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 .
Status of Claims
Claims 1-20 are pending in this application.
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gattis et al. (US Publication 2025/0291366 A1) in view of Iynoolkhan et al. (US Patent 11,012,526 B1).
Regarding claim 1, Gattis teaches a computer-implemented method for dynamic workflow adjustments to assist heavy machines involved in accidental scenarios, the method comprising: monitoring real-time data associated with a first activity area where a first heavy machine is performing an activity (Gattis: Para. 128; working machines status updates may include position information, coverage area, obstacle information, path adjustments, speed, current operational diagnostics); analyzing the real-time data (Gattis: Para. 126, 128; Using the updated information, the cloud management system may confirm whether or not the working machines are operating consistent with the current mission plan or had to deviate); ………. ; analyzing a knowledge repository pertaining to capabilities of the first heavy machine and one or more other heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology); identifying a second heavy machine of the one or more other heavy machines to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63, 236; machines covering the needed capabilities for an operation may be prioritized based on proximity to a section or project field for the operation; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate for overall timing and efficiency) ………. ; and deploying the second heavy machine to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository.
However Iynoolkhan, in the same field of endeavor, teaches inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63; in the case of a fire destroying a property, the lead UAV may quickly assess sections of the property that have not yet been destroyed, and deploy multiple UAVs to quickly capture field data from those sections; machine learning system may be trained to analyze images to determine a geographic region that may be impacted, and quickly determine resources that may be available) ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository (Iynoolkhan: Col. 9 Lines 10-16; based on a direction of wind and an amount of rain, machine learning system 210 may infer that there is potential damage to a different portion of the roof).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 2, Gattis teaches the method as recited in claim 1 further comprising: adjusting a first workflow of the first heavy machine to accommodate support actions from the second heavy machine (Gattis: Para. 63; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach adjusting a second workflow of the second heavy machine to temporarily pause activity of the second heavy machine being performed in a second activity area and to include tasks to be performed at the first activity area.
However, Gattis is deemed to disclose an equivalent teaching. The detection of an unanticipated obstacle is a trigger that alters the standard operation of the vehicle (Gattis: Para. 206). Gattis teaches a first applicator vehicle that detects obstacles in its planned boundary and thus operates at a slower pace and gets behind its scheduled time (Gattis: Para. 63). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or specific project operations (Gattis: Para. 236). It would be obvious to pause the second heavy machine’s activity in the second area so that the second heavy vehicle can aid the first vehicle in the first area when the first vehicle is behind schedule due to detected obstacles and first area priority.
It would have been obvious to one of ordinary skill before the effective filing date to pause the activity of the second vehicle in the second area to do work in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Regarding claim 3, Gattis teaches the method as recited in claim 2 further comprising: deploying the second heavy machine to perform the adjusted second workflow (Gattis: Para. 128; if the working machine has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift to another available and capable machine to compensate for overall timing and efficiency to complete the operations).
Regarding claim 4, Gattis doesn’t explicitly teach deploying the second heavy machine to resume activities from a paused position in a second activity area in response to resolving the inferred accidental scenario.
However, Gattis is deemed to disclose an equivalent teaching. Gattis teaches a mission plan of a starting point, a travel path, and boundaries split between the vehicles (Gattis: Para. 103). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or project field (Gattis: Para. 236).When the second vehicle is done helping the first vehicle in the first area and returns to the second area, the second vehicle resumes its activity upon entering the second boundary.
It would have been obvious to one of ordinary skill before the effective filing date for the second vehicle to resume activities in the second area when the inferred accidental scenario is resolved in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Regarding claim 5, Gattis teaches the method as recited in claim 1 further comprising: receiving a first set of data associated with activity areas where heavy machines are performing various activities (Gattis: Para. 128; working machines status updates may include position information, coverage area, obstacle information, path adjustments, speed, current operational diagnostics); receiving a second set of data pertaining to capabilities of heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology).
Gattis doesn’t explicitly teach receiving a third set of data pertaining to accidental scenarios involving heavy machines in activity areas; and building and training the first artificial intelligence model to infer an accidental scenario using the first, second, and third sets of received data.
However Iynoolkhan, in the same field of endeavor, teaches receiving a third set of data pertaining to accidental scenarios involving heavy machines in activity areas (Iynoolkhan: Col. 9 Lines 10-16, Col. 14 Lines 32-39, Col. 14 Lines 50-63; machine learning system 303 may analyze historical field data to associate images with types of damage, extent of damage, type of material); and building and training the first artificial intelligence model to infer an accidental scenario using the first, second, and third sets of received data (Iynoolkhan: Col. 14 Lines 50-63; machine learning system may be trained to analyze images to determine a geographic region that may be impacted, and quickly determine resources that may be available; upon a determination that the affected area is a rural region, machine learning system may be trained to identify nearby available resources, and estimate a time taken for the resources to arrive).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 6, Gattis teaches the method as recited in claim 1 further comprising: receiving historical data comprising capabilities of heavy machines, proximity of assisting heavy machines to assisted heavy machine, availability of assisting heavy machines to assist heavy machine, operational status of assisting heavy machines, capability scores, and accidental scenario priorities (Gattis: Para. 33, 236; use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology; the mission planning system prioritizes operations for the machines based on capabilities and current status).
Gattis doesn’t explicitly teach building and training the second artificial intelligence model to identify one or more heavy machines to assist a heavy machine engaged in an activity involving an inferred accidental scenario using the historical data.
However Iynoolkhan, in the same field of endeavor, teaches building and training the second artificial intelligence model to identify one or more heavy machines to assist a heavy machine engaged in an activity involving an inferred accidental scenario using the historical data (Iynoolkhan: Col. 9 Lines 9-16; field UAVs may have sent an image of damage to one side of a roof; based on a direction of wind and an amount of rain, machine learning system may infer that there is potential damage to a different portion of the roof; UAV management software may direct Field UAVs, or deploy an additional UAV, to capture additional images of the potentially damaged portions of the roof).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 7, Gattis teaches the method as recited in claim 1 further comprising: analyzing the knowledge repository pertaining to the capabilities of the first heavy machine and the one or more other heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology), proximity of the one or more other heavy machines to the first heavy machine (Gattis: Para. 236; machines covering the needed capabilities for an operation may be prioritized based on proximity to a section), availability of assisting the first heavy machine by the one or more other heavy machines (Gattis: Para. 145, 147; determine which machines are available), operational status of the one or more other heavy machines, and priority of the inferred accidental scenario (Gattis: Para. 236; prioritizes operations for the machines based on capabilities and current status; priority may be based on any number of factors, such as precision, efficiency, machine location, terrain capabilities, user indications or any other factor impacting preferred use between machines).
Regarding claim 8, Gattis teaches the method as recited in claim 1, wherein the first and second heavy machines are autonomous heavy machines (Gattis: Para. 122; automated working machine system; two working machines; working machines may be tractors, lawnmowers, agricultural equipment, road construction equipment, terraforming equipment, building machines).
Regarding claim 9, Gattis teaches a computer program product for dynamic workflow adjustments to assist heavy machines involved in accidental scenarios, the computer program product comprising: a set of one or more computer-readable storage media; and program instructions, collectively stored in the set of one or more computer-readable storage media, for causing a processor set to perform the following computer operations: (Gattis: Para. 291; computer programs generally comprise instructions that are stored in machine-readable; processors) monitoring real-time data associated with a first activity area where a first heavy machine is performing an activity (Gattis: Para. 128; working machines status updates may include position information, coverage area, obstacle information, path adjustments, speed, current operational diagnostics); analyzing the real-time data (Gattis: Para. 126, 128; Using the updated information, the cloud management system may confirm whether or not the working machines are operating consistent with the current mission plan or had to deviate); …….. ; analyzing a knowledge repository pertaining to capabilities of the first heavy machine and one or more other heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology); identifying a second heavy machine of the one or more other heavy machines to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63, 236; machines covering the needed capabilities for an operation may be prioritized based on proximity to a section or project field for the operation; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate for overall timing and efficiency) ……. ; and deploying the second heavy machine to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository.
However Iynoolkhan, in the same field of endeavor, teaches inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63; in the case of a fire destroying a property, the lead UAV may quickly assess sections of the property that have not yet been destroyed, and deploy multiple UAVs to quickly capture field data from those sections; machine learning system may be trained to analyze images to determine a geographic region that may be impacted, and quickly determine resources that may be available) ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository (Iynoolkhan: Col. 9 Lines 10-16; based on a direction of wind and an amount of rain, machine learning system 210 may infer that there is potential damage to a different portion of the roof).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 10, Gattis teaches the computer program product as recited in claim 9, wherein the program instructions cause the processer set to perform the following computer operation: adjusting a first workflow of the first heavy machine to accommodate support actions from the second heavy machine (Gattis: Para. 63, 115; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach adjusting a second workflow of the second heavy machine to temporarily pause activity of the second heavy machine being performed in a second activity area and to include tasks to be performed at the first activity area.
However, Gattis is deemed to disclose an equivalent teaching. The detection of an unanticipated obstacle is a trigger that alters the standard operation of the vehicle (Gattis: Para. 206). Gattis teaches a first applicator vehicle that detects obstacles in its planned boundary and thus operates at a slower pace and gets behind its scheduled time (Gattis: Para. 63). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or specific project operations (Gattis: Para. 236). It would be obvious to pause the second heavy machine’s activity in the second area so that the second heavy vehicle can aid the first vehicle in the first area when the first vehicle is behind schedule due to detected obstacles and first area priority.
It would have been obvious to one of ordinary skill before the effective filing date to pause the activity of the second vehicle in the second area to do work in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Regarding claim 11, Gattis teaches the computer program product as recited in claim 10, wherein the program instructions cause the processer set to perform the following computer operation: deploying the second heavy machine to perform the adjusted second workflow (Gattis: Para. 128; if the working machine has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift to another available and capable machine to compensate for overall timing and efficiency to complete the operations).
Regarding claim 12, Gattis doesn’t explicitly teach deploying the second heavy machine to resume activities from a paused position in a second activity area in response to resolving the inferred accidental scenario.
However, Gattis is deemed to disclose an equivalent teaching. Gattis teaches a mission plan of a starting point, a travel path, and boundaries split between the vehicles (Gattis: Para. 103). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or project field (Gattis: Para. 236).When the second vehicle is done helping the first vehicle in the first area and returns to the second area, the second vehicle resumes its activity upon entering the second boundary.
It would have been obvious to one of ordinary skill before the effective filing date for the second vehicle to resume activities in the second area when the inferred accidental scenario is resolved in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Regarding claim 13, Gattis teaches the computer program product as recited in claim 9, wherein the program instructions cause the processer set to perform the following computer operation: receiving a first set of data associated with activity areas where heavy machines are performing various activities (Gattis: Para. 128; working machines status updates may include position information, coverage area, obstacle information, path adjustments, speed, current operational diagnostics); receiving a second set of data pertaining to capabilities of heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology).
Gattis doesn’t explicitly teach receiving a third set of data pertaining to accidental scenarios involving heavy machines in activity areas; and building and training the first artificial intelligence model to infer an accidental scenario using the first, second, and third sets of received data.
However Iynoolkhan, in the same field of endeavor, teaches receiving a third set of data pertaining to accidental scenarios involving heavy machines in activity areas (Iynoolkhan: Col. 9 Lines 10-16, Col. 14 Lines 32-39, Col. 14 Lines 50-63; machine learning system 303 may analyze historical field data to associate images with types of damage, extent of damage, type of material); and building and training the first artificial intelligence model to infer an accidental scenario using the first, second, and third sets of received data (Iynoolkhan: Col. 14 Lines 50-63; machine learning system may be trained to analyze images to determine a geographic region that may be impacted, and quickly determine resources that may be available; upon a determination that the affected area is a rural region, machine learning system may be trained to identify nearby available resources, and estimate a time taken for the resources to arrive).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 14, Gattis teaches the computer program product as recited in claim 9, wherein the program instructions cause the processer set to perform the following computer operation: receiving historical data comprising capabilities of heavy machines, proximity of assisting heavy machines to assisted heavy machine, availability of assisting heavy machines to assist heavy machine, operational status of assisting heavy machines, capability scores, and accidental scenario priorities (Gattis: Para. 33, 236; use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology; the mission planning system prioritizes operations for the machines based on capabilities and current status).
Gattis doesn’t explicitly teach building and training the second artificial intelligence model to identify one or more heavy machines to assist a heavy machine engaged in an activity involving an inferred accidental scenario using the historical data.
However Iynoolkhan, in the same field of endeavor, teaches building and training the second artificial intelligence model to identify one or more heavy machines to assist a heavy machine engaged in an activity involving an inferred accidental scenario using the historical data (Iynoolkhan: Col. 9 Lines 9-16; field UAVs may have sent an image of damage to one side of a roof; based on a direction of wind and an amount of rain, machine learning system may infer that there is potential damage to a different portion of the roof; UAV management software may direct Field UAVs, or deploy an additional UAV, to capture additional images of the potentially damaged portions of the roof).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 15, Gattis teaches the computer program product as recited in claim 9, wherein the program instructions cause the processer set to perform the following computer operation: analyzing the knowledge repository pertaining to the capabilities of the first heavy machine and the one or more other heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology), proximity of the one or more other heavy machines to the first heavy machine (Gattis: Para. 236; machines covering the needed capabilities for an operation may be prioritized based on proximity to a section), availability of assisting the first heavy machine by the one or more other heavy machines (Gattis: Para. 145, 147; determine which machines are available), operational status of the one or more other heavy machines, and priority of the inferred accidental scenario (Gattis: Para. 236; prioritizes operations for the machines based on capabilities and current status; priority may be based on any number of factors, such as precision, efficiency, machine location, terrain capabilities, user indications or any other factor impacting preferred use between machines).
Regarding claim 16, Gattis teaches the computer program product as recited in claim 9, wherein the first and second heavy machines are autonomous heavy machines (Gattis: Para. 122; automated working machine system; two working machines; working machines may be tractors, lawnmowers, agricultural equipment, road construction equipment, terraforming equipment, building machines).
Regarding claim 17, Gattis teaches a system, comprising: a memory for storing a computer program for dynamic workflow adjustments to assist heavy machines involved in accidental scenarios; and a processor connected to the memory, wherein the processor is configured to execute program instructions of the computer program comprising: (Gattis: Para. 291; computer programs generally comprise instructions that are stored in machine-readable; processors) monitoring real-time data associated with a first activity area where a first heavy machine is performing an activity (Gattis: Para. 128; working machines status updates may include position information, coverage area, obstacle information, path adjustments, speed, current operational diagnostics); analyzing the real-time data (Gattis: Para. 126, 128; Using the updated information, the cloud management system may confirm whether or not the working machines are operating consistent with the current mission plan or had to deviate); …… ; analyzing a knowledge repository pertaining to capabilities of the first heavy machine and one or more other heavy machines (Gattis: Para. 33, 136; the automation system may use each machine's profile and the mission activity options within the machine's capability to build a common mission plan for the fleet and assign machine specific tasks compatible with each machine's operating technology); identifying a second heavy machine of the one or more other heavy machines to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63, 236; machines covering the needed capabilities for an operation may be prioritized based on proximity to a section or project field for the operation; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate for overall timing and efficiency); and ……… ; and deploying the second heavy machine to assist the first heavy machine to mitigate the inferred accidental scenario (Gattis: Para. 63; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository.
However Iynoolkhan, in the same field of endeavor, teaches inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63; in the case of a fire destroying a property, the lead UAV may quickly assess sections of the property that have not yet been destroyed, and deploy multiple UAVs to quickly capture field data from those sections; machine learning system may be trained to analyze images to determine a geographic region that may be impacted, and quickly determine resources that may be available) ……. by a second trained artificial intelligence model based on the analysis of the knowledge repository (Iynoolkhan: Col. 9 Lines 10-16; based on a direction of wind and an amount of rain, machine learning system 210 may infer that there is potential damage to a different portion of the roof).
It would have been obvious to one having ordinary skill in the art to modify the working machines mission planning system (Gattis: Para. 126, 236) with the trained machine learning system (Iynoolkhan: Col. 14 Lines 50-63) with a reasonable expectation of success because a trained machine learning system can analyze real time UAV images, determine a fire is destroying a property, identify and send nearby fire resistant UAVs to capture field data before the fire destroys the information needed (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63).
Regarding claim 18, Gattis teaches the system as recited in claim 17, wherein the program instructions of the computer program further comprise: adjusting a first workflow of the first heavy machine to accommodate support actions from the second heavy machine (Gattis: Para. 63; if the applicator has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift more of the field to applicator to compensate).
Gattis doesn’t explicitly teach adjusting a second workflow of the second heavy machine to temporarily pause activity of the second heavy machine being performed in a second activity area and to include tasks to be performed at the first activity area.
However, Gattis is deemed to disclose an equivalent teaching. The detection of an unanticipated obstacle is a trigger that alters the standard operation of the vehicle (Gattis: Para. 206). Gattis teaches a first applicator vehicle that detects obstacles in its planned boundary and thus operates at a slower pace and gets behind its scheduled time (Gattis: Para. 63). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or specific project operations (Gattis: Para. 236). It would be obvious to pause the second heavy machine’s activity in the second area so that the second heavy vehicle can aid the first vehicle in the first area when the first vehicle is behind schedule due to detected obstacles and first area priority.
It would have been obvious to one of ordinary skill before the effective filing date to pause the activity of the second vehicle in the second area to do work in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Regarding claim 19, Gattis teaches the system as recited in claim 18, wherein the program instructions of the computer program further comprise: deploying the second heavy machine to perform the adjusted second workflow (Gattis: Para. 128; if the working machine has operated at a slower pace to address detected obstacles and is behind schedule, the cloud management system may adjust the mission plan to shift to another available and capable machine to compensate for overall timing and efficiency to complete the operations).
Regarding claim 20, Gattis doesn’t explicitly teach deploying the second heavy machine to resume activities from a paused position in a second activity area in response to resolving the inferred accidental scenario.
However, Gattis is deemed to disclose an equivalent teaching. Gattis teaches a mission plan of a starting point, a travel path, and boundaries split between the vehicles (Gattis: Para. 103). The vehicle management system adjust the mission plan and shifts more of the field into the second applicator vehicle’s boundary to compensate for the first applicator’s slower pace (Gattis: Para. 63) where the vehicle performs the activity within the boundary and pauses the activity upon leaving the boundary (Gattis: Para. 245). Gattis teaches priority based on proximity to a section or project field (Gattis: Para. 236).When the second vehicle is done helping the first vehicle in the first area and returns to the second area, the second vehicle resumes its activity upon entering the second boundary.
It would have been obvious to one of ordinary skill before the effective filing date for the second vehicle to resume activities in the second area when the inferred accidental scenario is resolved in the first area as taught in Gattis with a reasonable expectation of success because shifting field boundaries between vehicles can compensate for obstacle detection by increasing efficiency due to the priority of the activities (Gattis: Para. 63, 236).
Response to Arguments
Applicant’s arguments, filed 18 April 2026, with respect to the rejection of claims 1-20 under 35 U.S.C. §103 have been fully considered, but they are not persuasive.
The applicant’s attorney argues that “inferring an accidental scenario involving the first heavy machine by a first trained artificial intelligence model based on the analysis of the real-time data” is not taught by the prior arts.
In response to the argument above, Iynoolkhan teaches multiple field vehicles (Iynoolkhan: Col. 1 Lines 42-50) that includes autonomous road vehicles. The applicant’s specification describes some examples of accidental scenarios from paragraph 19 to paragraph 30 which includes an accidental scenario involving fire. The prior art teaches a scenario of a fire destroying a property. The lead uav quickly assess sections of the property that have not yet been destroyed, and deploy multiple UAVs to quickly capture field data from those sections and deploy one or more fire resistant UAVs to capture field data from the sections of the property that are burning. The system uses machine learning that has been trained to associate images with known objects so that it can analyze audio and visual data to quickly inferred upcoming events based on real-time data (Iynoolkhan: Col. 10 Lines 6-12, Col. 14 Lines 50-63). The prior art clearly teaches a first vehicle at an active fire that infers the progression of the fire and property destruction based on real-time data and a machine learning model. The machine learning model trained for the task is a trained artificial intelligence model based on real-time data.
The prior art’s UAV is defined as an unmanned autonomous vehicle, Iynoolkhan also teaches an autonomous road vehicle as a vehicle for their method. In a case of a fire, a first unmanned autonomous vehicle involved in data collection at a fire site can infer the accidental scenario of structural instability or fire consumption of the building based on the real-time data.
The applicant next argues that “identifying a second heavy machine of the one or more other heavy machines to assist the first heavy machine to mitigate the inferred accidental scenario by a second trained artificial intelligence model based on the analysis of the knowledge repository” is not taught by the prior arts.
In response to the argument above, Gattis teaches a cloud management system that watches the operation of farming machine that is slower than its expected based on detected obstacles. The system will prioritize operations for the group of machines based on capabilities and current status. The system can adjust the mission plan to shift more of the work to a second farming machine based on its proximity to a section of the field (Gattis: Para. 33, 136).
Iynoolkhan teaches a machine learning system that infers the potential damage to a different portion of the roof based on the direction of wind and an amount of rain (Iynoolkhan: Col. 9 Lines 10-16). The first set of data is the real-time fire data collected by the unmanned autonomous vehicle and items can be inferred by this real time data. The system adds in wind direction and amount of rain that is based on a knowledge database with the current real-time data to have a better estimation about the progression of the fire.
Both Gattis and Iynoolkhan teach multiple machines, real-time data, and reallocation of vehicles based on decisions made from the current data. Gattis’s main example is based on the work allocation of farm machines which are heavy machines. Iynoolkhan’s example is of UAV at a dangerous scenario of a fire. The combination of using the predictive machine learning model with Gattis’s cloud management system teaches the claimed limitation.
The applicant next argues that “deploying the second heavy machine to assist the first heavy machine to mitigate the inferred accidental scenario” is not taught by the prior arts.
In response to the argument above, Gattis does detect a slow pace to address detected obstacles. The system moves a second applicator to the field to compensate and finish the field fertilization operation (Gattis: Para. 63). Gattis teaches a series of unexpected obstacles that it must drive around (Gattis: Para. 58). With a series of unexpected obstacles it would be easy to reallocate a vehicle on the other side of the unexpected obstacles to complete that portion of the field instead of risking the first vehicle traversing an obstacle laden area. This mitigates the inferred accidental scenario for the first vehicle by sending the part of the field on the opposite side of the series of unexpected obstacles to a second vehicle. This reallocated compensates for the detected series of unexpected obstacles for better overall timing and efficiency to complete the task.
The applicant next argues that claims 2-8 depend on claim 1 and are allowable at least based on their dependencies of claim 1.
In response to the argument above, Claim 1 is rejected. Therefore claims 2-8 are rejected at least based on their dependencies.
The applicant next argues that claims 10-16 depend on claim 9 and are allowable at least based on their dependencies of claim 9.
In response to the argument above, Claim 9 is rejected. Therefore claims 10-16 are rejected at least based on their dependencies.
The applicant next argues that claims 18-20 depend on claim 17 and are allowable at least based on their dependencies of claim 17.
In response to the argument above, Claim 17 is rejected. Therefore claims 18-20 are rejected at least based on their dependencies.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Gattis’s cloud management system with Iynoolkhan’s machine learning analysis of real time data reads on applicant’s dynamic workflow adjustment to assist heavy machines involved in accidental scenarios. The rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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