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 1-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 03/17/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The amended claims overcome the 102 rejection by adding new limitations to the independent claims. However, a new 103 rejection is facilitated by the amendments.
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.
Response to Arguments - 35 USC § 102/103
The amended claims overcome the 102 rejection by adding new limitations to the independent claims. However, a new 103 rejection is facilitated by the amendments. The 102/103 Rejections 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 1-20 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 user associated system, 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 a predictive 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 the predictive progress;
generating, in response to the user input, a recommended operational adjustment; and
controlling the user associated system 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 user associated system, 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 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;
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;
controlling the user associated system 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 user associated system (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;. 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 5-9 and 14-18 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 and 11, the claim further recite the additional element(s) of controlling the… system comprises…controlling an interface mechanism… to provide a presentation. 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 3 and 12, the claim further recite the additional element(s) of controlling the… system comprises…controlling… a mobile work machine. Examiner notes that the Applicant’s specification is merely exemplary 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). 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 4 and 13, the claim further recite the additional element(s) of controlling an interface mechanism… to… provide a presentation. Examiner notes that the Applicant’s specification is merely exemplary 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). 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 20, the claim further recite the additional element(s) of controlling an interface mechanism… to provide a presentation or controlling… a 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.
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 1-5, 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 Tajammul et al. (US 20190180399 A1).
Regarding Claims 1 and 10, Wesley teaches: A computer implemented method of controlling a user associated system, 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).
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 a predictive 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).
controlling the user associated system 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 displaying a recommendation but relies on Tajammul below to explicitly teach the recommended operational adjustment.
While Wesley teaches generating an output indicative of predictive progress, Wesley does not appear to teach obtaining user input to generate recommended adjustments. However, Wesley in view of the analogous art of Tajammul (i.e. operations management) does teach: obtaining, in response the first output, a user input querying how to adjust the predictive progress; generating, in response to the user input, a recommended operational adjustment; and (Tajammul, [39]; the forecasting platform can provide the one or more recommendations for display on a user interface of the user device. As shown by reference number 150, the forecasting platform can implementation a recommendation of the one or more recommendations. For example, the forecasting platform can implement the recommendation based on a request from the user device, automatically based on a trigger, and/or the like, as described further herein and Tajammul, [65]; forecasting platform 230 can receive, from user device 210, a request for a simulation of a forecasted change (i.e. a query for how to adjust a predictive progress) in production of a client organization, such as a forecasted change in a number of work orders. The simulation can be used to generate a set of simulated work orders and a set of simulated schedules that can be further processed to generate recommendations associated with preparing for changes in production of the client organization).
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 output indicative of predictive progress with the teachings of Tajammul including obtaining user input to generate recommended adjustments in order to determine actions to improve processes of the organization (Tajammul, [110]; In this way, forecasting platform 230 is able to generate one or more recommendations associated with improving processes of the client organization).
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 11, Wesley teaches: The computer implemented method of claim 1, wherein controlling the user associated system comprises controlling an interface mechanism of the user associated system to provide a presentation indicative of 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 interprets the displaying of a recommended operational adjustment as controlling the interface.
Regarding Claims 3 and 12, Wesley teaches: The computer implemented method of claim 1, wherein controlling the user associated system comprises controlling, as the user associated system, a 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, [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.
Regarding Claims 4 and 13, Wesley teaches: controlling an interface mechanism of the user associated system to provide a presentation indicative of the first performance perspective data (Wesley, [12]; FIGS. 5A-5E show a specific example of a specialized management operator interface (annotated) (spanning the five sheets) according to one or more embodiments of the present disclosure. and Wesley, [Fig. 5C]; visualization of compliance metrics including plan Actual/Target/Forecast values and Wesley, [Fig.8A-8D]; further representation of performance perspective data).
Regarding Claims 5 and 14, Wesley teaches: The computer implemented method of claim 1 and further comprising: wherein the 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 Tajammul et al. (US 20190180399 A1), and Farquhar et al. (US 20140279343 A1).
Regarding Claims 6 and 15, Wesley/Tajammul including a scope and time periods (Wesley, [04]) neither appear to teach an agricultural season teaches. However, Wesley/Tajammul 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/Tajammul 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/Tajammul 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 Tajammul et al. (US 20190180399 A1), and Dawson et al. (US 20220246287 A1).
Regarding Claims 7 and 16, Wesley/Tajammul teaches: The computer implemented method of claim 1, wherein the recommended operational adjustment comprises a first recommended operational adjustment, the computer implemented-and further comprising: generating, in response to the user input, a second recommended operational adjustment; (Tajammul, [39]; the forecasting platform can provide the one or more recommendations for display on a user interface of the user device. As shown by reference number 150, the forecasting platform can implementation a recommendation of the one or more recommendations. For example, the forecasting platform can implement the recommendation based on a request from the user device, automatically based on a trigger, and/or the like, as described further herein and Tajammul, [65]; forecasting platform 230 can receive, from user device 210, a request for a simulation of a forecasted change (i.e. a query for how to adjust a predictive progress) in production of a client organization, such as a forecasted change in a number of work orders. The simulation can be used to generate a set of simulated work orders and a set of simulated schedules that can be further processed to generate recommendations associated with preparing for changes in production of the client organization).
While Wesley/Tajammul teach a plurality of recommendations of operational adjustments, neither appear to explicitly teach selected a recommendation. However, Wesley/Tajammul 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/Tajammul 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 user associated system, 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).
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).
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). 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 an output indicative of predictive progress, Wesley does not appear to teach obtaining user input to generate recommended adjustments. However, Wesley in view of the analogous art of Tajammul (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; (Tajammul, [39]; the forecasting platform can provide the one or more recommendations for display on a user interface of the user device. As shown by reference number 150, the forecasting platform can implementation a recommendation of the one or more recommendations. For example, the forecasting platform can implement the recommendation based on a request from the user device, automatically based on a trigger, and/or the like, as described further herein and Tajammul, [65]; forecasting platform 230 can receive, from user device 210, a request for a simulation of a forecasted change (i.e. a query for how to adjust a predictive progress) in production of a client organization, such as a forecasted change in a number of work orders. The simulation can be used to generate a set of simulated work orders and a set of simulated schedules that can be further processed to generate recommendations associated with preparing for changes in production of the client organization).
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 output indicative of predictive progress with the teachings of Tajammul including obtaining user input to generate recommended adjustments in order to determine actions to improve processes of the organization (Tajammul, [110]; In this way, forecasting platform 230 is able to generate one or more recommendations associated with improving processes of the client organization).
While Wesley/Tajammul teach a plurality of recommendations of operational adjustments, neither appear to explicitly teach selected a recommendation. However, Wesley/Tajammul 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/Tajammul 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 20, Wesley teaches: The computer implemented method of claim 19, wherein controlling the user associated system comprises a 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, [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.
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 Tajammul et al. (US 20190180399 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).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JEREMY L GUNN/ Examiner, Art Unit 3624
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