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
The following claims have been rejected or allowed for the following reasons:
Claim(s) 1-15 is rejected under 35 USC § 103
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-088764, filed on 5/31/22.
Information Disclosure Statement
The information disclosure statement/statements (IDS) were filed on 10/28/25, 10/21/24 and 7/7/26. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
Claim(s) 1, 3-4, 6-7, 9-10 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Ahn (US 20210283773 A1), in further view of LaFergola (US 20100179844 A1), in further view of Akiona (US 20210349473 A1), in further view of Merschformann (NPL | RAWSim-O: A Simulation Framework for Robotic Mobile Fulfillment Systems | 2018).
Regarding claim 1 Ahn teaches An information processing device comprising: at least one processor; and a memory, wherein the at least one processor is configured tocollect collection data including a route plan planned (Ahn [0020] reads “The processor may control the communication interface to transmit, to the terminal device, cleaning result information that includes only cleaning paths of each of a space in which cleaning is performed from among the plurality of spaces.”);
for a plurality of autonomous mobile robots, (Ahn [0054] reads “In FIG. 1, it is described that one robot cleaner and one terminal device are connected in the cleaning system 300, but in the cleaning system 300, there may be a plurality of robot cleaners which simultaneously operate and each robot cleaner can be connected to a plurality of terminal devices. In addition, one terminal device may be connected to a plurality of robot cleaners.”);
a job operating state indicating assignment of a robot operation to a job, (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
a movement trajectory of each of the robots, (Ahn [0015] reads “The cleaning result information may include a plurality of cleaning paths which are partitioned by a plurality of spaces, and the processor may generate a cleaning area image of the robot cleaner by a plurality of spaces based on the plurality of cleaning paths.”);
and an operating state of each of the robots; (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
and including a job required time, (Ahn [0099] reads “Also, the display 220 can display the time required for cleaning and whether an error has occurred when the cleaning result information is displayed. If there is a history of sucking an object or there is a non-cleaned area, this information can be displayed together.”);
Ahn does not teach compute, using the collection data, first evaluation index valuessecond evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot a job utilization ratio, and a robot utilization ratio, and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput at a level of each route in the route plan and including at least an average movement speed for each route in the route plancause a specific display to display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation, the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
compute, using the collection data, first evaluation index valuessecond evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot (LaFergola [0013] reads “The computer system operates in a plurality of report modes that include an impact report mode presenting reports about impacts of material handling vehicles with objects, a maintenance report mode that presents reports about maintenance and service performed on the material handling vehicles, and a verify report mode presenting data about inspections performed on the material handling vehicles. In addition, a metrics report mode provides reports about amounts of work performed by the material handling vehicles, and an overview report mode presents an overview report that summarizes information from each of the impact, maintenance, verify, and metrics report modes.”);
and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput (LaFergola [0074] reads “The vehicle information system 100 can also determine which vehicles in a fleet are operating at better levels of efficiency than other vehicles based on operating parameters, driver skill, and material transportation dynamics. Some types of material handling vehicles work better at horizontal transport, others at putting loads in a rack or taking them down. Some excel at order picking, both at high and low levels. In a given warehouse, the dynamic needs for moving materials may change hourly. The computing system can assign or reassign vehicles to certain tasks and improve throughput as conditions develop.”);
cause a specific display unit to display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation, a display mode enabling comparison of the result system evaluation index values, (LaFergola [0014] reads “The computer system transitions from the overview report mode to another report mode that is selected in response to activation of the user input device. Each of the impact, maintenance, verify, and metrics report modes has a summary display mode, a list display mode, and a trend display mode that are individually selectable in response a user activating the input device to select an element presented on the screen. The summary display mode has a format that graphically presents data complied from the plurality of material handling vehicles at the facility, and the list display mode individually lists data about each of at least some of the plurality of material handling vehicles. The trend display mode graphically presents a trend line of data during a defined time period.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn with that of LaFergola to include a method for determining and analyzing the throughput of an entire robotic system. Doing so would constitute the combination of known methods to yield predictable results since both multi-vehicle robotic systems and metrics for analyzing a system would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of large robotic system in which a user could better understand the overall efficiency of the entire system.
Ahn/LaFergola does not teach a job utilization ratio, and a robot utilization ratio, at a level of each route in the route plan and including at least an average movement speed for each route in the route plan of a cause-system corresponding to each route for the route plan; and a visualization control unit that controls so as to respectively display, on and [[a]] display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map mode enabling comparison of the cause system evaluation index values.
Akiona in analogous art, teaches a job utilization ratio, and a robot utilization ratio, (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.”);
at a level of each route in the route plan and including at least an average movement speed for each route in the route plan(Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola with that of Akiona to include more metrics related to the efficiency that the robotic system is operating at. This would allow for a user of the facility to get a better understanding on where their system can be improved. (Akiona [0005] reads “A fleet or site simulation tool can leverage discreet event simulation (DES). In such a simulation, each task that occurs is treated as an event with a set duration. When the timer for that event ends, the actor, e.g., robot or human, is free to begin another task. Although such an approach is highly scalable and makes it easy to model individual tasks, it can be overly simplistic for modeling complex, dynamic systems such as mobile robots and people that share a facility with the robots. For example, a DES approach may always indicate that tasks are performed faster with the addition of more robots, ignoring the effects of bottlenecks and gridlock caused by multiple robots and/or other actors occupying the same area. In addition, such an approach does not model coexistence-based interactions such as congestion and suffers from difficulties in capturing interactions and dependencies between multiple actors.”);
Ahn/LaFergola/Akiona does not teach and [[a]] display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
Merschformann in analogous art, teaches and [[a]] display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map(Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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Merschformann figure 5
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona with that of Merschformann to include a method for graphically viewing robot speed through the environment. This would allow the operator to better understand the environment that its robots are operating in. (Merschformann introduction reads “Due to the rise of e-commerce, the traditional manual picker-to-parts warehousing systems no longer work efficiently, and new types of warehousing systems are required, such as automated parts-to-picker systems. For details about the classification of different warehousing systems we refer to [8]. This paper studies one of the parts-to-picker systems, a so-called Robotic Mobile Fulfillment Systems (RMFS), such as the Kiva System ([4], nowadays Amazon Robotics), the GreyOrange Butler or the Swisslog CarryPick. The approach of an RMFS completely eliminates the need for travel within the warehouse, which accounts for approximately 50% of a picker’s time in manual warehouse operations according to [14]. [15] indicates that the Kiva System increases the productivity two to three times, in contrast to a traditional manual picker-to-parts system. Compared to other kinds of warehousing systems, the biggest advantages of an RMFS are its flexibility as a result of having virtually no fixed installations, the scalability due to accessing the inventory in a parallel manner, and the reliability due to the use of only homogeneous components, i.e., redundant components may compensate for faulty ones (see [5] and [16])”);
Regarding claim 3 Ahn/LaFergola/Akiona/Merschformann teaches The information processing device of claim 1, wherein: the analysis unit computes the at least one processor is configured to compute an average movement speed for each route in the route plan as the cause- system second evaluation index values; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
and the visualization control unit displays display the cause-system second display mode as a graph map so as to enable comparison of the average movement speed for each of the routes. (Akiona [0012] reads “The simulation techniques can use a graph that represents the physical area and that includes area nodes that represent the various regions of the area and terminal nodes that represent the location at which tasks are performed. The simulation techniques can also use agents that represent the various actors that perform tasks or otherwise move about the area. The agent for an actor, e.g., robot or person, can include a state machine and model that defines how the actor traverses the area and performs tasks that is used in the simulation to determine/adjust the durations of traversing area nodes and performing tasks at terminal nodes.”);
Regarding claim 4 Ahn/LaFergola/Akiona/Merschformann teaches The information processing device of claim 3, wherein the visualization control unit displays the at least one processor is configured to cause the display to display the average movement speed on the routes as a heat map so as to enable comparison. (Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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Regarding claim 6 Ahn teaches An information processing method executed by a computer and comprising: collecting collection data including a route plan planned (Ahn [0020] reads “The processor may control the communication interface to transmit, to the terminal device, cleaning result information that includes only cleaning paths of each of a space in which cleaning is performed from among the plurality of spaces.”);
for a plurality of autonomous mobile robots, (Ahn [0054] reads “In FIG. 1, it is described that one robot cleaner and one terminal device are connected in the cleaning system 300, but in the cleaning system 300, there may be a plurality of robot cleaners which simultaneously operate and each robot cleaner can be connected to a plurality of terminal devices. In addition, one terminal device may be connected to a plurality of robot cleaners.”);
a job operating state indicating assignment of a robot operation to a job, (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
a movement trajectory of each of the robots, (Ahn [0015] reads “The cleaning result information may include a plurality of cleaning paths which are partitioned by a plurality of spaces, and the processor may generate a cleaning area image of the robot cleaner by a plurality of spaces based on the plurality of cleaning paths.”);
and an operating state of each of the robots; (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
and including a job required time, (Ahn [0099] reads “Also, the display 220 can display the time required for cleaning and whether an error has occurred when the cleaning result information is displayed. If there is a history of sucking an object or there is a non-cleaned area, this information can be displayed together.”);
Ahn does not teach using the collection data to compute: first evaluation index values second evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot a job utilization ratio, and a robot utilization ratio, and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput at a level of each route in the route plan and including at least an average movement speed for each route in the route plancausing a display to: display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation; and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
LaFergola in analogous art, teaches using the collection data to compute: first evaluation index values second evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot (LaFergola [0013] reads “The computer system operates in a plurality of report modes that include an impact report mode presenting reports about impacts of material handling vehicles with objects, a maintenance report mode that presents reports about maintenance and service performed on the material handling vehicles, and a verify report mode presenting data about inspections performed on the material handling vehicles. In addition, a metrics report mode provides reports about amounts of work performed by the material handling vehicles, and an overview report mode presents an overview report that summarizes information from each of the impact, maintenance, verify, and metrics report modes.”);
and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput (LaFergola [0074] reads “The vehicle information system 100 can also determine which vehicles in a fleet are operating at better levels of efficiency than other vehicles based on operating parameters, driver skill, and material transportation dynamics. Some types of material handling vehicles work better at horizontal transport, others at putting loads in a rack or taking them down. Some excel at order picking, both at high and low levels. In a given warehouse, the dynamic needs for moving materials may change hourly. The computing system can assign or reassign vehicles to certain tasks and improve throughput as conditions develop.”);
and causing a display to: display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation; (LaFergola [0014] reads “The computer system transitions from the overview report mode to another report mode that is selected in response to activation of the user input device. Each of the impact, maintenance, verify, and metrics report modes has a summary display mode, a list display mode, and a trend display mode that are individually selectable in response a user activating the input device to select an element presented on the screen. The summary display mode has a format that graphically presents data complied from the plurality of material handling vehicles at the facility, and the list display mode individually lists data about each of at least some of the plurality of material handling vehicles. The trend display mode graphically presents a trend line of data during a defined time period.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn with that of LaFergola to include a method for determining and analyzing the throughput of an entire robotic system. Doing so would constitute the combination of known methods to yield predictable results since both multi-vehicle robotic systems and metrics for analyzing a system would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of large robotic system in which a user could better understand the overall efficiency of the entire system.
Ahn/LaFergola does not teach a job utilization ratio, and a robot utilization ratio, at a level of each route in the route plan and including at least an average movement speed for each route in the route planand display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
Akiona in analogous art, teaches a job utilization ratio, and a robot utilization ratio, (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.”);
at a level of each route in the route plan and including at least an average movement speed for each route in the route plan(Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola with that of Akiona to include more metrics related to the efficiency that the robotic system is operating at. This would allow for a user of the facility to get a better understanding on where their system can be improved. (Akiona [0005] reads “A fleet or site simulation tool can leverage discreet event simulation (DES). In such a simulation, each task that occurs is treated as an event with a set duration. When the timer for that event ends, the actor, e.g., robot or human, is free to begin another task. Although such an approach is highly scalable and makes it easy to model individual tasks, it can be overly simplistic for modeling complex, dynamic systems such as mobile robots and people that share a facility with the robots. For example, a DES approach may always indicate that tasks are performed faster with the addition of more robots, ignoring the effects of bottlenecks and gridlock caused by multiple robots and/or other actors occupying the same area. In addition, such an approach does not model coexistence-based interactions such as congestion and suffers from difficulties in capturing interactions and dependencies between multiple actors.”);
Ahn/LaFergola/Akiona does not teach and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
Merschformann in analogous art, teaches and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map(Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona with that of Merschformann to include a method for graphically viewing robot speed through the environment. This would allow the operator to better understand the environment that its robots are operating in. (Merschformann introduction reads “Due to the rise of e-commerce, the traditional manual picker-to-parts warehousing systems no longer work efficiently, and new types of warehousing systems are required, such as automated parts-to-picker systems. For details about the classification of different warehousing systems we refer to [8]. This paper studies one of the parts-to-picker systems, a so-called Robotic Mobile Fulfillment Systems (RMFS), such as the Kiva System ([4], nowadays Amazon Robotics), the GreyOrange Butler or the Swisslog CarryPick. The approach of an RMFS completely eliminates the need for travel within the warehouse, which accounts for approximately 50% of a picker’s time in manual warehouse operations according to [14]. [15] indicates that the Kiva System increases the productivity two to three times, in contrast to a traditional manual picker-to-parts system. Compared to other kinds of warehousing systems, the biggest advantages of an RMFS are its flexibility as a result of having virtually no fixed installations, the scalability due to accessing the inventory in a parallel manner, and the reliability due to the use of only homogeneous components, i.e., redundant components may compensate for faulty ones (see [5] and [16])”);
Regarding claim 7 Regarding claim 6 Ahn teaches A non-transitory computer-readable medium storing an information processing Program that is executable by a computer to preform processing, the processing comprising: collecting collection data including a route plan planned (Ahn [0020] reads “The processor may control the communication interface to transmit, to the terminal device, cleaning result information that includes only cleaning paths of each of a space in which cleaning is performed from among the plurality of spaces.”);
for a plurality of autonomous mobile robots, (Ahn [0054] reads “In FIG. 1, it is described that one robot cleaner and one terminal device are connected in the cleaning system 300, but in the cleaning system 300, there may be a plurality of robot cleaners which simultaneously operate and each robot cleaner can be connected to a plurality of terminal devices. In addition, one terminal device may be connected to a plurality of robot cleaners.”);
a job operating state indicating assignment of a robot operation to a job, (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
a movement trajectory of each of the robots, (Ahn [0015] reads “The cleaning result information may include a plurality of cleaning paths which are partitioned by a plurality of spaces, and the processor may generate a cleaning area image of the robot cleaner by a plurality of spaces based on the plurality of cleaning paths.”);
and an operating state of each of the robots; (Ahn [0064] reads “The display 120 may display information on an operation state of the robot cleaner 100 (whether the mode is a cleaning mode or an idle mode), information on progress of cleaning (for example, cleaning progress time, current cleaning mode (for example, suction intensity), battery information, whether the battery is charged, whether the dust container is full of dust, and an error state (liquid contact state) and the like. If an error is detected, the display 120 may display the detected error.”);
and including a job required time, (Ahn [0099] reads “Also, the display 220 can display the time required for cleaning and whether an error has occurred when the cleaning result information is displayed. If there is a history of sucking an object or there is a non-cleaned area, this information can be displayed together.”);
Ahn does not teach using the collection data to compute: first evaluation index values second evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot a job utilization ratio, and a robot utilization ratio, and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput at a level of each route in the route plan and including at least an average movement speed for each route in the route plancausing a display to: display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation; and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
LaFergola in analogous art, teaches using the collection data to compute: first evaluation index values second evaluation index values, the first evaluation index values being analyzed from a transport performance perspective of either an entire transport system or each robot (LaFergola [0013] reads “The computer system operates in a plurality of report modes that include an impact report mode presenting reports about impacts of material handling vehicles with objects, a maintenance report mode that presents reports about maintenance and service performed on the material handling vehicles, and a verify report mode presenting data about inspections performed on the material handling vehicles. In addition, a metrics report mode provides reports about amounts of work performed by the material handling vehicles, and an overview report mode presents an overview report that summarizes information from each of the impact, maintenance, verify, and metrics report modes.”);
and the second evaluation index values being analyzed for identifying a cause of a decrease in transport throughput (LaFergola [0074] reads “The vehicle information system 100 can also determine which vehicles in a fleet are operating at better levels of efficiency than other vehicles based on operating parameters, driver skill, and material transportation dynamics. Some types of material handling vehicles work better at horizontal transport, others at putting loads in a rack or taking them down. Some excel at order picking, both at high and low levels. In a given warehouse, the dynamic needs for moving materials may change hourly. The computing system can assign or reassign vehicles to certain tasks and improve throughput as conditions develop.”);
and causing a display to: display the first evaluation index values in a first display mode in which the first evaluation index values are compared using at least one of a predetermined graph or a route simulation; (LaFergola [0014] reads “The computer system transitions from the overview report mode to another report mode that is selected in response to activation of the user input device. Each of the impact, maintenance, verify, and metrics report modes has a summary display mode, a list display mode, and a trend display mode that are individually selectable in response a user activating the input device to select an element presented on the screen. The summary display mode has a format that graphically presents data complied from the plurality of material handling vehicles at the facility, and the list display mode individually lists data about each of at least some of the plurality of material handling vehicles. The trend display mode graphically presents a trend line of data during a defined time period.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn with that of LaFergola to include a method for determining and analyzing the throughput of an entire robotic system. Doing so would constitute the combination of known methods to yield predictable results since both multi-vehicle robotic systems and metrics for analyzing a system would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of large robotic system in which a user could better understand the overall efficiency of the entire system.
Ahn/LaFergola does not teach a job utilization ratio, and a robot utilization ratio, at a level of each route in the route plan and including at least an average movement speed for each route in the route planand display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map.
Akiona in analogous art, teaches a job utilization ratio, and a robot utilization ratio, (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.”);
at a level of each route in the route plan and including at least an average movement speed for each route in the route plan(Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola with that of Akiona to include more metrics related to the efficiency that the robotic system is operating at. This would allow for a user of the facility to get a better understanding on where their system can be improved. (Akiona [0005] reads “A fleet or site simulation tool can leverage discreet event simulation (DES). In such a simulation, each task that occurs is treated as an event with a set duration. When the timer for that event ends, the actor, e.g., robot or human, is free to begin another task. Although such an approach is highly scalable and makes it easy to model individual tasks, it can be overly simplistic for modeling complex, dynamic systems such as mobile robots and people that share a facility with the robots. For example, a DES approach may always indicate that tasks are performed faster with the addition of more robots, ignoring the effects of bottlenecks and gridlock caused by multiple robots and/or other actors occupying the same area. In addition, such an approach does not model coexistence-based interactions such as congestion and suffers from difficulties in capturing interactions and dependencies between multiple actors.”);
Ahn/LaFergola/Akiona does not teach and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map
Merschformann in analogous art, teaches and display the second evaluation index values in a second display mode in which the second evaluation index values are compared using at least one of a predetermined graph map or a heat map(Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona with that of Merschformann to include a method for graphically viewing robot speed through the environment. This would allow the operator to better understand the environment that its robots are operating in. (Merschformann introduction reads “Due to the rise of e-commerce, the traditional manual picker-to-parts warehousing systems no longer work efficiently, and new types of warehousing systems are required, such as automated parts-to-picker systems. For details about the classification of different warehousing systems we refer to [8]. This paper studies one of the parts-to-picker systems, a so-called Robotic Mobile Fulfillment Systems (RMFS), such as the Kiva System ([4], nowadays Amazon Robotics), the GreyOrange Butler or the Swisslog CarryPick. The approach of an RMFS completely eliminates the need for travel within the warehouse, which accounts for approximately 50% of a picker’s time in manual warehouse operations according to [14]. [15] indicates that the Kiva System increases the productivity two to three times, in contrast to a traditional manual picker-to-parts system. Compared to other kinds of warehousing systems, the biggest advantages of an RMFS are its flexibility as a result of having virtually no fixed installations, the scalability due to accessing the inventory in a parallel manner, and the reliability due to the use of only homogeneous components, i.e., redundant components may compensate for faulty ones (see [5] and [16])”);
Regarding claim 9 Ahn/LaFergola/Akiona/Merschformann teaches The information processing method of claim 6, wherein: computing an average movement speed for each route in the route plan as the second evaluation index values; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
and displaying the second display mode as a graph map so as to enable comparison of the average movement speed for each of the routes. (Akiona [0012] reads “The simulation techniques can use a graph that represents the physical area and that includes area nodes that represent the various regions of the area and terminal nodes that represent the location at which tasks are performed. The simulation techniques can also use agents that represent the various actors that perform tasks or otherwise move about the area. The agent for an actor, e.g., robot or person, can include a state machine and model that defines how the actor traverses the area and performs tasks that is used in the simulation to determine/adjust the durations of traversing area nodes and performing tasks at terminal nodes.”);
Regarding claim 10 Ahn/LaFergola/Akiona/Merschformann teaches The information processing method of claim 9, wherein causing the display to display the average movement speed on the routes as a heat map so as to enable comparison. (Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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Regarding claim 13 Ahn/LaFergola/Akiona/Merschformann teaches The non-transitory computer-readable medium of claim 7, wherein: computing an average movement speed for each route in the route plan as the second evaluation index values; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that average speed is a commonly used and appropriate metric in this field.);
And displaying the second display mode as a graph map so as to enable comparison of the average movement speed for each of the routes. (Akiona [0012] reads “The simulation techniques can use a graph that represents the physical area and that includes area nodes that represent the various regions of the area and terminal nodes that represent the location at which tasks are performed. The simulation techniques can also use agents that represent the various actors that perform tasks or otherwise move about the area. The agent for an actor, e.g., robot or person, can include a state machine and model that defines how the actor traverses the area and performs tasks that is used in the simulation to determine/adjust the durations of traversing area nodes and performing tasks at terminal nodes.”);
Regarding claim 14 Ahn/LaFergola/Akiona/Merschformann teaches The non-transitory computer-readable medium of claim 13, wherein causing the display to display the average movement speed on the routes as a heat map so as to enable comparison. (Merschformann figure 5 shows a visualization of a robots movement and other statistics as displayed as a heat map. It would be appreciated by one with ordinary skill in the art that a heat map as an output of a system could be used to output any type of spatially related data, such as speed over a specific area.);
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Claim(s) 2, 5, 8, 11, 12 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Ahn/LaFergola/Akiona/Merschformann, in further view of Kumar (US 20210375080 A1).
Regarding claim 2 Ahn/LaFergola/Akiona/Merschformann teaches The information processing device of claim 1,
Ahn/LaFergola/Akiona/Merschformann does not teach wherein the at least one processor is configured to cause the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan
Kumar in analogous art, teaches wherein the at least one processor is configured to cause the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Regarding claim 5 Ahn/LaFergola/Akiona/Merschformann teaches The information processing device of claim 1, wherein: [[is]]performs job processing with respect to operating time of the robot; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that for each relevant statistic given as an output of the system there would be a corresponding computation associated with it.);
Ahn/LaFergola/Akiona/Merschformann does not teach And the at least one processor is configured to cause the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot.
Kumar in analogous art, teaches And the at least one processor is configured to cause the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot. (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Regarding claim 8 Ahn/LaFergola/Akiona/Merschformann teaches The information processing method of claim 6.
Ahn/LaFergola/Akiona/Merschformann does not teach wherein causing the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan.
Kumar in analogous art, teaches wherein causing the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan. (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Regarding claim 11 Ahn/LaFergola/Akiona/Merschformann teaches The information processing method of claim 6, wherein: the job required time is taken as being a time from a job being assigned to the robot until job completion; the job utilization ratio is taken as being a proportion of time when a job is being executed out of a time from job issue to job completion; and the robot utilization ratio is taken as being a proportion of a total time when the robot performs job processing with respect to operating time of the robot; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that for each relevant statistic given as an output of the system there would be a corresponding computation associated with it.);
Ahn/LaFergola/Akiona/Merschformann does not teach and causing the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot.
Kumar in analogous art, teaches and causing the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot. (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Regarding claim 12 Ahn/LaFergola/Akiona/Merschformann teaches The non-transitory computer-readable medium of claim 7,
Ahn/LaFergola/Akiona/Merschformann does not teach wherein causing the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan.
Kumar in analogous art, teaches wherein causing the display to display such that, according to a selection of display mode input from a user, either: the first evaluation index values are displayed in the first display mode so as to enable comparison for a transport system as a whole or for each robot; or the second evaluation index values are displayed in the second display mode so as to enable comparison with the second evaluation index values being indicated for each route in the route plan. (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Regarding claim 15 Ahn/LaFergola/Akiona/Merschformann teaches The non-transitory computer-readable medium of claim 7, wherein: the job required time is taken as being a time from a job being assigned to the robot until job completion; the job utilization ratio is taken as being a proportion of time when a job is being executed out of a time from job issue to job completion; and the robot utilization ratio is taken as being a proportion of a total time when the robot performs job processing with respect to operating time of the robot; (Akiona [0062] reads “The robot controller 340 can compute various performance metrics based on the recorded data 326. For example, the robot controller 340 can compute the quantity of tasks performed by each agent, the total quantity of tasks performed by all agents during the simulation, the utilization of each agent, the aggregate utilization of all agents, the amount of time each agent spent waiting for conditions to be met for each task or overall among all tasks, the amount of delay caused by congestion at each node, the total amount of delay caused by congestion across all nodes, the average amount of time for each task to be performed, and/or other appropriate performance metrics.” It would be appreciated by one with ordinary skill in the art that for each relevant statistic given as an output of the system there would be a corresponding computation associated with it.);
Ahn/LaFergola/Akiona/Merschformann does not teach And causing the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot.
Kumar in analogous art, teaches And causing the display to visualize a status of an operating state of a transport system by a graph comparing the robot utilization ratio and the job utilization ratio in the transport system as a whole, a graph comparing a number of jobs issued to each robot, and a graph comparing the robot utilization ratio and the job utilization ratio for each robot. (Kumar [0170] reads “By way of example, a feature app in fleet management software (e.g., running on the platform 114—FIG. 1, running as a program on the control module 306 on a materials handling vehicle—FIG. 3, combination thereof, etc.) collects electronic vehicle records (e.g., see 402—FIG. 4). The electronic vehicle records can include feature specific (e.g., travel related) information that is collected from a corresponding materials handling vehicle. Example travel information can include overall travel distance per logged operator out of the total travel remote travel responsive to remote control and manually driven distance. The feature app then uses these two data sets to generate a usage percentage. For instance, when run by a server, the platform 114 can compute a usage percentage for every operator. For instance, an example usage can be established as UsageFeature=dremote/dtotal.” And [0172] reads “The feature app then compares every individual operator's feature usage percentage to the location's feature usage target and also calculates average usage values for groups of operators (e.g. teams or shifts).” It would be appreciated by one with ordinary skill in the art that fleet management software that is used to compare human working in warehouses could easily be adapted to measure the same comparison in a robotic system.);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Ahn/LaFergola/Akiona/Merschformann with that of Kumar to include a method for displaying system information such that a user can easily compare efficiency of different robots and tasks. This would allow for a manager of a facility to have a better understanding of the efficiency of their facility. (Kumar [0003] reads “Materials handling vehicles are commonly used for picking stock in warehouses and distribution centers. Such vehicles typically include a power unit and a load handling assembly, which may include load carrying forks. The vehicle also has control structures for controlling operation and movement of the vehicle. Moreover, wireless strategies are deployed by various enterprises to improve the efficiency and accuracy of operations.”);
Other references not Cited
Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Deng (US 20210116928 A1). Deng in analogous art, could have been relied upon to teach limitations regarding the monitoring and tracking of multiple robotic devices throughout a facility. Similarly, it could also be used to teach the recording of different information points about each of those devices. However, Deng does not teach some aspects that are central to the claimed invention, therefore it was not used in the current rejection of record.
Response to arguments
Applicant argues < Ahn merely monitors robot operation states. Akiona provides performance-related metrics. Kumar relates to comparative display of information. However, none of the references addresses identifying causes of throughput degradation, particularly at a route level. Specifically, none of the cited references discloses or suggests any such structured separation between performance evaluation and causal analysis, i.e., the first and second evaluation index values. The cited references further fail to disclose or suggest any such functional cooperation between: (1) the type of evaluation index values, and (2) the corresponding display mode. Further, while Merschformann may disclose a heat map, it merely relates to visualization of data. Thus, it does not teach defining the second evaluation index values at a route level, or using such values for identifying causes of throughput degradation. Accordingly, Merschformann does not remedy the above deficiencies of Ahn, Akiona, and Kumar. Accordingly, Applicant respectfully requests reconsideration and withdrawal of the above obviousness rejection.> [remarks page 8 paragraph 7-9]. The examiner respectfully disagrees. The current rejection of record relies on teachings of LaFergola to teach the newly claims limitations that relate to analyzing and displaying information about system degradation as a whole. Therefore, the combination teaches the claimed invention.
Conclusion
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/JOHN MARTIN O'MALLEY/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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