DETAILED ACTION
Amendment received 7 May 2026 is acknowledged. Claims 1-20 amended 7 May 2026 are pending and have been considered as follows.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US Pub. No. 2021/0229292) in view of Russell (US Patent No. 10,108,194), further in view of Aiso (US Pub. No. 2015/0105907).
As per Claim 1, Liu discloses a method of grasping an object (as per “items” in ¶31) by a suction-based gripper (as per “Picking element 110 comprises a set of suction-based picking mechanisms” in ¶31) of a robot (105), the method comprising:
receiving (as per arrow from 505 to 510 in Fig. 5), by a computing device (905), from a perception system (130/135), perception information (505) reflecting an object (as per “items” in ¶31) to be grasped by the suction-based gripper (110) (Figs. 1, 5, 9; ¶31-38, 47-50, 63-66);
determining, by the computing device (905), uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) reflecting an unknown or uncertain extent (as per “modeling uncertainty in various dimensions” in ¶39) and/or pose (as per “There are many possible variations regarding what could be true for the uncertain parts of an object. Each box may be different in length, width, height, position, pose, angle and similar properties” in ¶44) of the object (as per “items” in ¶31) (Figs. 1-3, 9; ¶31-45, 63-66);
determining, by the computing device (905), a grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) to grasp the object (as per “items” in ¶31) (Figs. 1-5, 9; ¶31-50, 63-66); and
controlling, by the computing device (905), the robot (105) to grasp the object (as per “items” in ¶31) using the grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) (Figs. 1, 5, 9; ¶31-38, 47-50, 63-66).
Liu does not expressly disclose:
wherein the robot is a mobile robot;
wherein the perception system is of the robot;
determining, by the computing device, a pose of the suction-based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information; and
wherein the grasp strategy is based, at least in part, on the pose of the suction-based gripper relative to the object.
Russell discloses a robotic truck unloader (300) that includes a robotic arm (302) with a suction grid gripper (304) for gripping objects within the environment (Fig. 3A; 15:9-16:7). The truck unloader (300) also includes a moveable cart (312) with wheels (314) for locomotion (Fig. 3A; 15:30-42). A sensing system of the truck unloader (300) also includes sensors (306, 308) that are two-dimensional and/or 3D depth sensors that sense information about the environment as the robotic arm (302) moves (Fig. 3A; 15:43-50). In this way, the unloader (300) is adapted to determine pick positions for objects and navigate the mobile base into a position for unloading or loading within a warehouse (15:9-64). Like Liu, Russell is concerned with robot control systems.
Aiso discloses a robot system (1) in which a robot (20) operates a hand (26) to grasp a work (W1) (Fig. 1; ¶54-57). The hand (26) is provided on the distal end of an arm (22) of the robot (20), the arm (22) including one or more joints (23) and links (24) (Fig. 1; ¶57). The robot (20) is governed by a robot controller (10) that includes a position control unit (210) that: determines a trajectory of the end point corresponding to amounts of movement and directions of movement of the end point in time series; determines the next movement position of target joint angles of respective actuators provided in the joints (23) based on the determined amount of movement and direction of movement; and generates a movement command value for moving the end point by the target angles and outputs the value (Fig. 11; ¶15-117, 127). In this way, the object is moved efficiently to target position (¶7). Like Liu, Aiso is concerned with robot control systems.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates:
“wherein the robot is a mobile robot” in that the robot arm (105) of Liu would be adapted for navigation within a warehouse as per Russell;
“wherein the perception system is of the robot” in that the robot arm (105) of Liu would be adapted for navigation within a warehouse as per Russell;
by “determining, by the computing device, a pose of the suction-based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information” in that the robot arm (105) of Liu would operate in view of data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso;
“wherein the grasp strategy is based, at least in part, on the pose of the suction-based gripper relative to the object” in that decisions related to future actions as per ¶24 would be informed by data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso.
As per Claim 2, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 1. Liu further discloses wherein
receiving perception information (505) reflecting an object (as per “items” in ¶31) to be grasped comprises receiving (as per arrow from 505 to 510 in Fig. 5) information (505) on a first extent (as per first dimension of a side of bounding box) and a second extent (as per second dimension of a side of bounding box) of a first face (as per dimensions of each side of bounding box) of the object (as per “items” in ¶31) (Figs. 5, 6A-8; ¶47-60), and
determining uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) comprises determining uncertainty information (as per “size of bounding boxes” corresponding to uncertainty in specified dimension in ¶60) for a third extent (as per third dimension of a side of bounding box) of a second face (as per dimensions of each side of bounding box) of the object (as per “items” in ¶31) (Figs. 5, 6A-8; ¶47-60).
As per Claim 3, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 2. Liu further discloses wherein the second face (as per dimensions of each side of bounding box) shares (as per adjacent faces of bounding box) one of the first extent (as per first dimension of a side of bounding box) or the second extent (as per second dimension of a side of bounding box) with the first face (as per dimensions of each side of bounding box) (Figs. 5, 6A-8; ¶47-60).
As per Claim 13, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 1. Liu further discloses wherein the system operates by:
determining a pick trajectory (as per “guide the robotic arm 105 to the item” in ¶34) of a manipulator (105) including the suction-based gripper (as per “Picking element 110 comprises a set of suction-based picking mechanisms” in ¶31) based, at least in part, on the uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3); and
determining the grasp strategy (as per “items” in ¶31) based, at least in part, on the pick trajectory (as per “guide the robotic arm 105 to the item” in ¶34).
Liu does not expressly disclose wherein the system operates by determining a pose of the suction- based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates by: “determining a pose of the suction-based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information” in that the robot arm (105) of Liu would operate in view of data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso.
As per Claim 14, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 13. Liu further discloses wherein determining a pick trajectory (as per “guide the robotic arm 105 to the item” in ¶34) of the manipulator (105) comprises:
determining a terminal end-effector pose (as per “how to pick up the item” in ¶30; as per “how to pick it up” in ¶33) of the pick trajectory (as per “guide the robotic arm 105 to the item” in ¶34) based, at least in part, on the uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3).
As per Claim 15, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 14. Liu does not expressly disclose wherein determining a pick trajectory of the manipulator further comprises:
determining an intermediate end-effector pose of the pick trajectory; and
determining the pick trajectory by constraining the pick trajectory to follow a target twist with a constant angular component from the intermediate end-effector pose to the terminal end- effector pose.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates wherein determining a pick trajectory of the manipulator further comprises:
“determining an intermediate end-effector pose of the pick trajectory” in that the robot arm (105) of Liu would operate in view of trajectory data in time series as per Aiso; and
“determining the pick trajectory by constraining the pick trajectory to follow a target twist with a constant angular component from the intermediate end-effector pose to the terminal end-effector pose” in that the robot arm (105) of Liu would operate in view of trajectory data in time series and with specified target joint angles as per Aiso.
As per Claim 16, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 15. Liu does not expressly disclose wherein the intermediate end-effector pose is determined, at least in part, on one or more of the terminal end-effector pose, a reach of the manipulator or a height of a distance sensor on a base of the mobile robot.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates “wherein the intermediate end-effector pose is determined, at least in part, on one or more of the terminal end-effector pose, {a reach of the manipulator or a height of a distance sensor on a base of the mobile robot}” in that the robot arm (105) of Liu would operate in view of trajectory data in time series and with specified target joint angles as per Aiso.
As per Claim 17, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 15. Liu does not expressly disclose wherein controlling the mobile robot to grasp the object based, at least in part, on the grasp strategy comprises:
detecting, as the manipulator is advanced along the pick trajectory, that a force associated with the manipulator exceeds a threshold value; and
stopping advancing of the manipulator in response to determining that the force exceeds the threshold value.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso. Aiso further discloses impedance control in which an impedance control unit (220) determines to end impedance control (as per S311) in response to determination that the value of force from a force sensor (25) exceeds a designated value (Fig. 18; ¶187-197).
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system wherein controlling the mobile robot to grasp the object based, at least in part, on the grasp strategy comprises:
“detecting, as the manipulator is advanced along the pick trajectory, that a force associated with the manipulator exceeds a threshold value” in that the robot arm (105) of Liu would operate in view of trajectory data in time series and impedance control as per Aiso; and
“stopping advancing of the manipulator in response to determining that the force exceeds the threshold value” in that the robot arm (105) of Liu would operate in view of trajectory data in time series and impedance control as per Aiso.
As per Claim 19, Liu discloses a robot (105) (Fig. 1; ¶31), comprising:
a suction-based gripper (as per “Picking element 110 comprises a set of suction-based picking mechanisms” in ¶31) (Fig. 1; ¶31, 33);
a perception system (130/135) (Fig. 1; ¶31); and
at least one computing device (905) programmed with a plurality of instructions (as per “program instructions” in ¶66) that, when executed, perform a method comprising:
receiving (as per arrow from 505 to 510 in Fig. 5) from the perception system (130/135), perception information (505) reflecting an object (as per “items” in ¶31) to be grasped by the suction-based gripper (110) (Figs. 1, 5, 9; ¶31-38, 47-50, 63-66);
determining uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) reflecting an unknown or uncertain extent (as per “modeling uncertainty in various dimensions” in ¶39) and/or pose (as per “There are many possible variations regarding what could be true for the uncertain parts of an object. Each box may be different in length, width, height, position, pose, angle and similar properties” in ¶44) of the object (as per “items” in ¶31) (Figs. 1-3, 9; ¶31-45, 63-66);
determining a grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) to grasp the object (as per “items” in ¶31) (Figs. 1-5, 9; ¶31-50, 63-66); and
controlling the robot (105) to grasp the object (as per “items” in ¶31) using the grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) (Figs. 1, 5, 9; ¶31-38, 47-50, 63-66).
Liu does not expressly disclose:
wherein the robot is a mobile robot;
determining a pose of the suction-based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information; and
wherein the grasp strategy is based, at least in part, on the pose of the suction-based gripper relative to the object.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates:
“wherein the robot is a mobile robot” in that the robot arm (105) of Liu would be adapted for navigation within a warehouse as per Russell;
by “determining a pose of the suction-based gripper relative to the object prior to grasping the object based, at least in part, on the uncertainty information” in that the robot arm (105) of Liu would operate in view of data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso;
“wherein the grasp strategy is based, at least in part, on the pose of the suction-based gripper relative to the object” in that decisions related to future actions as per ¶24 would be informed by data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso.
As per Claim 20, Liu discloses a controller (905) for a robot (105) (Figs. 1, 9; ¶31, 63-68), the controller (905) comprising:
at least one computing device (905) programmed with a plurality of instructions (as per “program instructions” in ¶66) that, when executed, perform a method comprising:
receiving (as per arrow from 505 to 510 in Fig. 5) from a perception system (130/135), perception information (505) reflecting an object (as per “items” in ¶31) to be grasped (via picking element 110) by the robot (105) (Figs. 1, 5; ¶31-38, 47-50);
determining uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) reflecting an unknown or uncertain extent (as per “modeling uncertainty in various dimensions” in ¶39) and/or pose (as per “There are many possible variations regarding what could be true for the uncertain parts of an object. Each box may be different in length, width, height, position, pose, angle and similar properties” in ¶44) of the object (as per “items” in ¶31) (Figs. 1-3; ¶31-45); and
determining a grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) to grasp the object (as per “items” in ¶31) (Figs. 1-5; ¶31-50); and
controlling the robot (105) to grasp the object (as per “items” in ¶31) using the grasp strategy (as per “The system can then make decisions related to future actions performed by the robotic device … and operate the robotic device accordingly” in ¶24; as per 405-420 in Fig. 4; as per “DECISION” in Fig. 5) (Figs. 1, 5; ¶31-38, 47-50).
Liu does not expressly disclose:
wherein the robot is a mobile robot;
wherein the perception system is of the robot;
determining a pose of a suction-based gripper of the robot relative to the object prior to grasping the object based, at least in part, on the uncertainty information; and
wherein the grasp strategy is determined based, at least in part, on the pose of the suction-based gripper relative to the object.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Therefore, from these teachings of Liu, Russell, and Aiso, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell and Aiso to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; and efficiently moving objects to a target position. Applying the teachings of Russell and Aiso to the system of Liu would result in a system that operates:
“wherein the robot is a mobile robot” in that the robot arm (105) of Liu would be adapted for navigation within a warehouse as per Russell;
“wherein the perception system is of the robot” in that the robot arm (105) of Liu would be adapted for navigation within a warehouse as per Russell;
by “determining a pose of a suction-based gripper of the robot relative to the object prior to grasping the object based, at least in part, on the uncertainty information” in that the robot arm (105) of Liu would operate in view of data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso; and
“wherein the grasp strategy is determined based, at least in part, on the pose of the suction-based gripper relative to the object” in that decisions related to future actions as per ¶24 would be informed by data describing objects (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) and trajectory data in time series and with specified target joint angles as per Aiso.
Claims 4-12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US Pub. No. 2021/0229292) in view of Russell (US Patent No. 10,108,194), further in view of Aiso (US Pub. No. 2015/0105907), further in view of Saunders (US Pub. No. 2021/0178579).
As per Claim 4, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 2. Liu does not expressly disclose wherein the pose of the suction-based gripper relative to the object includes an orientation of the suction-based gripper relative to a face of the object having an uncertain extent, and determining a grasp strategy comprises:
assigning a classification to each of a plurality of suction cups of the suction-based gripper based, at least in part, on the uncertainty information and the orientation of the suction-based gripper relative to the face of the object,
wherein controlling the mobile robot to grasp the object comprises controlling the mobile robot to grasp the object based, at least in part, on the classification assigned to each of the plurality of suction cups of the suction-based gripper.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
Saunders discloses a robot (100) that includes a vacuum based gripper (160) for moving boxes (20a) (Fig. 1A; ¶63). The gripper (160/200) includes a plurality of vacuum assemblies (300) that are individually addressable and a controller is configured to control operation of the vacuum assemblies (300) based on a relevant parameter (Fig. 2A; ¶86-87, 90). In operation, the gripper (160/200/700) contacts a box (20a/730) and individual cup assemblies (300/702) are appropriately deactivated (710) and activated (720) (Fig. 7A; ¶112). Information from cameras is used to determine whether or not individual cup assemblies should be activated or deactivated (¶114). In this way, grasping capabilities are improved (¶62). Like Liu, Saunders is concerned with robot control systems.
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein the pose of the suction-based gripper relative to the object includes an orientation of the suction-based gripper relative to a face of the object having an uncertain extent, and determining a grasp strategy comprises:
“assigning a classification to each of a plurality of suction cups of the suction-based gripper based, at least in part, on the uncertainty information and the orientation of the suction-based gripper relative to the face of the object” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders; and
“wherein controlling the mobile robot to grasp the object comprises controlling the mobile robot to grasp the object based, at least in part, on the classification assigned to each of the plurality of suction cups of the suction-based gripper” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell.
As per Claim 5, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 4. Liu further discloses wherein determining uncertainty information (as per 205-215 in Fig. 2; as per 305-325 in Fig. 3) for a third extent (as per third dimension of a side of bounding box (as per third dimension of a side of bounding box) of a second face (as per dimensions of each side of bounding box) of the object (as per “items” in ¶31) (Figs. 5, 6A-8; ¶47-60) comprises:
defining a first polygon (as per perimeter of side of bounding box) relative to the second face (as per dimensions of each side of bounding box), wherein the first polygon (as per perimeter of side of bounding box) has a first value (as per “certain dimensions remain the same” in ¶60) for the third extent (as per third dimension of a side of bounding box) (Figs. 5, 6A-8; ¶47-60); and
defining a second polygon (as per perimeter of another side of bounding box) relative to the second face (as per dimensions of each side of bounding box), wherein the second polygon (as per perimeter of another side of bounding box) has a second value (as per “dimensions that cannot be seen in the image” in ¶60) for the third extent (as per third dimension of a side of bounding box), wherein the second value (as per “dimensions that cannot be seen in the image” in ¶60) is larger than the first value (as per “certain dimensions remain the same” in ¶60) (Figs. 5, 6A-8; ¶47-60).
As per Claim 6, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 5. Liu does not expressly disclose wherein assigning a classification to each of a plurality of suction cups of the suction-based gripper comprises:
associating a first classification with a suction cup located within the first polygon; and
associating a second classification with a suction cup located outside of the first polygon and within the second polygon.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders.
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein assigning a classification to each of a plurality of suction cups of the suction-based gripper comprises:
“associating a first classification with a suction cup located within the first polygon” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders; and
“associating a second classification with a suction cup located outside of the first polygon and within the second polygon” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders.
As per Claim 7, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 6. Liu does not expressly disclose wherein controlling the mobile robot to grasp the object comprises selectively activating suction cups associated with the first classification.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders.
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system “wherein controlling the mobile robot to grasp the object comprises selectively activating suction cups associated with the first classification” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell.
As per Claim 8, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 6. Liu does not expressly disclose wherein controlling the mobile robot to grasp the object comprises:
activating suction cups associated with the first classification at a first time; and
activating a first subset of suction cups associated with the second classification at a second time after the first time.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders. Saunders further discloses adjusting amount of vacuum supplied to one or more cup assemblies in response to a determined pressure level at a second time after a first time (Fig. 5; ¶96-103).
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein controlling the mobile robot to grasp the object comprises:
“activating suction cups associated with the first classification at a first time” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell; and
“activating a first subset of suction cups associated with the second classification at a second time after the first time” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell.
As per Claim 9, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 8. Liu does not expressly disclose wherein controlling the mobile robot to grasp the object further comprises:
activating a second subset of suction cups associated with the second classification at a third time after the second time,
wherein the second subset includes at least one suction cup from the first subset and at least one suction cup not included in the first subset.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders. Saunders further discloses adjusting amount of vacuum supplied to one or more cup assemblies in response to a determined pressure level at a second time after a first time (Fig. 5; ¶96-103).
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein controlling the mobile robot to grasp the object further comprises:
“activating a second subset of suction cups associated with the second classification at a third time after the second time” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell; and
“wherein the second subset includes at least one suction cup from the first subset and at least one suction cup not included in the first subset” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell.
As per Claim 10, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 9. Liu does not expressly disclose wherein the at least one suction cup not included in the first subset comprises a suction cup neighboring a suction cup in the first subset having a seal quality above a threshold seal quality.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders. Saunders further discloses adjusting amount of vacuum supplied to one or more cup assemblies in response to a determined pressure level defining a seal quality at a second time after a first time (Fig. 5; ¶95-103).
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system “wherein the at least one suction cup not included in the first subset comprises a suction cup neighboring a suction cup in the first subset having a seal quality above a threshold seal quality” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times and according to a predetermined seal quality of individual suction cups in view of imaging data as per Saunders.
As per Claim 11, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 8. Liu further does not expressly disclose wherein controlling the mobile robot to grasp the object further comprises:
deactivating one or more of the suction cups in the first subset having a seal quality below a threshold seal quality.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders. Saunders further discloses adjusting amount of vacuum supplied to one or more cup assemblies in response to a determined pressure level defining a seal quality at a second time after a first time (Fig. 5; ¶95-103). In response to a determination that the determined pressure level is below a threshold value, it is determined that the seal between the cup assembly and the object is poor and the amount of vacuum supplied to that cup assembly is reduced (¶98).
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system “wherein controlling the mobile robot to grasp the object further comprises: deactivating one or more of the suction cups in the first subset having a seal quality below a threshold seal quality” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation at specified times and according to a predetermined seal quality of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would be further adapted for navigation within a warehouse as per Russell.
As per Claim 12, the combination of Liu, Russell, Aiso, and Saunders teaches or suggests all limitations of Claim 9. Liu does not expressly disclose selecting suction cups to include in the first subset based, at least in part, on one or more of an amount of available vacuum pressure for the mobile robot, an amount of flow allowed through the suction-based gripper, or the orientation of the suction-based gripper relative to the face of the object.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso.
See rejection of Claim 4 for discussion of teachings of Saunders.
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein “selecting suction cups to include in the first subset based, at least in part, on one or more of {an amount of available vacuum pressure for the mobile robot, an amount of flow allowed through the suction-based gripper}, or the orientation of the suction-based gripper relative to the face of the object” in that in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation of individual suction cups in view of imaging data as per Saunders.
As per Claim 18, the combination of Liu, Russell, and Aiso teaches or suggests all limitations of Claim 17. Liu further discloses wherein controlling the mobile robot to grasp the object based, at least in part, on the grasp strategy further comprises:
activating one or more suction cups of the suction-based gripper as the manipulator is advanced along the pick trajectory; and
sensing as the force, a seal quality between one or more of the activated one or more suction cups and the object.
See rejection of Claim 1 for discussion of teachings of Russell.
See rejection of Claim 1 for discussion of teachings of Aiso. Aiso further discloses impedance control in which an impedance control unit (220) determines to end impedance control (as per S311) in response to determination that the value of force from a force sensor (25) exceeds a designated value (Fig. 18; ¶187-197).
See rejection of Claim 4 for discussion of teachings of Saunders. Saunders further discloses adjusting amount of vacuum supplied to one or more cup assemblies in response to a determined pressure level defining a seal quality at a second time after a first time (Fig. 5; ¶95-103).
Therefore, from these teachings of Liu, Russell, Aiso, and Saunders, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Russell, Aiso, and Saunders to the system of Liu since doing so would enhance the system by: adapting the system for navigation within a warehouse; efficiently moving objects to a target position; and improving grasping capabilities. Applying the teachings of Russell, Aiso, and Saunders to the system of Liu would result in a system wherein controlling the mobile robot to grasp the object based, at least in part, on the grasp strategy further comprises:
“activating one or more suction cups of the suction-based gripper as the manipulator is advanced along the pick trajectory” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation according to a predetermined seal quality of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would operate in view of trajectory data in time series and impedance control as per Aiso; and
“sensing as the force, a seal quality between one or more of the activated one or more suction cups and the object” in that the image data processed to govern operation of the robot arm (105) of Liu would further govern selective actuation according to a predetermined seal quality of individual suction cups in view of imaging data as per Saunders and in that the robot arm (105) of Liu would operate in view of trajectory data in time series and impedance control as per Aiso.
Response to Arguments
Applicant's arguments filed 7 May 2026 have been fully considered as follows.
Applicant argues that the objections to the Drawings should not be maintained in view of the amendments (page 8 of Amendment). This argument is persuasive. Therefore, these objections are not maintained.
Applicant argues that the claim interpretation under 35 USC 112(f) should not be maintained in view of the amendments (page 8 of Amendment). This argument is persuasive. Therefore, the claim interpretation under 35 USC 112(f) is not maintained.
Applicant argues that the rejection of Claim 1 under 35 USC 103 should not be maintained in view of the amendments because “Liu fails to teach or suggest ‘determining … a pose [as per the amendments]” (page 9 of Amendment). Rejections under 35 USC 103 in view of Liu and Russell are not maintained in view of the amendments. However, the amendments necessitated the new ground(s) of rejection presented above.
Applicant argues that the rejection of Claim 1 under 35 USC 103 should not be maintained in view of the amendments because “Russell fails to cure the deficiencies of Liu” (page 10 of Amendment). Rejections under 35 USC 103 in view of Liu and Russell are not maintained in view of the amendments. However, the amendments necessitated the new ground(s) of rejection presented above.
Applicant argues that the rejection of Claim 19 under 35 USC 103 should not be maintained in view of the amendments because “As should be appreciated from the foregoing discussion, amended claim 19 patentably distinguishes the purported combination of Liu and Russell” (page 10 of Amendment). Rejections under 35 USC 103 in view of Liu and Russell are not maintained in view of the amendments. However, the amendments necessitated the new ground(s) of rejection presented above.
Applicant argues that the rejection of Claim 20 under 35 USC 103 should not be maintained in view of the amendments because “As should be appreciated from the foregoing discussion, amended claim 20 patentably distinguishes the purported combination of Liu and Russell” (page 10 of Amendment). Rejections under 35 USC 103 in view of Liu and Russell are not maintained in view of the amendments. However, the amendments necessitated the new ground(s) of rejection presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ota (US Pub. No. 2013/0184870) discloses methods and computer-program products for generating grasp patterns for use by a robot. Zevenbergen (US Patent No. 9,205,558) discloses multiple suction cup control.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN HOLWERDA whose telephone number is (571)270-5747. The examiner can normally be reached M-F 8am - 4:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KHOI TRAN can be reached at (571) 272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656
Prosecution Insights