass and volume and to intensive properties such as chemical composition and fragmentation levels.; [0156] The process of reconciling extensive properties is inherent in the described modelling method using an augmented state Kalman filter and the reconciliation states described above. The variances and covariances in the augmented state Kalman filter covariance matrix (P) may be used to isolate specific correlations. This can, for example, provide information on how much material in a particular stockpile has come from a specific grade block.);
stacking ore from the plurality of shovel loads at a lift height, based on the reconciliation report; ([0003] The excavated material is transported by haul trucks directly to dumping stations for primary crushing or to stockpiles; [0095] Fragmentation=Ore Fragmentation Level; [0051] In order to track the lumped masses through the production chain, some information about the lumped masses is required. The equipment such as excavators 14 and haul trucks 16 may be provided with sensors that monitor one or more characteristics of the material. The characteristics include extensive properties such as mass and volume that define the amount of material in a lumped mass. The measured characteristics may also include intensive properties such as chemical composition or a fragmentation level of the material.; [0097] Mass may be used as the measure in estimating other intensive material properties when combining lumped masses of material. An alternative selection would be to use the volume of material present in lumped masses. Mass and volume represent two extensive qualities of lumped material which can measure the quantity of the material present at any location. Typically however, mass is a more readily measurable quantity compared to volume. The majority of volume estimation techniques use a 3D point cloud of the surface (provided by an external sensor), which then can be used to either triangulate to create a surface projected against a plane, or through a point-axis integration method to determine a volume of material under this surface. This volume calculated is the bulk volume. Bulk volume is the volume of area the material occupies including the gap spaces between lumped material. Volume can be extrapolated from this by determination of the bulk factor.; [0155] By means of the correlations developed during this process, an algorithm may be developed which can reconcile material at later stages of the mining process back to the reconciled stage. The reconciliation may apply both to extensive properties such as mass and volume and to intensive properties such as chemical composition and fragmentation levels.; [Claim 11] The method of claim 10 wherein the intensive lumped mass property comprises at least one of a chemical composition, density and a fragmentation level.).
Innes doesn’t explicitly teach:
stacking ore from the plurality of shovel loads at a lift height,
and performing percolating of at least a portion of leaching from the lift height, in response to an activation signal generated based on the reconciliation report, the activation signal activating an auto-valve to add acid to a raffinate directed to the lift height, to optimize metal production.
Geislinger teaches:
stacking ore from the plurality of shovel loads at a lift height, ([Column 3, Lines 7-11] the ore is fed onto mobile stacking conveyors and is deposited in a layer whose thickness (lift height) may be determined by the average leach characteristics of the ore being stacked (usually between 15-30 feet high).);
and performing percolating of at least a portion of leaching from the lift height, ([Column 30, Lines 27-32] A more targeted addition of acid may be particularly feasible in the case of acid addition to agglomeration where signals from a system (e.g., an over-the-belt analyzer) may be used to control acid addition rates to agglomeration depending on the mineralogy of the ore being treated.; [Column 31, Lines 12-15] The system may use the data provided by Darcy's law to determine, for example, if a reagent should be added to the leach stockpile to help increase the percolation and improve leaching);
in response to an activation signal generated based on the reconciliation report, the activation signal activating an auto-valve to add acid to a raffinate directed to the lift height, to optimize metal production. ([Column 16, Line 35 – Column 17, Line 3] The forecast may be used to optimize future mine plans. Based on the forecast, the optimization process may be applied to inputs to suggest impactful changes. The system may suggest DUL (days under leach) cycles based on optimal forecasted production. The system may provide cross-pile forecasts that may drive optimization of placements between leach stockpiles. The system may determine an optimal diversion of raffinate between stockpiles to maximize leach production, so the system may send a signal to the raffinate dispenser or router to divert the raffinate over a different pathway. The system may determine dump allocations to lifts/piles based on forecasted production, so the system may send signals to the routing system (or directly to automated haul trucks) to change the routing of the haul trucks. The ore map tool may determine areas to automatically re-leach based on remaining Cu calculations, so the system may send a signal to a dispenser to automatically start a re-leaching process. The ore map may also determine areas to be re-mined based on a determination that re-mining efficiencies may exist, so the system may send a signal to a scheduling system or mining machine to start re-mining a certain area. In various embodiments, the system may determine that a particular stockpile on a particular area of a stockpile may benefit from increased acid addition. As such, the system may send a signal to an auto-valve that could be directed to open to a setpoint so that additional acid is added to the raffinate destined for a particular area under leach or to be leached. In various embodiments, the system may also determine (based on previous data or a column test model) that ore placed in a particular area of a stockpile may benefit from the addition of an additive or a combination of additives, microbes, and leach catalysts. As such, the system may send a signal to one or more auto-valves that could be directed to open to setpoints, so that one or a combination of additives, microbes, and leach catalysts may be added to the raffinate bound for a given area of the leach stockpile.; [Column 20, Lines 3-18] As set forth in FIG. 4, the backcast may reconcile the forecast (what is expected to be produced) with what is actually produced. The backcast and forecast may use the same predictive model (so both the backcast and forecast learn from the same information), in order to properly reconcile the actual and predicted performance. A mine may not operate as expected due to various changes (e.g., unexpected variance in operating norms or parameters driven by human deviation from forecast plans), so the actual performance of the mine may not match the predicted performance. For example, the forecast may include 1000 pounds of copper, but the actual production is 750 pounds of copper. If any deviations exist between the output of the forecast engine and the actual data, the system may evaluate the deviations (e.g., using data science) to generate a product backcast.; [Column 20, Lines 19-46] By adjusting the variables to the actual amounts, the backcast enables breaking down the deviations by specific causes. Determining the specific causes helps to determine how the predictive model and/or forecast may be changed to improve the accuracy of the predictive model and/or forecast. In the above example, the system may use the backcast and the same predictive model to determine that adjusting the days under leach may account for 50 pounds, adjusting for the head grade may account for 75 pounds, and adjusting for the raffinate flow may account for 125 pounds. Because the forecast period is already completed, the system can use observed actual information to determine which parameters to adjust to better match the desired forecast. Therefore, the backcast may determine how to adjust the parameters to compensate for the 250 pound deviation. The adjustments may be provided in a report, displayed on a screen, provided as suggestions and/or may be used to initiate practical applications. For example, the adjustments may result in sending a signal to a machine to adjust the machine's operations in line with achieving the desired production.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Innes with Geislinger’s feature(s) listed above. One would’ve been motivated to do so in order to adjust leaching operations and to optimize metals production (Geislinger; [Column 1, Lines 9-10]). By incorporating the teachings of Geislinger, one would’ve been able to stack ore on a lift height and perform percolating as a result of a signal sent to an auto-valve to optimize metal production, based on the reconciliation report.
Regarding claim 2: Modified Innes teaches the method of claim 1. Innes further teaches:
wherein the forecast model block represents a forecast block and the district model block represents a district block. ([0003] An example of a production chain and an operational process chain is an open-pit iron-ore mine. In open-pit iron-ore mining, material is excavated from specific locations after being blasted. The amount excavated from each location is usually determined by production requirements to meet a certain level of quality and quantity of material. The excavated material is transported by haul trucks directly to dumping stations for primary crushing or to stockpiles, from which the material is removed for further processing. Material is also removed to enable the development of the pit to access future deposits. Such material is transported to dumping locations which may or may not be permanent. The material dumped may be used as fill for previous excavations.; [0007] A variety of sensors are used to measure different properties of the material at spatially distinct locations as the material is moved through a production chain or operational process chain.; [0029] FIG. 1 is a schematic representation of a system in which material is excavated from a plurality of grade blocks; [0162] FIG. 13 shows a simple experimental example which involves estimating mass and volume from a simulated grade block to a ROM stockpile. Examiner notes that one of ordinary skill in the art would reasonably interpret the different material locations disclosed by Innes as equivalent to the forecast model block and the district model block from Applicant’s claim.).
Regarding claim 7: Modified Innes teaches the method of claim 1. Innes further teaches:
wherein the reconciliation report includes the differences from the target block characteristics of the target block with the district model block characteristics. (Fig. 14: Predicted Excavator Ore Removal, Excavator Observation Update).
Regarding claim 8: Modified Innes teaches the method of claim 1. Innes further teaches:
further comprising determining at least one of the shovel load data that is missing or the shovel load data that does not match the truck load data. ([0198] the material from the excavator bucket is intended to be wholly transferred to the haul truck. However, this is not always the case and there are inevitably some unintended material transfers or losses. Losses from the excavator bucket to the haul truck are calculated in this example once the haul truck is full. Examiner notes that one of ordinary skill in the art would reasonably consider calculating the material loss once the truck is full as equivalent to as determining the shovel load data does not match the truck data.)
Regarding claim 9: Modified Innes teaches the method of claim 1. Innes further teaches:
further comprising backfilling the shovel load data that is missing by using at least one of shovel cut data, spatial data, prediction data, average data from past truck loads, or last known data from the past truck loads. ([0179] Following the grade block initialisation, this section details the models describing the interaction between the excavator bucket and the grade block. Data fusion from sensor inputs is also included.; [0180] A grade-block-removed state is used in place of the actual grade block state described in the previous section. This state is used to store the current amount of material estimated to have been removed from the current grade block.; [0183] The following models use the filter prediction step equations to describe the process of removing material from the grade block. In this case, the material is added to the grade block removed states.; [0190] This section details unloading the excavator bucket into a waiting haul truck.; [0198] In this scenario, the material from the excavator bucket is intended to be wholly transferred to the haul truck. However, this is not always the case and there are inevitably some unintended material transfers or losses. Losses from the excavator bucket to the haul truck are calculated in this example once the haul truck is full. To ensure that the correct correlations are maintained, a temporary loss state is used to transfer the estimated lost material.; [0199] Once the loss state is initialised with the expected losses and the variance associated with these losses it is applied to the current system model. In this particular example it is assumed the material lost from the bucket will be fully returned to the original grade block, thus subtracting from the estimated material removed. Examiner notes that one of ordinary skill in the art would reasonably interpret returning the material lost and subtracting from the estimated material removed from the excavator bucket once it is returned, as backfilling the shovel load data once the material is returned to the shovel location, by using previous shovel cut data, and past truck load data.)
Regarding claim 10: Modified Innes teaches the method of claim 1. Innes further teaches:
further comprising backfilling the shovel load data that is missing by using shovel cut data from shovel cut files from the period of time and over the shovel load locations. ([0054] The calculation of bucket-fill volume information is currently available in solutions offered by Motion Metrics International Corp.; [0063] 3-D geometric information may be formed by software running on the host computer using time-stamped data from the radar and camera of the hybrid sensor.; [0179] Following the grade block initialisation, this section details the models describing the interaction between the excavator bucket and the grade block. Data fusion from sensor inputs is also included.; [0180] A grade-block-removed state is used in place of the actual grade block state described in the previous section. This state is used to store the current amount of material estimated to have been removed from the current grade block.; [0183] The following models use the filter prediction step equations to describe the process of removing material from the grade block. In this case, the material is added to the grade block removed states.; [0190] This section details unloading the excavator bucket into a waiting haul truck.; [0198] In this scenario, the material from the excavator bucket is intended to be wholly transferred to the haul truck. However, this is not always the case and there are inevitably some unintended material transfers or losses. Losses from the excavator bucket to the haul truck are calculated in this example once the haul truck is full. To ensure that the correct correlations are maintained, a temporary loss state is used to transfer the estimated lost material.; [0199] Once the loss state is initialised with the expected losses and the variance associated with these losses it is applied to the current system model. In this particular example it is assumed the material lost from the bucket will be fully returned to the original grade block, thus subtracting from the estimated material removed. Examiner notes that one of ordinary skill in the art would reasonably interpret returning the material lost and subtracting from the estimated material removed from the excavator bucket once it is returned, as backfilling the shovel load data once the material is returned to the shovel location, by using previous shovel cut data, and past truck load data.)
Regarding claim 11: Modified Innes teaches the method of claim 1. Innes teaches:
and backfilling the shovel load data that is missing with shovel cut data having the first characteristics and a percentage of the second characteristics. ([0095] The properties may include: M=Mass, V=Volume, Fe=Iron %, SiO.sub.2=Silicon Dioxide %, Al.sub.2O.sub.3=Aluminium Oxide %, Fragmentation=Ore Fragmentation Level; [0179] Following the grade block initialisation, this section details the models describing the interaction between the excavator bucket and the grade block. Data fusion from sensor inputs is also included.; [0180] A grade-block-removed state is used in place of the actual grade block state described in the previous section. This state is used to store the current amount of material estimated to have been removed from the current grade block.; [0182] For example, bucket mass and volume are initialized; [0183] The following models use the filter prediction step equations to describe the process of removing material from the grade block. In this case, the material is added to the grade block removed states.; [0190] This section details unloading the excavator bucket into a waiting haul truck.; [0198] In this scenario, the material from the excavator bucket is intended to be wholly transferred to the haul truck. However, this is not always the case and there are inevitably some unintended material transfers or losses. Losses from the excavator bucket to the haul truck are calculated in this example once the haul truck is full. To ensure that the correct correlations are maintained, a temporary loss state is used to transfer the estimated lost material.; [0199] Once the loss state is initialised with the expected losses and the variance associated with these losses it is applied to the current system model. In this particular example it is assumed the material lost from the bucket will be fully returned to the original grade block, thus subtracting from the estimated material removed. Examiner notes that one of ordinary skill in the art would reasonably interpret subtracting the estimated material removed from the excavator bucket once it is returned, as backfilling the shovel load data once the material is returned to the shovel location, by using previous shovel cut data, and past truck load data.)
Innes doesn’t teach:
further comprising: overlaying a shovel cut progress polygon over a plurality of blocks within a block model of a mine, wherein the plurality of blocks include at least one of the forecast model block, the district model block or the target block; determining a first subset of the plurality of blocks that are fully contained within the shovel cut progress polygon, wherein the first subset of the plurality of blocks have first characteristics;
determining a second subset of the plurality of blocks that are partially contained within the shovel cut progress polygon, based on one or more vertices or centroids being within the shovel cut progress polygon, wherein the second subset of the plurality of blocks have second characteristics;
determining a second subset of the plurality of blocks that are partially contained within the shovel cut progress polygon, based on one or more vertices or centroids being within the shovel cut progress polygon, wherein the second subset of the plurality of blocks have second characteristics; and backfilling the shovel load data that is missing with shovel cut data having the first characteristics and a percentage of the second characteristics.
Geislinger teaches:
further comprising: overlaying a shovel cut progress polygon over a plurality of blocks within a block model of a mine, wherein the plurality of blocks include at least one of the forecast model block, the district model block or the target block; ([Column 7, Lines 12-16] The ore map may comprise section mapping data that includes creating a polygon map. The polygon map may include correcting for overlapping polygons. The polygon map may include creating shapes of an overlap portion of polygons with known ore characteristics.);
determining a first subset of the plurality of blocks that are fully contained within the shovel cut progress polygon, wherein the first subset of the plurality of blocks have first characteristics; ([Column 7, Lines 13-16] The polygon map may include correcting for overlapping polygons. The polygon map may include creating shapes of an overlap portion of polygons with known ore characteristics..; [Column 8, Lines 48-65] In various embodiments, the system may include a method comprising receiving, by a processor, ore placement data for a stockpile, wherein the ore placement data may include dispatch data, haul truck sensor data (e.g., from one or more of the sensors in FIGS. 7A-7F), polygon data (e.g., GIS), assay data and mineralogy data; determining, by the processor, ore placement locations for the stockpile, based on the ore placement data; determining, by the processor, an amount of mineral extracted from the stockpile, based on historical leaching process data for the stockpile; and determining, by the processor, recovery locations for recoverable amounts of mineral in the stockpile, based on historical leaching process data for the stockpile. The mineralogy data may be from the block model and included in the mine material tracking data. The ore placement data may include the estimated material extracted from the stockpile and may include data from a column test predictive model.; [Column 9, Lines 9-19] Determining the total mineralogy for the stockpile may include aggregating mineralogy details to a section level by combining MMT truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level; obtaining maximum days under leach (DUL) for each section at the section level by using irrigation data over all stockpiles at the section level; and determining an intermediate ore map for a stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon.);
determining a second subset of the plurality of blocks that are partially contained within the shovel cut progress polygon, ([Column 8, Lines 48-65] In various embodiments, the system may include a method comprising receiving, by a processor, ore placement data for a stockpile, wherein the ore placement data may include dispatch data, haul truck sensor data (e.g., from one or more of the sensors in FIGS. 7A-7F), polygon data (e.g., GIS), assay data and mineralogy data; determining, by the processor, ore placement locations for the stockpile, based on the ore placement data; determining, by the processor, an amount of mineral extracted from the stockpile, based on historical leaching process data for the stockpile; and determining, by the processor, recovery locations for recoverable amounts of mineral in the stockpile, based on historical leaching process data for the stockpile. The mineralogy data may be from the block model and included in the mine material tracking data. The ore placement data may include the estimated material extracted from the stockpile and may include data from a column test predictive model.; [Column 9, Lines 9-19] Determining the total mineralogy for the stockpile may include aggregating mineralogy details to a section level by combining MMT truckload data at a dump level, MMT imputation data at the dump level and MMT final section mapping data at the dump level and the section level; obtaining maximum days under leach (DUL) for each section at the section level by using irrigation data over all stockpiles at the section level; and determining an intermediate ore map for a stockpile by combining the aggregating mineralogy details, the maximum DUL for each section and a primary new section polygon. Examiner notes that one of ordinary skill in the art would reasonably consider the multiple polygons disclosed by Geislinger as equivalent to a second subset of the plurality of blocks as disclosed in Applicant’s claim.);
based on one or more vertices or centroids being within the shovel cut progress polygon, ([Column 10, Lines 9-15] The method may further include determining which sections are economically viable for recovery via irrigation (which may include irrigation by drip, wobbler, or targeted injection of raffinate or raffinate enhanced with acid, additives, or oxygen) based on a number of contiguous high-remaining sections and the proximity of the high-remaining sections to a top lift. Examiner notes that one of ordinary skill in the art would reasonably consider the contiguous sections as intersecting, which is equivalent to vertex/vertices.);
wherein the second subset of the plurality of blocks have second characteristics; ([Column 7, Lines 13-16] The polygon map may include correcting for overlapping polygons. The polygon map may include creating shapes of an overlap portion of polygons with known ore characteristics.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Innes & Deenathayalan with Geislinger’s feature(s) listed above. One would’ve been motivated to do so, so that information from the trucks may be compared with defined GIS Polygons and dispatch information to provide more accurate dump location precision (Geislinger; [Column 27, Lines 48-51]). By incorporating the teachings of Geislinger, one would’ve been able to overlay polygons over a plurality of blocks to facilitate planning and reporting at the mine.
Regarding claim 12: Modified Innes teaches the method of claim 1. Innes further teaches:
further comprising determining the target block corresponding to the shovel load locations; ([0152] comparing the actual quality and amount of material mined from a designated area compared to the expected output of that area.).
Innes doesn’t teach:
and determining the percentage of the target block that was mined.
Geislinger further teaches:
and determining the percentage of the target block that was mined. ([Column 1, Line 67 – Column 2, Line 1] around 15% of the contained copper; [Column 3, Lines 54-57] The copper recovered after this first leach cycle is called first cycle recovery and represents most (about 80%) of the copper that may be recovered from that ore.; [Column 10, Lines 6-9] The method may further include calculating the recoverable amounts of mineral at the section-level by deducting estimated recovered mineral from initial placements.; [Column 38, Lines 25-28] The MMT tool may integrate with the block model data to provide real-time tracking (e.g., past 24 hours percent TCu deliveries) and improved process modeling and analysis (e.g., past 24 hours percent TClay deliveries).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Geislinger’s feature(s) listed above. One would’ve been motivated to do so, so the leach recovery from chalcopyrite minerals may be improved (Geislinger; [Column 2, Lines 8-9]). By incorporating the teachings of Geislinger, one would’ve been able to determine the percentage of a block that was mined.
Regarding claim 13: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising at least one of:
determining a percent of a block within the block model that was mined, wherein the block includes at least one of the forecast model block, the district model block or the target block;
forecasting, using a mine plan with user-defined table functions (UDTFs), areas of polygons to be mined first over a period of time;
displaying mined areas overlayed on a mine plan, wherein the mine plan includes areas that should have been mined;
or creating area categories in a mine plan as at least one of mined as planned, planned not mined, mined not planned or routed outside of the mined plan.
Geislinger further teaches:
further comprising at least one of:
determining a percent of a block within the block model that was mined, wherein the block includes at least one of the forecast model block, the district model block or the target block;
forecasting, using a mine plan with user-defined table functions (UDTFs), areas of polygons to be mined first over a period of time;
displaying mined areas overlayed on a mine plan, wherein the mine plan includes areas that should have been mined; ([Column 9, Lines 49-64] The method may further include providing a visualization of the recovery locations for the recoverable amounts of mineral in the stockpile. The method may further include determining at least one of x,y,z coordinates or time-series layering information for the recovery locations for the recoverable amounts of mineral in the stockpile. The method may further include providing a visualization of section mineralogy populated on a map of each of the stockpiles. The method may further include filtering of the sections by at least one of lift, stockpile or mineralogy composition. The method may further include displaying aggregated values for at least a subset of the sections. The method may further include defining boundaries of the stockpiles and the sections based on polygons recorded in a geographic information system (GIS).);
or creating area categories in a mine plan as at least one of mined as planned, planned not mined, mined not planned or routed outside of the mined plan.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Geislinger’s feature(s) listed above. One would’ve been motivated to do so in order to include displaying aggregated values for at least a subset of the sections (Geislinger; [Column 9, Lines 60-61]). By incorporating the teachings of Geislinger, one would’ve been able to display mined areas on a mine plan.
Regarding claim 15: Modified Innes teaches the method of claim 1. Innes further teaches:
further comprising determining, using recovery data, that a mine plan recovered an amount of metal that was planned. ([0003] The amount excavated from each location is usually determined by production requirements to meet a certain level of quality and quantity of material.; [0065] an in-ground model may provide a description of the disposition of shale, Banded Iron Formation (BIF) and iron ore zones; Fig. 14: Predicted Excavator Ore Removal, Excavator Observation Update; [0152] Reconciliation in the mining industry is generally considered as the process of comparing the actual quality and amount of material mined from a designated area compared to the expected output of that area. Reconciliation is performed usually on monthly (or longer) schedules).
Regarding claim 20: The combination of Innes, Deenathayalan and Geislinger teach the method of claim 1. Innes doesn’t teach:
further comprising determining, using a cutoff file, a threshold grade for a type of the material for routing the material to at least one of a processing facility or a processing area.
Geislinger further teaches:
further comprising determining, using a cutoff file, a threshold grade for a type of the material for routing the material to at least one of a processing facility or a processing area. ([Column 26, Lines 13-18] Ores that contain un-economic percentages of copper are said to be “below-cutoff-grade” materials. Because the processing cost via froth flotation and smelting is higher than that for heap leaching, ores that are below the “mill cutoff grade” may often be economically processed via leaching.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Geislinger’s feature(s) listed above. One would’ve been motivated to do so, so that the processes, mechanics and economics of ore routing may be optimized by utilization of models, simulators and engines that are adjustable according to metal pricing and market conditions (Geislinger; [Column 26, Lines 9-13]). By incorporating the teachings of Geislinger, one would’ve been able to use a cutoff grade to route material for processing.
Claims 3, 5, 6, 16, 18, 19, and 21 are rejected under 35 U.S.C. §103 as unpatentable over Innes et al. (US 20130272829 A1, hereinafter “Innes”), in view of Geislinger et al. (US 11521138 B1, hereinafter “Geislinger”) as applied to claim 1 above, in further view of Deenathayalan et al. (EP 3836052 A1, hereinafter “Deenathayalan”), in further view of Coutinho et al. (US 20180302751 A1, hereinafter “Coutinho”).
Regarding claim 3: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising: obtaining centroid data about the forecast model block and the district model block from the shovel load data for the truck load;
matching the centroid data to centroid data of the target block;
and determining the target block.
Deenathayalan further teaches:
and determining the target block. (Fig. 3, Step 214 – Identify Pile Location 216 from Plurality of Pile Locations; Par. [0011] teaches to remove the material from the worksite 100 and transport it to an end use such as a construction site, customers or other responsible entities may send one or more road trucks 130 that are configured to haul the material.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Deenathayalan’s feature(s) listed above. One would’ve been motivated to identify an identified pile location from among the plurality of pile locations at the worksite (Deenathayalan; [0005]). By incorporating the teachings of Deenathayalan, one would’ve been able to determine target blocks.
Innes and Deenathayalan doesn’t teach:
further comprising: obtaining centroid data about the forecast model block and the district model block from the shovel load data for the truck load;
matching the centroid data to centroid data of the target block;
Coutinho teaches:
further comprising: obtaining centroid data about the forecast model block and the district model block from the shovel load data for the truck load; ([0218] The system herein described herein adds value to those applications, whether it is used as a sole positioning solution, or combined with other positioning systems, and can improve the final position precision. Moreover, aspects of the present disclosure can provide position fixes in geographic areas where traditional GNSS-based (e.g., GPS) approaches cannot. For instance, various aspects of the present disclosure are able to provide positioning information for vehicles located in the middle of “container canyons” in a harbor, in “urban canyons” (e.g., tall buildings) in cities, in closed parking lots, in tunnels, and in harsh, controlled spaces such as a mining site.; [0209] The calculation of the location estimate may, for example, determine a centroid of the geographic locations identified. Examiner notes that one of ordinary skill in the art would reasonably consider the locations in a mining site as equivalent to the forecast model block and the district model block from Applicant’s claim.);
matching the centroid data to centroid data of the target block; ([0209] the calculation of the location estimate may, for example, determine a centroid of the geographic locations identified; [0210] The process of estimating a location based on the wireless fingerprint sample data described above may involve the calculation of the centroid of the wireless snapshot samples. In accordance with aspects of the present disclosure, those samples may be matched against the different RF signal characteristics that the network node sensed and sent within the location estimation request.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Coutinho’s feature(s) listed above. One would’ve been motivated to do so in order to generate/provide a requested location estimate (Coutinho; [0209]). By incorporating the teachings of Coutinho, one would’ve been able to determine the centroid of different locations.
Regarding claim 5: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising at least one of: assigning a route code to the truck load based on the truck load data; re-calculating the route code based on grades in the forecast model; or calculating cut-off files using the route code based on the truck load data.
Deenathayalan further teaches:
further comprising at least one of: assigning a route code to the truck load based on the truck load data; ([0024] the backend system 144 can be configured to determine a route or path through the worksite 100 for the road truck and in a route generation step 239 can generate a route or path for the road truck 130 to travel to the identified pile locations 216, which may be based on the shortest distance or time of travel. Examiner notes that one of ordinary skill in the art would reasonably interpret the data coming from the track’s wireless transmitter/receiver as being equivalent to truck load data.); re-calculating the route code based on grades in the forecast model; or calculating cut-off files using the route code based on the truck load data.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Innes with Deenathayalan’s feature(s) listed above. One would’ve been motivated to do so in order to communicate the identified pile location 216 and directions which may include the generated route or path to the pile location (Deenathayalan; [0024]). By incorporating the teachings of Deenathayalan, one would’ve been able to assign a route code to the truck load.
Regarding claim 6: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising determining copper recovery from the target block by the comparing of the forecast model block characteristics of the forecast model block and the district model block characteristics of the district model block with the target block characteristics of the target block.
Deenathayalan teaches:
further comprising determining copper recovery from the target block by the comparing of the forecast model block characteristics of the forecast model block and the district model block characteristics of the district model block with the target block characteristics of the target block. (Par. [0008] teaches examples of these materials include stone, sand, sandstone, chalk, clay, coal, iron ore, copper ore, gypsum, etc.; Par. [0009] teaches the processed materials, because of their aggregate or granular form, may be disposed in various piles 110 about the worksite 100 until they have been sold and their transportation from the worksite occurs. Because the processed materials are available in different sizes or grades, and because different types of material (e.g., stone and sand) may be obtained from the mine 102, the piles 110 are typically designated and separated by material type, grade, and/or other characteristics. Examiner notes that one of ordinary skill in the art would reasonably interpret the process of separating and disposing material in the various piles based on, among other things, material type and grade, as being equivalent to comparing the characteristics of the material from the different mine locations.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Innes with Deenathayalan to include the limitations listed above. One would’ve been motivated to do so in order to preserve the homogeneity of the particular material in the pile (Deenathayalan at par. [0009]), and to compare the data from a plurality of job confirmation data 264 associated with an identified pile location 216 and reorder material to replenish an identified pile location 216 if appropriate (Deenathayalan at par. [0030]). By incorporating the teachings of Deenathayalan, one of ordinary skill in the art would’ve been able to determine copper recovery from the mine.
Regarding claim 16: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising joining the truck load data into the shovel load data using a load shift index, load number and shift date.
Deenathayalan teaches:
further comprising joining the truck load data into the shovel load data using a load shift index, load number and shift date. (Par. [0027] teaches the loading machine 124 may conduct a loading operation 250 in which material is loaded from the material pile 110 to the road truck 130. During the loading operation 250, the onboard controller 160 and/or payload monitoring system 168 associated with the loading machine 124 can conduct a measurement step 252 to measure attributes and progress associated with the loading operation.; [0028] To make the measured attributes associated with the loading operation 250, available for tracking and assessment, the onboard controller 160 in a data compilation step 262 can compile and communicate such information to the backend system 144 as job confirmation data 264. Examples of job confirmation data 264 can include the loaded material quantity 254, the loading time 256, the cycle count 258, and road truck identification 226 associated with the job order 218 and/or road truck identification 226. Examiner notes that one of ordinary skill in the art would reasonably interpret loaded material quantity 254 as a load number, loading time 256 as a timestamp inclusive of date and time of the operation, and cycle count 258 as a count of shifts – shift index – before the machine needs to be serviced.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine modified Innes with Deenathayalan to include the limitations listed above. One would’ve been motivated to do so in order to communicate the job confirmation data 264 to the backend system 144 (Deenathayalan at par. [0023]). By incorporating the teachings of Deenathayalan, one of ordinary skill in the art would’ve been able to join truck load data and shovel data using a load shift index, load number and shift date.
Regarding claim 18: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising providing, using mapping tables, consistent data for models.
Deenathayalan further teaches:
further comprising providing, using mapping tables, consistent data for models. (([Fig. 2] 170); [0015] Referring to FIG. 2, there is illustrated systems and devices that may be operatively associated with the loading machines 124 such as a bucket loader 126 to facilitate operation at the worksite 100.; [0026] For example, referring to FIG. 2, the operator interface display 170 may visually present a plurality of job orders 218 to the operator by, for example, road truck identification 226. Moreover, the job orders 218 may be sorted by suitable criteria. For example, the job orders 218 may be sorted by the order in which the road trucks 130 arrive at the worksite 100, order in which the road trucks 130 arrive at the identified pile location 216, the material quantity 206 corresponding to the job order 218, proximity of the loading machine 124 and road truck 130 as determined through the GPS system 150, or other suitable criteria.; [0027] As illustrated in FIG. 2, the attributes measured in the measurement step 252 may be displayed in real time on the operator interface display 170 on the loading machine 124.; [0028] To make the measured attributes associated with the loading operation 250, available for tracking and assessment, the onboard controller 160 in a data compilation step 262 can compile and communicate such information to the backend system 144 as job confirmation data 264. Examiner notes that one of ordinary skill in the art would reasonably consider the automated data gathering as equivalent to consistent data.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Deenethayalan’s feature(s) listed above. One would’ve been motivated to do so in order to query whether there are additional job orders 218 to perform (Deenathayalan; [0028]). By incorporating the teachings of Deenathayalan, one would’ve been able to get consistent data for mapping tables.
Regarding claim 19: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising displaying a point representing a shovel scoop of the material and at least one of projected yield of the material, routes for the material or processing locations for the material.
Deenathayalan further teaches:
further comprising displaying a point representing a shovel scoop of the material and at least one of projected yield of the material, routes for the material or processing locations for the material. ([Fig. 3] 214: Identify Pile Location 216 from Plurality of Pile Locations; [0024] the backend system 144 can be configured to determine a route or path through the worksite 100 for the road truck and in a route generation step 239 can generate a route or path for the road truck 130 to travel to the identified pile locations 216, which may be based on the shortest distance or time of travel. To provide instructions to the road truck 130, in a communication step 240 the backend system 144 can communicate the identified pile location 216 and directions which may include the generated route or path to the pile location from the scale house 134 back to the front end system 138 where they can be communicated to the operator of the road truck 130. In embodiments where the road truck 130 includes a wireless transmitter/receiver 143, the information can be conveyed electronically and can include digital maps, directions and the like.)
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Deenathayalan’s feature(s) listed above. One would’ve been motivated to do so in order to communicate the identified pile location 216 and directions which may include the generated route or path to the pile location (Deenathayalan; [0024]). By incorporating the teachings of Deenathayalan, one would’ve been able to display the location of shovel scoops and the route.
Regarding claim 21: Modified Innes teaches the method of claim 1. Innes further teaches:
determining similar shovel load characteristics for a subset of the plurality of shovel loads based on the subset of the plurality of shovel loads being in the same district model block; ([0051] In order to track the lumped masses through the production chain, some information about the lumped masses is required. The equipment such as excavators 14 and haul trucks 16 may be provided with sensors that monitor one or more characteristics of the material. The characteristics include extensive properties such as mass and volume that define the amount of material in a lumped mass. The measured characteristics may also include intensive properties such as chemical composition or a fragmentation level of the material. Examiner notes that one of ordinary skill in the art would reasonably interpret the mass and volume of all loads as equivalent to determining similar shovel load characteristics as recited by Applicant’s claim.);
matching the plurality of shovel loads with the truck load based on the forecast value and the district value; ([0048] FIG. 1 gives an example of an open pit production chain 10 in which material is tracked using the systematic approach outlined in this specification. A plurality of grade blocks 12 is excavated by a set of excavators 14. The material from the excavators 14 is loaded onto one or more haul trucks 16, which then unload the material at one or more locations such as run of mine (ROM) stockpiles 18 or a crusher.);
and reconciling an actual mining yield with the forecast model block and the district model block by determining that the actual mining yield in the target block meets a forecasted yield from the forecast model block and the district model block. ([0002] In the mining industry, having incorrect estimates of grade and quantity in stockpiles can lead to financial penalties. Improving the quality of information by tracking material at each stage would enable mine engineers to perform greater planning to avoid these penalties.; [0007] A variety of sensors are used to measure different properties of the material at spatially distinct locations as the material is moved through a production chain or operational process chain. An appropriate framework for ensuring consistent data fusion is also described.; [0107] Lumped material estimates of material separated from other lumped materials are correlated to those original lumped material estimates.; [0147] An aim in bulk material tracking is to ensure that material is not `invented`. Take again the simple example case of modelling an excavator loading material from a stockpile using two separate Kalman filters in a system similar to that shown in FIG. 8A, one filter for the stockpile and the other for the excavator bucket. When fusing new information about the material in excavator bucket, there is no automatic method to update the material in the stockpile to reflect the correlation between the two lumped materials. The excavator bucket can be carrying more or less material then what is estimated to be removed from the original stockpile. Thus after fusing in new information there may be a discrepancy of total mass in the system from what is originally estimated.; [0152] Reconciliation in the mining industry is generally considered as the process of comparing the actual quality and amount of material mined from a designated area compared to the expected output of that area.; [0153] This can be achieved, for example, by adding a new special reconciliation state for a stockpile, initialized with 0 mean and variance. Material removed can be added to this state and conversely material added can be subtracted from the reconciliation state.; [0161] The reconciliation may be performed at specified intervals, for example once a day or once per shift. It may also be carried out when a specified event occurs. For example, a reconciliation may be performed if the state space is augmented or diminished. A reconciliation may also be performed if a new observation is available.).
Innes doesn’t teach:
determining a forecast value of the forecast model block and a district value of the district model block for each of the plurality of shovel loads;
matching the plurality of shovel loads with the truck load based on the forecast value and the district value;
Deenathayalan teaches:
determining a forecast value of the forecast model block and a district value of the district model block for each of the plurality of shovel loads; ([0002] The processed materials are then stored about the worksite at different zones or locations by grade, size, or type.; [0003] In some worksites, the piles locations may be situated considerable distances apart from each other.; [0016] To receive and process data about operations of the loading machine 124, the onboard controller 160 can be operatively associated with a plurality of sensors, actuators, and other systems disposed about the loading machine 124. By way of example, this may include a payload monitoring system 168 operatively associated with the bucket 128 to measure load and cycle counts, load weights and the like. Sensors may, for example, monitor load and dump cycles through which the bucket 128 is maneuvered, and may monitor load weights through operative association with the hydraulic system to measure hydraulic forces generated during load and dump cycles or may utilize other force measurement technologies. The payload monitoring system 168 and onboard controller 160 can track performance data such as by daily totals or the like.; [0026] For example, referring to FIG. 2, the operator interface display 170 may visually present a plurality of job orders 218 to the operator by, for example, road truck identification 226. Moreover, the job orders 218 may be sorted by suitable criteria. For example, the job orders 218 may be sorted by the order in which the road trucks 130 arrive at the worksite 100, order in which the road trucks 130 arrive at the identified pile location 216, the material quantity 206 corresponding to the job order 218, proximity of the loading machine 124 and road truck 130 as determined through the GPS system 150, or other suitable criteria.; [0027] Referring back to FIG. 4, the loading machine 124 may conduct a loading operation 250 in which material is loaded from the material pile 110 to the road truck 130. During the loading operation 250, the onboard controller 160 and/or payload monitoring system 168 associated with the loading machine 124 can conduct a measurement step 252 to measure attributes and progress associated with the loading operation. For example, attributes may include loaded material quantity 254, job duration or loading time 256, cycle count 258 that represents the number of operating cycles such as loading and dumping that the loading machine 124 conducts. The measured attributes may be associated with the road truck 130 being loaded via the road truck identification 226, associated by the loading machine 124 conducting the loading operation 250, or otherwise. As illustrated in FIG. 2, the attributes measured in the measurement step 252 may be displayed in real time on the operator interface display 170 on the loading machine 124. Examiner notes that one of ordinary skill in the art would reasonably consider payload as equivalent to value.);
matching the plurality of shovel loads with the truck load based on the forecast value and the district value; ([0027] Referring back to FIG. 4, the loading machine 124 may conduct a loading operation 250 in which material is loaded from the material pile 110 to the road truck 130. During the loading operation 250, the onboard controller 160 and/or payload monitoring system 168 associated with the loading machine 124 can conduct a measurement step 252 to measure attributes and progress associated with the loading operation. For example, attributes may include loaded material quantity 254, job duration or loading time 256, cycle count 258 that represents the number of operating cycles such as loading and dumping that the loading machine 124 conducts. The measured attributes may be associated with the road truck 130 being loaded via the road truck identification 226, associated by the loading machine 124 conducting the loading operation 250, or otherwise. Examiner notes that one of ordinary skill in the art would reasonably consider payload as equivalent to value.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Innes with Deenathayalan’s feature(s) listed above. One would’ve been motivated to do so in order to track performance data such as by daily totals or the like (Deenathayalan; [0016]). By incorporating the teachings of Deenathayalan, one would’ve been able to determine the value of loads.
Claims 14 is rejected under 35 U.S.C. §103 as unpatentable over Innes et al. (US 20130272829 A1, hereinafter “Innes”), in view of Geislinger et al. (US 11521138 B1, hereinafter “Geislinger”) as applied to claim 1 above, in further view of, in further view of Aras et al. (US 20210398157 A1, hereinafter “Aras”).
Regarding Claim 14: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising determining, based on tons and grades inside each of the area categories at least one of
percentage of time mining operations achieved the mine plan as forecasted,
percentage of the material that was moved forward from subsequent months,
percentage of the material that was deferred or how each of the area categories impacted the amount of metal that was obtained.
Aras teaches:
further comprising determining, based on tons and grades inside each of the area categories ([0369] The mine production schedule will be generated to mine the blocks in the ultimate pit under the guidance of production requirements. The life of the mine is 10 years. The mine plan must comply with the yearly production requirements outlined in Table 5.7. The requirements include restrictions on the maximum tonnage that can be processed at mill and leach pads, minimum average grade from mill and leach pads, restrictions on the maximum proportion of the ore blocks that belongs to an inferred category which possess high risk and minimum requirements on the proportion of the ore blocks that belongs to indicated and measured risk categories. Risk proportions basically quantify the risk exposure of the ore blocks in the process flow.), at least one of
percentage of time mining operations achieved the mine plan as forecasted,
percentage of the material that was moved forward from subsequent months,
percentage of the material that was deferred or how each of the area categories impacted the amount of metal that was obtained. ([0376] As seen the significant proportion of the ore blocks are leach blocks.; [0366] We can also say that leaving those leach blocks on the ground lead to a decrease of the proportion of the inferred blocks about 1.4%).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Aras’ feature(s) listed above. One would’ve been motivated to do so in order to calculate the value of the pit with undiscounted block values (Aras; [0366]). By incorporating the teachings of Aras, one would’ve been able to determine the percentage of the material that was deferred.
Claims 17 is rejected under 35 U.S.C. §103 as unpatentable over Innes et al. (US 20130272829 A1, hereinafter “Innes”), in view of Geislinger et al. (US 11521138 B1, hereinafter “Geislinger”) as applied to claim 1 above, in further view of Deenathayalan et al. (EP 3836052 A1, hereinafter “Deenathayalan”), in further view of Benter et al., "Determining bulk density of mine rock piles using ground penetrating radar frequency downshift," 2011 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), Aachen, Germany, 2011, pp. 1-6 (hereinafter “Benter”).
Regarding claim 17: Modified Innes teaches the method of claim 1. Innes doesn’t teach:
further comprising joining the shovel load data into mapping tables using pit name, mined pit code and centroid z.
Deenathayalan further teaches:
further comprising joining the shovel load data into mapping tables using pit name, mined pit code… ([Fig. 2] 170; [0024] the backend system 144 can be configured to determine a route or path through the worksite 100 for the road truck and in a route generation step 239 can generate a route or path for the road truck 130 to travel to the identified pile locations 216, which may be based on the shortest distance or time of travel. To provide instructions to the road truck 130, in a communication step 240 the backend system 144 can communicate the identified pile location 216 and directions which may include the generated route or path to the pile location from the scale house 134 back to the front end system 138 where they can be communicated to the operator of the road truck 130. In embodiments where the road truck 130 includes a wireless transmitter/receiver 143, the information can be conveyed electronically and can include digital maps, directions and the like.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Deenathayalan’s feature(s) listed above. One would’ve been motivated to do so in order to communicate the identified pile location 216 and directions which may include the generated route or path to the pile location (Deenathayalan; [0024]). By incorporating the teachings of Deenathayalan, one would’ve been able to join shovel load data, and mapping data.
Deenathayalan doesn’t teach:
further comprising joining the shovel load data into mapping tables using centroid z.
Benter teaches:
further comprising joining the shovel load data into mapping tables using centroid z. ([Table 2] Sample, Height, samples, and Centroid frequency).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Innes with Benter’s feature(s) listed above. One would’ve been motivated to do so in order to suggest that it is possible to determine the presence of large fragments in the draw point by determining the rate of frequency downshift over a window in the GPR trace (Benter; [Page 5]). By incorporating the teachings of Benter, one would’ve been able to use centroid data in the joining of load data and mapping data.
Accordingly, claims 1-3, and 5-21 are rejected under 35 USC 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Megannon et al. (WO 2022251888 A1), which discloses a computer-assisted management system for a mining and production process of natural resource and reserve with mining technical equipment on an automated and commercial basis.
Job (WO 2021108838 A1), which discloses a method for detecting changes in the ore grade of a rock face in near real time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.J.T./Examiner, Art Unit 3625
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
Prosecution Insights