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 . Claims 1-2 and 4-21 have been reviewed and are under consideration by this office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/24/2026 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant, on 07/24/2026, amended claims, cancelled claim 3, and added claim 21. Claims 1-2 and 4-21 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that amended claims overcome the need for 101 Rejections.
Examiner respectfully disagrees. The amended claims have been updated and rejected below. See below for full 101 Rejection analysis. The controlling of a mobile work machine is recited at a high level of generality and as such remains rejected as seen below.
Response to Arguments - 35 USC § 102/103
Applicant’s arguments with respect to the 35 USC 103 rejections have been fully considered, but are moot in view of the new line of 103 Rejections seen 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 1-2 and 4-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the Claims is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent Claims, (additional elements bolded)
Regarding Claims 1 and 10, A computer implemented method of controlling a mobile work machine, the computer implemented method comprising: obtaining a first operational strategy input identifying a first operational strategy;
obtaining a first progress perspective selection input identifying a first progress perspective selection;
obtaining historical data;
obtaining forecast data;
generating a first output including first progress perspective data indicative of one or more predictive parameters of progress corresponding to the first progress perspective selection based on the historical data, the forecast data, the first operational strategy input, and the first progress perspective selection;
obtaining, in response the first output, a user input querying how to adjust at least one predictive parameter of progress of the one or more predictive parameters of progress;
generating, in response to the user input, a recommended operational adjustment to adjust the at least one predictive parameter of progress; and
controlling the mobile work machine based on the first output.
Further regarding Claim 10, An operations computing system comprising: one or more processors; memory; and computer executable instructions stored in the memory, the computer executable instructions, when executed by the one or more processors, configuring the one or more processors to:
Regarding Claim 19, A computer implemented method of controlling a mobile work machine, the computer implemented method comprising: obtaining a first operational strategy input identifying a first operational strategy and including a plan and a constraint;
obtaining a first progress perspective selection input identifying a first progress perspective selection and including a scope and a progress definition;
obtaining historical data;
obtaining forecast data;
generating a first output including first perspective data indicative of one or more predictive parameters of progress corresponding to the first progress perspective selection based on the historical data, the forecast data, the first operational strategy, and the first progress perspective selection;
obtaining, in response to the first output, a user query input querying how to adjust at least one predictive parameter of progress of the one or more predictive parameters of progress;
generating, in response to the user query input, a plurality of recommended operational adjustments to adjust the at least one predictive parameter of progress;
controlling the mobile work machine based on the selected recommended operational adjustment.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards obtaining first operational strategy input; obtaining historical data; obtaining forecast data; obtaining a second operational strategy input; and obtaining second progressive perspective input all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards generating outputs indicative of progress of one or more operations, one or more jobs, or one or more seasons which can indicate one or more recommend operational plan adjustments (See Specification,[20]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an computer; control(ing) the mobile work machine (recited at a high level of generality up to and including displaying/presentation of data); An operations computing system comprising: one or more processors; memory; and computer executable instructions stored in the memory, the computer executable instructions, when executed by the one or more processors, configuring the one or more processors to;. Examiner notes that the Applicant’s specification is merely exemplary with respect to the mobile work machine and could include off road machine, tools, implements which under the broadest reasonable interpretation of the claim could merely be a user’s mobile device/phone and causing anything to be displayed (i.e. presentation). The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claims 4-6, 8-9, 11-12, 14-15, 17-18, and 21 the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims (i.e. generating a control signal).
Regarding Claims 2, 13, and 20, the claim further recite the additional element(s) of controlling the mobile work machine comprises controlling one or more controllable subsystems of the mobile work machine. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claims 7 and 16, the claim further recite the additional element(s) of control, based on the operational adjustment preference selection input, the mobile work machine based on the first recommended operational adjustment.. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the Claims amounts to significantly more than the abstract idea itself.
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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 5, 8, 10-14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wesley et al. (US 20240249370 A1) in view of Cherney et al. (US 20190198015 A1).
Regarding Claims 1 and 10, Wesley teaches: A computer implemented method of controlling a mobile work machine, the computer implemented method comprising: obtaining a first operational strategy input identifying a first operational strategy; (Wesley, [abstract]; Systems, methods, and computer-program products can perform task-based management of a mine site. The systems, methods, and computer-program products can generate one of a specialized management operator interface or a specialized active work operator interface, for selective display on a display device and Wesley, [30]; FIG. 2 is a schematic illustration of the mining management system 12. The mining management system 12 may receive data, for the planning and/or management of the mine site 10, including mine site-related data and mine site planning data (i.e., one or more plans each with one or more jobs for the mine site 10). Optionally, mining management system 12 can receive other types of information that may be helpful or useful for mine site planning and/or management, such, but not limited to, as market data, weather data, delivery site data, and feed rate of crusher and Wesley, [35]; The operator control interface 31 can also receive inputs, control and/or data, for planning and/or management of the mine site 10 and Wesley, [216]; as used herein, the term “circuitry” can refer to any or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry); (b) to combinations of circuits and software (and/or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s)/software (including digital signal processor(s)), software and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and (c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present). Examiner interprets the displaying of the optimized plan as a control signal to cause the interface to display).
obtaining a first progress perspective selection input identifying a first progress perspective selection; (Wesley, [97]; The information provided by the specialized management operator interface 330 can identify compliance to plan, that is, current and forecasted information regarding whether progress is according to plan or not. This can indicate behind schedule or even ahead of schedule. In this regard, the specialized management operator interface 330 can identify which aspects are progressing according to plan and which aspects are not progressing according to plan. Here, cycles can be allocated to individual jobs and tasks so that the specialized management operator interface 330 can provide direct feedback).
obtaining historical data; (Wesley, [85]; The allocation of ‘Available’ tasks over a SIC window may be referred to or characterized as dispatch to plan using short interval control. Put more generally, the assignment engine 27 can take ‘Available’ tasks and also other input data (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data, etc.), and/or operator-related data), identify and allocate a best or optimal configuration of entities (e.g., machines and/or operators)) based on the ‘Available’ tasks and input data, and determine how best to coordinate the activities of some or all of the work machines to achieve the tasks of the short-term plan).
obtaining forecast data; (Wesley, [43]; the forecasting engine 28, and the active work engine 29 may operate at the same time or in parallel (e.g., in the background while another is operating). According to one or more embodiments, caches or tables of tasks, production arcs, and forecast rates, as defined herein, may constitute an integration point between the assignment engine 27 and each of the forecasting engine 28 and Wesley, [61]; A forecast rate can be regarded as a rate over a specific time period for a specific machine. The forecast rate may be used to compile metrics and predict completion times for auto-assigned tasks (‘Ready’ and ‘Available’ tasks). According to one or more embodiments, forecast rates may be produced by the forecasting engine 28 for ‘Ready’ and ‘Available’ tasks. Optionally, there may be multiple forecast rates for a single work machine (e.g., one of the hauling machines 50) during a given time period where the work machine is referenced in, or assigned with, multiple tasks. A forecast arc can include start and end times so that changes in forecast production rates may be modeled across planned delays (i.e., operational tasks) and Wesley, [104]; Turning now specifically to forecasting, in the context of the present disclosure, forecasting can be regarded as involving incremental calculation of production rates and completion times for tasks, loading tools (e.g., digging machine 52), and processors (e.g., a crusher at processing plant 48). Calculations or determinations can be performed, for instance, by the controller 20, for individual work machines such that the forecasting performance can scale or be scaled proportionally to the changes made and not to the size of the mine site 10. For example, if a change is made that invalidates the forecasts for five (5) tasks, the forecasting can respond with updated forecasts in a defined, consistent time regardless of the size of the mine site 10, e.g., no matter how many machines are working at the mine site 10. The forecasting can predict future production rates for loading tools (e.g., digging machine 52) and processors (e.g., a crusher at processing plant 48).
generating a first output including first progress perspective data indicative of one or more predictive parameters of progress corresponding to the first progress perspective selection based on the historical data, the forecast data, the first operational strategy input, and the first progress perspective selection; (Wesley, [29]; In embodiments of the present disclosure, current and/or historical cycles may be matched to tasks to determine progress towards task completion and current and historical production rates, respectively and Wesley, [81]; assignment may be referred to or characterized as backend processing by the assignment engine 27 to assign tasks, for instance, to enhance (automate or optimize) the completion of tasks, i.e., compliance to plan over time, rather than necessarily optimizing to static production rates, i.e., compliance to static rates. That is, the assignment engine 27 can, based on the inputs thereto (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data), and/or operator-related data), automatically identify and allocate an enhanced (e.g., optimal) configuration of entities (e.g., work machines and/or operators) to complete each task of the short-term plan and Wesley, [97]; The information provided by the specialized management operator interface 330 can identify compliance to plan, that is, current and forecasted information regarding whether progress is according to plan or not. This can indicate behind schedule or even ahead of schedule). Examiner interprets compliance to a plan, configuration of entities to complete task, and telemetry data as data indicative of one or more predictive parameters of progress as the cited parameters would all indicate expected progress.
controlling the mobile work machine based on the recommended operational adjustment. (Wesley, [76]; Referring again specifically to the specialized analyzing operator interface, as noted above, the specialized analyzing operator interface can provide planning and/or management information such as the short-term plan or plans, the job or jobs associated with the short-term plan(s), the task or tasks associated with each job (e.g., operational tasks and/or non-production tasks), equipment, sources, dumps, materials, status of each of the tasks, prioritization or rank for the tasks, and task backlog information and Wesley, [124]; an artificial intelligence (AI) tool may be implemented to annotate the Spark Chart(s). Generally, the AI tool can analyze the data and provide recommendations, output on the Spark Charts, for instance, for how to improve production. For instance, a pop-up may be generated on the Spark Chart. As a specific example, based on the pop-up the operator could say at this time it looks like production is dropping, so extra capacity exists. The AI tool may recommend specific changes to the tasks to potentially make to increase overall production). Examiner notes that Wesley teaches controlling a mobile work machine but relies on Cherney below to explicitly teach the recommended operational adjustment.
While Wesley teaches generating data indicative of predictive parameters of progress and controlling a work machine, Wesley does not appear to teach obtaining user input to generate recommended adjustments. However, Wesley in view of the analogous art of Cherney (i.e. operations management) does teach: obtaining, in response to the first output, a user input querying how to adjust at least one predictive parameter of progress of the one or more predictive parameters of progress generating, in response to the user input, a recommended operational adjustment to adjust the at least one predictive parameter of progress; and (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system and Cherney, [44-45]; These sensed variables may provide inputs to sensor signal processing logic 166 that cause sensor signal processing logic 166 to generate a suggested change in operating parameters, machine settings, machine configuration, etc. That information can be output to speech processing system 170 and a synthesized message can be played for operator 116 suggesting a settings change, a machine configuration change, or another operational parameter change for machine 102. The machine can be automatically controlled to make those changes and a speech output can be generated to notify the operator of the change. These are just examples of sensor inputs that can trigger speech processing services and examples of services that can be performed, and a wide variety of others can be used as well. Using a sensor input to trigger speech processing services is indicated by block 206 in the flow diagram of FIG. 3… It may also be that operator 116 provides another input, through another user interface mechanism 150, and this may trigger speech services. Triggering speech services based on another operator input is indicated by block 208).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including generating an data indicative of predictive parameters of progress with the teachings of Cherney including receiving user input and a responsive recommendation in order to allow for remote users effectively control an operation (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system (e.g., remote machine control logic 125) to generate control signals to change the settings on the target machine. Generating control signals to adjust settings is indicated by block 238).
Further regarding Claim 10, Wesley teaches: An operations computing system comprising: one or more processors; memory; and computer executable instructions stored in the memory, the computer executable instructions, when executed by the one or more processors, configuring the one or more processors to: (Wesley, [07]; According to yet another aspect of the present disclosure, a non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, causes the processor to perform steps pursuant to the computer-implemented method disclosed herein. The one or more processors can be caused to).
Regarding Claims 2 and 13, Wesley teaches: The computer implemented method of claim 1, wherein controlling the mobile work machine comprises controlling one or more controllable subsystems of the mobile work machine (Wesley, [76]; Referring again specifically to the specialized analyzing operator interface, as noted above, the specialized analyzing operator interface can provide planning and/or management information such as the short-term plan or plans, the job or jobs associated with the short-term plan(s), the task or tasks associated with each job (e.g., operational tasks and/or non-production tasks), equipment, sources, dumps, materials, status of each of the tasks, prioritization or rank for the tasks, and task backlog information and Wesley, [124]; an artificial intelligence (AI) tool may be implemented to annotate the Spark Chart(s). Generally, the AI tool can analyze the data and provide recommendations, output on the Spark Charts, for instance, for how to improve production. For instance, a pop-up may be generated on the Spark Chart. As a specific example, based on the pop-up the operator could say at this time it looks like production is dropping, so extra capacity exists. The AI tool may recommend specific changes to the tasks to potentially make to increase overall production and Wesley, [216]; as used herein, the term “circuitry” can refer to any or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry); (b) to combinations of circuits and software (and/or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s)/software (including digital signal processor(s)), software and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and (c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present). Examiner interprets the displaying of the optimized plan as a control signal to cause the interface to display). Examiner interprets the displaying of a recommended operational adjustment controlling a subsystem as the displaying function would be a subsystem of the mobile work machine itself.
Regarding Claims 4 and 11, While Wesley teaches a plurality of progress parameters, Wesley does not appear to teach a date. However, Wesley/Cherney teaches: wherein the at least one predictive parameter of progress comprises a date (Cherney, [72]; Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including a plurality of progress parameters with the teachings of Cherney including a date in order to provide timing functions for the processing devices (Cherney, [72]; Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17).
Regarding Claims 5 and 14, Wesley teaches: The computer implemented method of claim 1 and further comprising: wherein the first progress perspective selection identifies a definition of progress and a scope of progress (Wesley, [97]; The information provided by the specialized management operator interface 330 can identify compliance to plan, that is, current and forecasted information regarding whether progress is according to plan or not. This can indicate behind schedule or even ahead of schedule. In this regard, the specialized management operator interface 330 can identify which aspects are progressing according to plan and which aspects are not progressing according to plan and Wesley, [113]; work in progress, i.e., ‘Active’ assignments for loaded hauling machines, and travel times can be taken into consideration when calculating completion times and rates for loading tools and Wesley, [149, 151, 157]; Tasks can be progressed through a sequence of predefined states, for example, ‘New,’ ‘Ready,’ ‘Available,’ and ‘Closed.’ Tasks in the ‘Available’ state can be used to set dynamic production goals for assignment over the short interval control (SIC) window... The controller may need to quickly analyze the progress towards task completion and receive indications of upcoming risks for a plan not being achieved or overachieved before it occurs… together with the forecasting feature, which can provide near real time feedback, for instance, on the console 30 regarding task progress, can improve both operator productivity and compliance to plan). Examiner interprets the definition of progress to be whether the project is going to plan or not and further notes progress scope is determined for each task such as loaded hauling machines, travel times, or progress towards a specific task completion.
Regarding Claims 8 and 17, Wesley teaches: The computer implemented method of claim 1 and further comprising: obtaining assets data; and (Wesley, [81]; the assignment engine 27 can, based on the inputs thereto (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data), and/or operator-related data), automatically identify and allocate an enhanced (e.g., optimal) configuration of entities (e.g., work machines and/or operators) to complete each task of the short-term plan. Thus, assignment engine 27, in the context of the present disclosure, can be regarded as a processing engine).
generating the first output including first progress perspective data based further on the assets data. (Wesley, [76]; Referring again specifically to the specialized analyzing operator interface, as noted above, the specialized analyzing operator interface can provide planning and/or management information such as the short-term plan or plans, the job or jobs associated with the short-term plan(s), the task or tasks associated with each job (e.g., operational tasks and/or non-production tasks), equipment, sources, dumps, materials, status of each of the tasks, prioritization or rank for the tasks, and task backlog information and Wesley, [67]; Discussed in more detail below, the prioritized tasks can be automatically processed by the assignment engine 27 to perform task-based management and scheduling (e.g., enhanced or optimized work machine allocation) for the job(s) of the mine site 10). Examiner interprets the displaying of the optimized plan as a control signal to cause the interface to display.
Claims 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wesley et al. (US 20240249370 A1) in view of Cherney et al. (US 20190198015 A1), and Farquhar et al. (US 20140279343 A1).
Regarding Claims 6 and 15, Wesley/Cherney including a scope and time periods (Wesley, [04]) neither appear to teach an agricultural season teaches. However, Wesley/Cherney in view of the analogous art of Farquhar (i.e. production management) does teach: The computer implemented method of claim 5, wherein the scope comprises an agricultural season (Farquhar, [07]; Crop production is also susceptible to crop failure or risk based on weather or other potentially unforeseen factors during a crop production season. If it were possible from the perspective of the farmer to lock in or guarantee at least a baseline of revenue from a crop production season they could both personally as well as from the perspective of their business enable some additional business decisions to be made, if they had any guarantee of some revenue with respect to a particular crop or crop production season).
It would have been obvious to try by one of ordinary skill in the art at the time the invention was made, to use agricultural seasons data of Farquhar and incorporate it into the system of Wesley/Cherney since the system perform calculation and considers project scope and predetermined time periods would have performed the same regardless of the type of time period is used and one of ordinary skill in the art could have pursued the known potential solutions with reasonable expectation of success (categorizing data). (See MPEP2143(E) – Obvious to try rationale).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley/ Cherney including a scope and time periods with the teachings of Farquhar including agricultural seasons in order to consider external factors such as weather associated with a particular season (Farquhar, [54]; Calculation of the minimum yield 5 which would be contracted for could be calculated using various factors including past historical yield data within the crop production area 3, weather forecasting or other external environmental variables which may impact the likely outcome of a particular crop season 2 and/or past farming practices of the farmer 20 within the crop production area 3).
Claims 7, 16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wesley et al. (US 20240249370 A1) in view of Cherney et al. (US 20190198015 A1), and Dawson et al. (US 20220246287 A1).
Regarding Claims 7 and 16, Wesley/Cherney teaches: wherein controlling the mobile work machine comprises controlling the mobile work machine based on the operational adjustment preference selection input. (Wesley, [76]; Referring again specifically to the specialized analyzing operator interface, as noted above, the specialized analyzing operator interface can provide planning and/or management information such as the short-term plan or plans, the job or jobs associated with the short-term plan(s), the task or tasks associated with each job (e.g., operational tasks and/or non-production tasks), equipment, sources, dumps, materials, status of each of the tasks, prioritization or rank for the tasks, and task backlog information and Wesley, [124]; an artificial intelligence (AI) tool may be implemented to annotate the Spark Chart(s). Generally, the AI tool can analyze the data and provide recommendations, output on the Spark Charts, for instance, for how to improve production. For instance, a pop-up may be generated on the Spark Chart. As a specific example, based on the pop-up the operator could say at this time it looks like production is dropping, so extra capacity exists. The AI tool may recommend specific changes to the tasks to potentially make to increase overall production). Examiner notes that Wesley teaches displaying a recommendation but relies on Cherney below to explicitly teach the recommended operational adjustment.
While Wesley teaches generating data indicative of predictive parameters of progress and controlling a work machine, Wesley does not appear to teach obtaining user input to generate recommended adjustments. However, Wesley in view of the analogous art of Cherney (i.e. operations management) does teach: The computer implemented method of claim 1, wherein the recommended operational adjustment comprises a first recommended operational adjustment, the computer implemented method further comprising: generating, in response to the user input, a second recommended operational adjustment to adjust the at least one predictive parameter of progress; (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system and Cherney, [44-45]; These sensed variables may provide inputs to sensor signal processing logic 166 that cause sensor signal processing logic 166 to generate a suggested change in operating parameters, machine settings, machine configuration, etc. That information can be output to speech processing system 170 and a synthesized message can be played for operator 116 suggesting a settings change, a machine configuration change, or another operational parameter change for machine 102. The machine can be automatically controlled to make those changes and a speech output can be generated to notify the operator of the change. These are just examples of sensor inputs that can trigger speech processing services and examples of services that can be performed, and a wide variety of others can be used as well. Using a sensor input to trigger speech processing services is indicated by block 206 in the flow diagram of FIG. 3… It may also be that operator 116 provides another input, through another user interface mechanism 150, and this may trigger speech services. Triggering speech services based on another operator input is indicated by block 208). Examiner notes that while Cherney teaches outputting recommendations the second recommendation would also be duplication of parts In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960) (Claims at issue were directed to a water-tight masonry structure wherein a water seal of flexible material fills the joints which form between adjacent pours of concrete. The claimed water seal has a "web" which lies in the joint, and a plurality of "ribs" projecting outwardly from each side of the web into one of the adjacent concrete slabs. The prior art disclosed a flexible water stop for preventing passage of water between masses of concrete in the shape of a plus sign (+). Although the reference did not disclose a plurality of ribs, the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including generating an data indicative of predictive parameters of progress with the teachings of Cherney including receiving user input and a responsive recommendation in order to allow for remote users effectively control an operation (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system (e.g., remote machine control logic 125) to generate control signals to change the settings on the target machine. Generating control signals to adjust settings is indicated by block 238).
While Wesley/Cherney teach a plurality of recommendations of operational adjustments, neither appear to explicitly teach selected a recommendation. However, Wesley/ Cherney in view of the analogous art of Dawson (i.e. operations management) does teach: and obtaining an operational adjustment preference selection input selecting the first recommended operational adjustment (Dawson, [42]; The example suggestions may be selectable within the user interface 114 allowing a user to accept suggestions (and modify them if desired) within the user interface 114, causing the appropriate preference cards to be updated by the SPMS 110).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley/ Cherney including a plurality of recommendations of operational adjustments with the teachings of Dawson including selecting a recommendation in order to determine user preferences, improve performances, save cost, and reduce waste. (Dawson, [23]; the present disclosure addresses these and other shortcomings by gathering and storing preference cards electronically and coordinating with existing medical information systems (i.e., electronic health records, scheduling systems, billing systems, etc.) to monitor and gather data related to those preference cards on an ongoing basis to identify opportunities to modify preference cards to achieve improved surgical outcomes, immediate costs savings by reducing waste, and indirect cost savings due to increased automation. Relevant data and suggestions are presented directly to users who directly drive costs and outcomes in an integrated environment which allows changes to be implemented immediately.
Regarding Claims 19, Wesley teaches: A computer implemented method of controlling a mobile work machine, the computer implemented method comprising: obtaining a first operational strategy input identifying a first operational strategy and including a plan and a constraint; (Wesley, [abstract]; Systems, methods, and computer-program products can perform task-based management of a mine site. The systems, methods, and computer-program products can generate one of a specialized management operator interface or a specialized active work operator interface, for selective display on a display device and Wesley, [30]; FIG. 2 is a schematic illustration of the mining management system 12. The mining management system 12 may receive data, for the planning and/or management of the mine site 10, including mine site-related data and mine site planning data (i.e., one or more plans each with one or more jobs for the mine site 10). Optionally, mining management system 12 can receive other types of information that may be helpful or useful for mine site planning and/or management, such, but not limited to, as market data, weather data, delivery site data, and feed rate of crusher and Wesley, [35]; The operator control interface 31 can also receive inputs, control and/or data, for planning and/or management of the mine site 10 and Wesley, [216]; as used herein, the term “circuitry” can refer to any or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry); (b) to combinations of circuits and software (and/or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s)/software (including digital signal processor(s)), software and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and (c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present). Examiner interprets the displaying of the optimized plan as a control signal to cause the interface to display)).
obtaining a first progress perspective selection input identifying a first progress perspective selection and including a scope and a progress definition; (Wesley, [97]; The information provided by the specialized management operator interface 330 can identify compliance to plan, that is, current and forecasted information regarding whether progress is according to plan or not. This can indicate behind schedule or even ahead of schedule. In this regard, the specialized management operator interface 330 can identify which aspects are progressing according to plan and which aspects are not progressing according to plan. Here, cycles can be allocated to individual jobs and tasks so that the specialized management operator interface 330 can provide direct feedback).
obtaining historical data; (Wesley, [85]; The allocation of ‘Available’ tasks over a SIC window may be referred to or characterized as dispatch to plan using short interval control. Put more generally, the assignment engine 27 can take ‘Available’ tasks and also other input data (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data, etc.), and/or operator-related data), identify and allocate a best or optimal configuration of entities (e.g., machines and/or operators)) based on the ‘Available’ tasks and input data, and determine how best to coordinate the activities of some or all of the work machines to achieve the tasks of the short-term plan).
obtaining forecast data; (Wesley, [43]; the forecasting engine 28, and the active work engine 29 may operate at the same time or in parallel (e.g., in the background while another is operating). According to one or more embodiments, caches or tables of tasks, production arcs, and forecast rates, as defined herein, may constitute an integration point between the assignment engine 27 and each of the forecasting engine 28 and Wesley, [61]; A forecast rate can be regarded as a rate over a specific time period for a specific machine. The forecast rate may be used to compile metrics and predict completion times for auto-assigned tasks (‘Ready’ and ‘Available’ tasks). According to one or more embodiments, forecast rates may be produced by the forecasting engine 28 for ‘Ready’ and ‘Available’ tasks. Optionally, there may be multiple forecast rates for a single work machine (e.g., one of the hauling machines 50) during a given time period where the work machine is referenced in, or assigned with, multiple tasks. A forecast arc can include start and end times so that changes in forecast production rates may be modeled across planned delays (i.e., operational tasks) and Wesley, [104]; Turning now specifically to forecasting, in the context of the present disclosure, forecasting can be regarded as involving incremental calculation of production rates and completion times for tasks, loading tools (e.g., digging machine 52), and processors (e.g., a crusher at processing plant 48). Calculations or determinations can be performed, for instance, by the controller 20, for individual work machines such that the forecasting performance can scale or be scaled proportionally to the changes made and not to the size of the mine site 10. For example, if a change is made that invalidates the forecasts for five (5) tasks, the forecasting can respond with updated forecasts in a defined, consistent time regardless of the size of the mine site 10, e.g., no matter how many machines are working at the mine site 10. The forecasting can predict future production rates for loading tools (e.g., digging machine 52) and processors (e.g., a crusher at processing plant 48).
generating a first output including first perspective data indicative of a predictive progress corresponding to the first progress perspective selection based on the historical data, the forecast data, the first operational strategy, and the first progress perspective selection; (Wesley, [29]; In embodiments of the present disclosure, current and/or historical cycles may be matched to tasks to determine progress towards task completion and current and historical production rates, respectively and Wesley, [81]; assignment may be referred to or characterized as backend processing by the assignment engine 27 to assign tasks, for instance, to enhance (automate or optimize) the completion of tasks, i.e., compliance to plan over time, rather than necessarily optimizing to static production rates, i.e., compliance to static rates. That is, the assignment engine 27 can, based on the inputs thereto (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data), and/or operator-related data), automatically identify and allocate an enhanced (e.g., optimal) configuration of entities (e.g., work machines and/or operators) to complete each task of the short-term plan and Wesley, [97]; The information provided by the specialized management operator interface 330 can identify compliance to plan, that is, current and forecasted information regarding whether progress is according to plan or not. This can indicate behind schedule or even ahead of schedule). Examiner interprets compliance to a plan, configuration of entities to complete task, and telemetry data as data indicative of one or more predictive parameters of progress as the cited parameters would all indicate expected progress.
controlling the user associated system based on the selected recommended operational adjustment. (Wesley, [76]; Referring again specifically to the specialized analyzing operator interface, as noted above, the specialized analyzing operator interface can provide planning and/or management information such as the short-term plan or plans, the job or jobs associated with the short-term plan(s), the task or tasks associated with each job (e.g., operational tasks and/or non-production tasks), equipment, sources, dumps, materials, status of each of the tasks, prioritization or rank for the tasks, and task backlog information and Wesley, [124]; an artificial intelligence (AI) tool may be implemented to annotate the Spark Chart(s). Generally, the AI tool can analyze the data and provide recommendations, output on the Spark Charts, for instance, for how to improve production. For instance, a pop-up may be generated on the Spark Chart. As a specific example, based on the pop-up the operator could say at this time it looks like production is dropping, so extra capacity exists. The AI tool may recommend specific changes to the tasks to potentially make to increase overall production). Examiner notes that Wesley teaches controlling a mobile work machine but relies on Cherney below to explicitly teach the recommended operational adjustment.). Examiner interprets the displaying of the optimized plan as a control signal to cause the interface to display. Examiner notes that Dawson below teaches the selected recommended adjustment.
While Wesley teaches generating data indicative of predictive parameters of progress and controlling a work machine, Wesley does not appear to teach obtaining user input to generate recommended adjustments. However, Wesley in view of the analogous art of Cherney (i.e. operations management) does teach: obtaining, in response to the first output, a user query input querying how to adjust the predictive progress; generating, in response to the user query input, a plurality of recommended operational adjustments; (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system and Cherney, [44-45]; These sensed variables may provide inputs to sensor signal processing logic 166 that cause sensor signal processing logic 166 to generate a suggested change in operating parameters, machine settings, machine configuration, etc. That information can be output to speech processing system 170 and a synthesized message can be played for operator 116 suggesting a settings change, a machine configuration change, or another operational parameter change for machine 102. The machine can be automatically controlled to make those changes and a speech output can be generated to notify the operator of the change. These are just examples of sensor inputs that can trigger speech processing services and examples of services that can be performed, and a wide variety of others can be used as well. Using a sensor input to trigger speech processing services is indicated by block 206 in the flow diagram of FIG. 3… It may also be that operator 116 provides another input, through another user interface mechanism 150, and this may trigger speech services. Triggering speech services based on another operator input is indicated by block 208).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including generating an data indicative of predictive parameters of progress with the teachings of Cherney including receiving user input and a responsive recommendation in order to allow for remote users effectively control an operation (Cherney, [52]; it may be that remote user 134 provides a spoken input to adjust settings on mobile machine 102. In that case, one of the speech processing systems in architecture 100 (e.g., system 123 or 126) will perform speech recognition and natural language understanding on that input and use a control system (e.g., remote machine control logic 125) to generate control signals to change the settings on the target machine. Generating control signals to adjust settings is indicated by block 238).While Wesley/ Cherney teach a plurality of recommendations of operational adjustments, neither appear to explicitly teach selected a recommendation. However, Wesley/ Cherney in view of the analogous art of Dawson (i.e. operations management) does teach: obtaining, a user selection input, selecting one of the plurality recommended operational adjustments as a selected recommended operational adjustment: and (Dawson, [42]; The example suggestions may be selectable within the user interface 114 allowing a user to accept suggestions (and modify them if desired) within the user interface 114, causing the appropriate preference cards to be updated by the SPMS 110).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley/Cherney including a plurality of recommendations of operational adjustments with the teachings of Dawson including selecting a recommendation in order to determine user preferences, improve performances, save cost, and reduce waste. (Dawson, [23]; the present disclosure addresses these and other shortcomings by gathering and storing preference cards electronically and coordinating with existing medical information systems (i.e., electronic health records, scheduling systems, billing systems, etc.) to monitor and gather data related to those preference cards on an ongoing basis to identify opportunities to modify preference cards to achieve improved surgical outcomes, immediate costs savings by reducing waste, and indirect cost savings due to increased automation. Relevant data and suggestions are presented directly to users who directly drive costs and outcomes in an integrated environment which allows changes to be implemented immediately.
Claims 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wesley et al. (US 20240249370 A1) in view of Cherney et al. (US 20190198015 A1), and Eder et al. (US 20190362436 A1).
Regarding Claims 9 and 18, generating the first output including the first progress perspective data based further on the one or more of the model selection and the output selection. (Wesley, [81]; assignment may be referred to or characterized as backend processing by the assignment engine 27 to assign tasks, for instance, to enhance (automate or optimize) the completion of tasks, i.e., compliance to plan over time, rather than necessarily optimizing to static production rates, i.e., compliance to static rates. That is, the assignment engine 27 can, based on the inputs thereto (e.g., mine plan or plans, mine site data (e.g., received in real time and/or historical), machine-related data (e.g., telemetry data received in real time and/or historical, status data), and/or operator-related data), automatically identify and allocate an enhanced (e.g., optimal) configuration of entities (e.g., work machines and/or operators) to complete each task of the short-term plan). Examiner notes that the combination of Wesley and Eder is relied upon to teach the entirety of the claim while Wesley is relied upon to teach generating an output based on progress perspective data and various other metric while Eder teaches the model selection limitations.
While Wesley teaches modeling data, Wesley does not appear to teach a model selection. However, Wesley in view of the analogous art of Eder (i.e. process optimization) does teach: The computer implemented method of claim 1 and further comprising: obtaining one or more of a model selection input indicative of a model selection and an output selection input indicative of an output selection; and (Eder, [168]; After the causal predictive model bots complete their processing for each model, the software in block 313 uses a model selection algorithm to identify the model that best fits the data for each element of performance, sub-element of performance and external factor being analyzed. For the system of the present invention, a cross validation algorithm is used for model selection and (Eder, [claim 2]; wherein developing the measure context layer by learning from the data comprises completing a multi-stage process that includes multiple stages for developing a linear or nonlinear predictive measure model wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including modeling data with the teachings of Eder including model/output selection in order to find the best model for the specific datasets (Eder, [168]; the causal predictive model bots complete their processing for each model, the software in block 313 uses a model selection algorithm to identify the model that best fits the data for each element of performance, sub-element of performance and external factor being analyzed).
Claims 12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wesley et al. (US 20240249370 A1) in view of Cherney et al. (US 20190198015 A1), Readick et al. (US 20230177330 A1) and Dawson et al. (US 20220246287 A1). Examiner notes that claim 12 does not require the Dawson prior art reference but is included for claim 20 only as the prior art is used to reject independent claim 19.
Regarding Claims 12 and 20, While Wesley teaches a plurality of progress parameters, Wesley does not appear to teach a harvest date. However, Wesley/Readick teaches: The computer implemented method of claim 19, wherein the date comprises a date of harvest (Readick, [04]; In some embodiments, the current cultivar condition, the historical cultivar condition, or both comprises a condition of a field of plants. In some embodiments, the cultivar regimen recommendation, the historical cultivar regimen, or both comprises a fertilizer quantity adjustment, a fertilizer type adjustment, a pruning quantity adjustment, a pruning location adjustment, a pesticide quantity adjustment, a pesticide type adjustment, a planting date adjustment, a harvesting date adjustment, an irrigation quantity adjustment, an irrigation time of day adjustment, an irrigation schedule adjustment, a crop type adjustment, or any combination thereof.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wesley including a plurality of progress parameters with the teachings of Readick including specific parameter such as harvest date in order to allow for adjustment of a plurality of parameters for further optimization of systems. (Readick, [35]; Further while measurement of some data sources can be performed autonomously and/or continuously, other qualitative or quantitative measurement means require manned resources. As many farmers plant large quantities of each crop, the difference between an optimal cultivar regimen and a non-optimal cultivar regimen can equate to a large difference in the yield and income for a harvesting season. As such, the algorithms, methods, and platforms herein enable capture of cultivar conditions and determination of cultivar recommendations on various time and data size scales).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/ Primary Examiner, Art Unit 3624
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