DETAILED ACTION
Claims 1, 20-27, 29-45, and 47-55 are pending in this application.
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 .
Specification
The disclosure is objected to because of the following informalities:
Paragraph 001-003 needs to be updated or revised because it includes related application or documents that include information that may have since changed.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claims 1, 20-24, 27 and 38-42 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2021/0284198 A1 to Schmidt et al. in view of U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and further in view of U.S. 2022/0163969 to LI et al.
As to claim 1, Schmidt teaches a resource management system, comprising:
at least one processor in communication with at least one computer storage device (Memory 12) comprising computer program code executable by the at least one processor (Processor 10);
a route network (route and path) stored in the at least one computer storage device (memory) (“… As an example, the supervisor system 200 (and/or WMS) could wirelessly communicate a route, including a path, for the vehicle to navigate for the vehicle to perform a task or series of tasks. The route and path can be relative to a map of the environment stored in memory and, optionally, updated from time-to-time in real-time from vehicle sensor data. As an example, in a warehouse setting, the route could include a plurality of stops along a path for the picking and loading and/or unloading of goods…In example embodiments, a path may also be developed by “training” the vehicle 100. That is, an operator may guide the vehicle 100 through a route while the vehicle, through a machine-learning process, learns and stores the path for use in operation. The path may be stored for future use and may be amended, for example, to include additional stops, to delete stops, or to revise a route to an existing stop, as examples…The memory 12 can also store various types of data and information. Such data and information can include route data, path data, pick data, environmental data, and/or sensor data, as examples…” paragraphs 0086-0089) and
a resource manager module (Supervisor System 200) configured to:
analyze the route (“…As an example, the supervisor system 200 (and/or WMS) could wirelessly communicate a route, including a path, for the vehicle to navigate for the vehicle to perform a task or series of tasks…” paragraph 0086).
Schmidt is silent with reference to the resources including non-shared spaces accessible by an autonomously navigating vehicle,
graph network stored in the at least one computer storage device, the graph network comprising interconnected nodes representing resources in an environment, the resources including shared spaces and non-shared spaces accessible by an autonomously navigating vehicle configured to execute the route; and
a resource manager module configured to:
analyze the route based on the graph network to determine one or more sets of related shared spaces on the route;
apply an ordering algorithm to generate a hierarchy associated with each set of related shared spaces; and
for each set of related shared spaces, generate a set of behaviors to be executed by the vehicle to request access to the set of related shared spaces based on the associated hierarchy.
Sharma teaches the resources including non-shared spaces accessible by an autonomously navigating vehicle (gray colored nodes are disabled (passive)) (“…FIG. 8 illustrates a graphical representation of route plan depicting nodes and edges covering a device area, in accordance with some embodiments of the present disclosure. In one embodiment, the MRRP optimizes global planning of routes for a plurality of autonomous vehicles working in an operating environment. The MRRP plans routes based on a discrete representation of a map in the form of a graph with connected nodes and edges. The graph may be generated manually or auto-generated using the BLA/UI as described in FIG. 1. For example, as shown in 800 of FIG. 8, an autonomous vehicle like a forklift requests a hard lock to a passage in a device like mobile rack unit 2 801. The MRRP generates a route plan as a response to the mobile rack unit 2 and LBC running on the forklift requests the system for a hard lock on the mobile rack unit 2 801. At the same time, there is a possibility of another forklift waiting at the entry point of the passage between rack 3 and rack 2 and waiting for a hard lock on rack 2. Since, both the forklifts are waiting for the hard locks, the first forklift may not be able to navigate as the second forklift may be blocking the path and the second forklift may be blocking the path as the forklift is not able to get the hard lock. Embodiments of the present disclosure provides technical solutions to resolve such deadlock scenarios or inefficient waiting time by the forklifts that include the system invoking a passive request to the MRRP module. In such a scenario, in response to the passive request, the MRRP module may instruct the forklift to move to any other location to give way to one of the forklifts. The LBC running on the robots, like forklift may decide whether a passive request is to be initiated or a path to navigate to a destination may be requested. As shown in FIG. 8, the gray colored nodes are disabled (passive) by invoking a passive request…Therefore, if a node is not enabled for a forklift, then MRRP initiates a passive path for the forklift…” paragraphs 0038/0039).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt with the teaching of Sharma because the teaching of Sharma would improve the system of Schmidt by providing a technique for optimizing global planning of routes for a plurality of autonomous vehicles and controlling access to route/path of the plurality of autonomous vehicles.
LI teaches graph network stored in the at least one computer storage device, the graph network comprising interconnected nodes (nodes) (representing resources in an environment (Warehouse management system (WMS)/Warehouse Control System (WCS) 130), the resources including shared spaces (nodes) accessible by an autonomously navigating vehicle configured to execute the route (Path Coordinator 204); and
a resource manager module configured to:
analyze the route based on the graph network to determine one or more sets of related shared spaces on the route (The system analyzes the tree and determines no existing planned routes at this stage, so the optimal route is a straight line from the root node to node A);
apply an ordering algorithm (MPTS module 213) to generate a hierarchy associated with each set of related shared spaces (“…In one embodiment, the MPTS module 213 primarily determines the ordering of the path or route plans for the fleet of robots. If there are known trajectories of other robots, then the MPTS module 213 performs an optimization step of determining, at the basic level, which robot may yield to other robots (s) and which robot may have a higher priority value. MPTS module 213 may resolve this by identifying the robot that may be moved first and also, maintain statistics on total travel time, the total number of collisions encountered, etc. The MPTS module 213 utilizes the path coordinator 204 as a storage structure for storing the aforementioned information. In addition, the output of path search module 214 gets appended back to the path coordinator's 204 tree-like hierarchical databases. The system verifies if the ordering of the plan is optimal or not by traversing the tree from the root node to the leaf node, and a single traversal may include a set of paths that gives an estimation of the count of collisions, path time, cost function, etc. The output of the parallel process of Multi-path search module 213 and Path search module 214 is a set of path trajectories 205. The output is like a location and time sequence for every single request, represented in detail herewith. The output, which is a set of trajectories may include additional information like, if the collision is bound to happen, the specific robots that may be involved in a collision, which robot is colliding with the other robot, along with the time when the collision may happen, the route where the collision may occur, expected travel time, other statistical information, etc. For every robot, there is information related to the node and the time sequence. The system may also include some tasks that may be passively converted which is also part of the output information, for example, some robots may not be able to navigate to a particular node where their route plan indicates, but, in order to avoid collisions, the system may force the robots to go somewhere else. Most of the computation happens in the combinatorial tree search of MPTS module 213 and path search module 214, and the problem space expands exponentially with the number of robots…” paragraph 0046); and
for each set of related shared spaces, generate a set of behaviors to be executed by the vehicle to request access to the set of related shared spaces based on the associated hierarchy (Set of Path Trajectories 205) (“…In one embodiment, the MPTS module 213 primarily determines the ordering of the path or route plans for the fleet of robots. If there are known trajectories of other robots, then the MPTS module 213 performs an optimization step of determining, at the basic level, which robot may yield to other robots (s) and which robot may have a higher priority value. MPTS module 213 may resolve this by identifying the robot that may be moved first and also, maintain statistics on total travel time, the total number of collisions encountered, etc. The MPTS module 213 utilizes the path coordinator 204 as a storage structure for storing the aforementioned information. In addition, the output of path search module 214 gets appended back to the path coordinator's 204 tree-like hierarchical databases. The system verifies if the ordering of the plan is optimal or not by traversing the tree from the root node to the leaf node, and a single traversal may include a set of paths that gives an estimation of the count of collisions, path time, cost function, etc. The output of the parallel process of Multi-path search module 213 and Path search module 214 is a set of path trajectories 205. The output is like a location and time sequence for every single request, represented in detail herewith. The output, which is a set of trajectories may include additional information like, if the collision is bound to happen, the specific robots that may be involved in a collision, which robot is colliding with the other robot, along with the time when the collision may happen, the route where the collision may occur, expected travel time, other statistical information, etc. For every robot, there is information related to the node and the time sequence. The system may also include some tasks that may be passively converted which is also part of the output information, for example, some robots may not be able to navigate to a particular node where their route plan indicates, but, in order to avoid collisions, the system may force the robots to go somewhere else. Most of the computation happens in the combinatorial tree search of MPTS module 213 and path search module 214, and the problem space expands exponentially with the number of robots…” paragraph 0046).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt and Sharma with the teaching of LI because the teaching of LI would improve the system of Schmidt and Sharma by providing a technique to optimize route plans for handling critical scenarios faced in a warehouse environment.
As to claim 20, see the rejection of claim 1 above.
As to claim 21, Schmidt teaches the method of claim 20, wherein the vehicle is an autonomous mobile robot (AMR) (“…Various types of vehicles exist that can navigate without direct reliance on a human driver, such as automated mobile robots (AMRs), autonomous or automated guided vehicle (AGV), vision-guided vehicles (VGV), and autonomous guided carts (AGCs), as examples. For purposes of brevity, such vehicles will be collectively referred to as autonomous vehicles AVs. AV forms of pallet trucks and powered tuggers exist. They are most often used in industrial applications to move materials and/or goods around a manufacturing facility or a warehouse, such as in the case of AV forklifts and AV tuggers…” paragraph 0011).
As to claim 22, Schmidt teaches the method of claim 21, wherein the AMR is an autonomously navigating pallet truck or tugger (AV forms of pallet trucks and powered tuggers exist/ AV tuggers) (“…Various types of vehicles exist that can navigate without direct reliance on a human driver, such as automated mobile robots (AMRs), autonomous or automated guided vehicle (AGV), vision-guided vehicles (VGV), and autonomous guided carts (AGCs), as examples. For purposes of brevity, such vehicles will be collectively referred to as autonomous vehicles AVs. AV forms of pallet trucks and powered tuggers exist. They are most often used in industrial applications to move materials and/or goods around a manufacturing facility or a warehouse, such as in the case of AV forklifts and AV tuggers…” paragraph 0011).
As to claim 23, LI teaches the method of claim 20, further comprising the resource manager generating the hierarchy associated with each set of related shared spaces prior to the vehicle navigating the route (“…In one embodiment, the MPTS module 213 primarily determines the ordering of the path or route plans for the fleet of robots. If there are known trajectories of other robots, then the MPTS module 213 performs an optimization step of determining, at the basic level, which robot may yield to other robots (s) and which robot may have a higher priority value. MPTS module 213 may resolve this by identifying the robot that may be moved first and also, maintain statistics on total travel time, the total number of collisions encountered, etc. The MPTS module 213 utilizes the path coordinator 204 as a storage structure for storing the aforementioned information. In addition, the output of path search module 214 gets appended back to the path coordinator's 204 tree-like hierarchical databases. The system verifies if the ordering of the plan is optimal or not by traversing the tree from the root node to the leaf node, and a single traversal may include a set of paths that gives an estimation of the count of collisions, path time, cost function, etc. The output of the parallel process of Multi-path search module 213 and Path search module 214 is a set of path trajectories 205. The output is like a location and time sequence for every single request, represented in detail herewith. The output, which is a set of trajectories may include additional information like, if the collision is bound to happen, the specific robots that may be involved in a collision, which robot is colliding with the other robot, along with the time when the collision may happen, the route where the collision may occur, expected travel time, other statistical information, etc. For every robot, there is information related to the node and the time sequence. The system may also include some tasks that may be passively converted which is also part of the output information, for example, some robots may not be able to navigate to a particular node where their route plan indicates, but, in order to avoid collisions, the system may force the robots to go somewhere else. Most of the computation happens in the combinatorial tree search of MPTS module 213 and path search module 214, and the problem space expands exponentially with the number of robots…” paragraph 0046).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt and Sharma with the teaching of LI because the teaching of LI would improve the system of Schmidt and Sharma by providing a technique to optimize route plans for handling critical scenarios faced in a warehouse environment.
As to claim 24, LI teaches the method of claim 20, wherein the shared spaces include physical spaces (figures 3(A)-3(E)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt and Sharma with the teaching of LI because the teaching of LI would improve the system of Schmidt and Sharma by providing a technique to optimize route plans for handling critical scenarios faced in a warehouse environment.
Sharma teaches the non-shared spaces include physical spaces (gray colored nodes are disabled (passive)) (“…FIG. 8 illustrates a graphical representation of route plan depicting nodes and edges covering a device area, in accordance with some embodiments of the present disclosure. In one embodiment, the MRRP optimizes global planning of routes for a plurality of autonomous vehicles working in an operating environment. The MRRP plans routes based on a discrete representation of a map in the form of a graph with connected nodes and edges. The graph may be generated manually or auto-generated using the BLA/UI as described in FIG. 1. For example, as shown in 800 of FIG. 8, an autonomous vehicle like a forklift requests a hard lock to a passage in a device like mobile rack unit 2 801. The MRRP generates a route plan as a response to the mobile rack unit 2 and LBC running on the forklift requests the system for a hard lock on the mobile rack unit 2 801. At the same time, there is a possibility of another forklift waiting at the entry point of the passage between rack 3 and rack 2 and waiting for a hard lock on rack 2. Since, both the forklifts are waiting for the hard locks, the first forklift may not be able to navigate as the second forklift may be blocking the path and the second forklift may be blocking the path as the forklift is not able to get the hard lock. Embodiments of the present disclosure provides technical solutions to resolve such deadlock scenarios or inefficient waiting time by the forklifts that include the system invoking a passive request to the MRRP module. In such a scenario, in response to the passive request, the MRRP module may instruct the forklift to move to any other location to give way to one of the forklifts. The LBC running on the robots, like forklift may decide whether a passive request is to be initiated or a path to navigate to a destination may be requested. As shown in FIG. 8, the gray colored nodes are disabled (passive) by invoking a passive request…Therefore, if a node is not enabled for a forklift, then MRRP initiates a passive path for the forklift…” paragraphs 0038/0039).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt and LI with the teaching of Sharma because the teaching of Sharma would improve the system of Schmidt and LI by providing a technique for optimizing global planning of routes for a plurality of autonomous vehicles and controlling access to route/path of the plurality of autonomous vehicles.
As to claim 27, Li teaches the method of claim 20, wherein the ordering algorithm is a global ordering algorithm (MPTS Module 213) applied by a plurality of autonomously navigating vehicles within the environment (“…In one embodiment, the MPTS module 213 primarily determines the ordering of the path or route plans for the fleet of robots. If there are known trajectories of other robots, then the MPTS module 213 performs an optimization step of determining, at the basic level, which robot may yield to other robots (s) and which robot may have a higher priority value. MPTS module 213 may resolve this by identifying the robot that may be moved first and also, maintain statistics on total travel time, the total number of collisions encountered, etc. The MPTS module 213 utilizes the path coordinator 204 as a storage structure for storing the aforementioned information. In addition, the output of path search module 214 gets appended back to the path coordinator's 204 tree-like hierarchical databases. The system verifies if the ordering of the plan is optimal or not by traversing the tree from the root node to the leaf node, and a single traversal may include a set of paths that gives an estimation of the count of collisions, path time, cost function, etc. The output of the parallel process of Multi-path search module 213 and Path search module 214 is a set of path trajectories 205. The output is like a location and time sequence for every single request, represented in detail herewith. The output, which is a set of trajectories may include additional information like, if the collision is bound to happen, the specific robots that may be involved in a collision, which robot is colliding with the other robot, along with the time when the collision may happen, the route where the collision may occur, expected travel time, other statistical information, etc. For every robot, there is information related to the node and the time sequence. The system may also include some tasks that may be passively converted which is also part of the output information, for example, some robots may not be able to navigate to a particular node where their route plan indicates, but, in order to avoid collisions, the system may force the robots to go somewhere else. Most of the computation happens in the combinatorial tree search of MPTS module 213 and path search module 214, and the problem space expands exponentially with the number of robots…” paragraph 0046).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt and Sharma with the teaching of LI because the teaching of LI would improve the system of Schmidt and Sharma by providing a technique to optimize route plans for handling critical scenarios faced in a warehouse environment.
As to claim 38, see the rejection of claim 1 above, expect for a navigation system operatively coupled to a drive system, and
a communication system configured to wirelessly communicate with an external supervisor system.
Schmidt teaches a navigation system (Navigation 110) operatively coupled to a drive system (Drive Controller 120), and
a communication system configured to wirelessly communicate with an external supervisor system (Supervisor System 200).
As to clam 39, see the rejection of claim 21 above.
As to claim 40, see the rejection of claim 22 above.
As to clam 41, see the rejection of claim 23 above.
As to claim 42, see the rejection of claim 24 above.
Claims 25, 26, 43 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2021/0284198 A1 to Schmidt et al. in view of U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and further in view of U.S. 2022/0163969 to LI et al. as applied to claims 20 and 38 above, and further in view of U.S. Pub. No. 2023/0173675 A1 to Hong et al.
As to claim 25, Schmidt as modified by Sharma and LI teaches the method of claim 20, wherein the resource manager module is on-board the vehicle.
Hong teaches wherein the resource manager module is on-board the vehicle (distributed controllers onboard the AMR vehicle 105) (“…Although described in the singular form, it should be appreciated that the robotic vehicle computer 145 comprises a plurality of controllers operating as an integrated and distributed control system for the AMR vehicle 105. For example, the AMR vehicle 105 may include one or more package delivery controllers 196, an autonomous vehicle controller (AVC) 194, among other computing systems. One benefit of the AMR vehicle 105 includes an interchangeable top and bottom module, where each respective module of the AMR vehicle 105 chassis is exchangeable, making unique configurations of vehicle hardware possible. The distributed control system refers to the AMR vehicle control system sharing computing and control tasks between the top module, which generally controls environmental operational control and payload loading and unloading responsibilities, and the bottom module, which manages navigation, drive, and other operational tasks. Accordingly, the distributed controllers onboard the AMR vehicle 105 are referred to herein individually and as a system (the robotic vehicle computer 145)…The AMR vehicle system 107 may be configured and/or programmed as a framework for robot operations for package delivery. The sensors and computation onboard the AMR vehicle 105 processes environmental data and navigates on sidewalks and other paths. The AMR vehicle system 107 may operate independently using the AVC 194 and may receive control signals from another offboard computing system (not shown in FIG. 1) via the TCU 160 when connected remotely with a remote terminal via the network(s) 125…” paragraphs 0028/0055).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt, Sharma and LI with the teaching of Hong because the teaching of Hong would improve the system of Schmidt, Sharma and LI by providing an onboard software for allowing devices (in this case, autonomous vehicles) to operate independent of remote management server or service.
As to claim 26, Schmidt as modified by Sharma and LI teaches the method of claim 20, wherein at least a portion of the resource manager module is offboard the vehicle.
Hong teaches wherein at least a portion of the resource manager module is offboard the vehicle (offboard computing system) (“…The AMR vehicle system 107 may be configured and/or programmed as a framework for robot operations for package delivery. The sensors and computation onboard the AMR vehicle 105 processes environmental data and navigates on sidewalks and other paths. The AMR vehicle system 107 may operate independently using the AVC 194 and may receive control signals from another offboard computing system (not shown in FIG. 1) via the TCU 160 when connected remotely with a remote terminal via the network(s) 125…” paragraphs 0028/0055).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt, Sharma and LI with the teaching of Hong because the teaching of Hong would improve the system of Schmidt, Sharma and LI by providing an offboard software for allowing devices (in this case, autonomous vehicles) to operate with the help of remote management server or service.
As to claim 43, see the rejection of claim 25 above.
As to claim 44, see the rejection of claim 26 above.
Claims 29 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2021/0284198 A1 to Schmidt et al. in view of U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and further in view of U.S. 2022/0163969 to LI et al. as applied to claims 20 and 38 above, and further in view of U.S. Pub. No. 2018/0260780 A1 to Mazetti et al.
As to claim 29, Schmidt as modified by Sharma and LI teaches the method of claim 20, however it is silent with reference to the resource manager module automatically adding the markers to the route, the markers indicating locations of the sets of related shared spaces on the route.
Mazetti teaches the resource manager module automatically adding the markers to the route, the markers indicating locations of the sets of related shared spaces on the route (“…According to some aspects, the method comprises assigning the first and second transports with transport location markers which are automatically updated with current position information received from the transports. Thus, the back end system plans routes and locations to converge with other transports based on real time locations of the transports…According to some aspects, determining a route is based at least on the transport location marker of the respective transport. In other words, the routing may be done continuously by using actual positions…” paragraphs 0009/0010).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt, Sharma and LI with the teaching of Mazetti because the teaching of Mazetti would improve the system of Schmidt, Sharma and LI by providing a transport location markers for marking the route of travel of autonomous vehicles.
As to claim 47, see the rejection of claim 29 above.
Claims 30 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2021/0284198 A1 to Schmidt et al. in view of U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and further in view of U.S. 2022/0163969 to LI et al. as applied to claims 20 and 38 above, and further in view of U.S. Pub. No. 2019/0080612 A1 to Weissman et al.
As to claim 30, Schmidt as modified by Sharma and LI teaches method of claim 20, further comprising the resource manager module adding the markers to the route in response to user inputs via a user interface, the markers indicating locations of the sets of related shared spaces on the route.
Weissman teaches the resource manager module adding the markers to the route in response to user inputs via a user interface, the markers indicating locations of the sets of related shared spaces on the route (“…Furthermore, the navigation server can be configured to alert an administrator before making an automated changes to the marker location information and/or the map information. The administrator can review the proposed changes to the marker location information and/or the map information, and can approve or deny the changes. The navigation server can also be configured to generate a repair request to dispatch someone to repair or replace missing, malfunctioning, or damaged markers responsive to receiving more than a threshold number of error reports regarding a particular marker...” paragraph 0042).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt, Sharma and LI with the teaching of Weissman because the teaching of Weissman would improve the system of Schmidt, Sharma and LI by allowing a user or administrator to repair or fix route markers.
As to claim 48, see the rejection of claim 30 above.
Claims 31 and 49 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2021/0284198 A1 to Schmidt et al. in view of U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and further in view of U.S. 2022/0163969 to LI et al. as applied to claims 20 and 38 above, and further in view of W.O. No. 2019220235 A1 to Johnson et al.
As to claim 31, Schmidt as modified by Sharma and LI teaches the method of claim 28, further comprising the resource manager module using each marker as a queue to instruct the vehicle to execute a set of behaviors associated with a set of related shared spaces associated with the marker.
Johnson teaches the resource manager module (Vehicle Control Component 144) using each marker (lane marker) as a queue to instruct the vehicle to execute a set of behaviors (first/second operations) associated with a set of related shared spaces (temporary zone) associated with the marker (“…In some examples, such as examples in which vehicle control component 144 is responsible for directly controlling navigation of PAAV 110, vehicle control component 144 may be configured to modify, based on the indication of the temporary zone, the mode of autonomous operation of PAAV 110 while operating within the temporary zone on the vehicle pathway. For example, PAAV 110 may detect a navigational stimulus from a sensor, such as a lane marker from one of image capture devices 102. Based on characteristics of the lane marker, such as a position of the lane marker with respect to PAAV 110, PAAV 110 may perform a first operation, such as notifying a driver that the lane marker is near, in a first mode of autonomous operation and perform a second operation, such as avoiding the lane marker, in a second mode of operation. As such, a change in a mode of autonomous operation may include changing a response of PAAV 110 to the navigational stimulus, such as through different operating rules or different levels of autonomous operation …” paragraph 0066).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the system of Schmidt, Sharma and LI with the teaching of Johnson because the teaching of Johnson would improve the system of Schmidt, Sharma and LI by providing a technique of using route markers to determine the sequence of operations or route of travel by the autonomous vehicles.
As to claim 49, see the rejection of claim 31 above.
Allowable Subject Matter
Claims 32-37 and 50-55 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Reasons for allowance
The following is an examiner’s statement of reasons for allowance:
The closest prior art of records, (U.S. Pub. No. 2021/0284198 A1 to Schmidt et al., U.S. Pub. No. 2022/0234872 A1 to Sharma et al. and U.S. 2022/0163969 to LI et al.), taken alone or in combination do not specifically disclose or suggest the claimed recitations (claims 32-37 and 50-55), when taken in the context of claims as a whole.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. .Pub. No. 2019/0049975 A1 to Kattepur et al. and directed to autonomous devices, and system to optimally allocate warehouse procurement tasks to distributed autonomous devices.
U.S. Pub. No. 2018/0299882 A1 to Kicihkaylo and directed to robot routing method involves determining first route through environment along roadmap for use by first robot and the second route through the environment along the roadmap for use by second robot.
W.O. No. 2022037202 A1 to AI et al. and directed to a method and a system for assisting a robot (120) to navigate in a warehouse environment.
U.S. Pub. No. 2023/0063370 A1 LI and directed to a method and system for multi-robot route planning.
U.S. Pub. No. 2018/0307941 A1 to Holz et al. and directed to methods and systems for simultaneous localization and calibration including a sensor on a vehicle may be calibrated (e.g., by determining a position and orientation of the sensor on the vehicle) as part of a graph-based localization and mapping system
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/CHARLES E ANYA/Primary Examiner, Art Unit 2194
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