DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This application has been examined. Claims 1-20 are pending.
The prior art submitted on 6/5/25, and 8/19/25 has been considered.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Clams 1-20, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yap et al. (10899560).
As per claim 1, Yap et al. disclose a system for automatically selecting an end effector function for material handling, said system comprising: a robot comprising end effectors of different type (see at least columns 7-8, lines 48-18; and columns 23-24, lines 35-44, all para. disclose end effector comprises suction gripper, magnetic connector, and vacuum subsystem); one or more machine vision components (see at least column 18, lines 16-54 disclose plurality of cameras); a controller in electronic communication with the one or more machine vision components and the robot, said controller comprising software instructions, which when executed, configure the controller to: receive image data from the one or more machine vision components of a part at a workspace for handling by the robot (see at least column 19, lines 1-34 disclose using the vision system, the control system determines an object to sort of a location that has a high probability of being suitable grasp spot); analyze said image data to determine characteristics of said part (see at least columns 10-11, lines 63-23 disclose analyze image data to determine information related to the object; and columns 20-21, lines 35-30 disclose determine attributes of the object, such as its weight, center of gravity, and flexibility); and based, at least in part, on said analyzed image data, including said characteristics, determine at least one of the end effectors to utilize for the part (see at least columns 21-22, lines 31-56; and columns 23-24, lines 35-44, all para. disclose determine at least one of the end effector to utilize for the object).
As per claim 2, Yap et al. disclose end effectors comprises a mechanical gripper, a vacuum subsystem, and one or more magnets (see at least columns 7-8, lines 48-18; and columns 23-24, lines 35-44, all para. disclose end effector comprises suction gripper, magnetic connector, and vacuum subsystem).
As per claim 3, Yap et al. disclose end effectors are integrated into a unitary subassembly (see at least columns 23-24, lines 35-44 disclose the end effector is coupled to the motion device via one or more magnetic components on the end effector).
As per claim 4, Yap et al. disclose mechanical gripper comprises a first mechanical gripping component, a second mechanical gripping component, and at least one motor for moving the first mechanical gripping component relative to the second mechanical gripping component; said one or more magnets comprise one or more magnets located at the first mechanical gripping component; and said vacuum subsystem comprises tubes extending through the first mechanical gripping component (see at least columns 7-8, lines 47-17; and columns 23-24, lines 35-44, all para. disclose the end effector is coupled to the motion device via one or more magnetic components on the end effector).
As per claim 5, Yap et al. disclose characteristics comprise at least two of: a shape, a material, and an orientation of the part (see at least columns 10-11, lines 63-23 disclose analyze image data to determine information related to the object, determine the object orientation and a height of the object; and columns 20-21, lines 35-30 disclose determine attributes of the object, such as its weight, center of gravity, and flexibility);
As per claim 6, Yap et al. disclose the characteristics comprise each of: the shape, the material, and the orientation of the part (see at least columns 20-21, lines 35-30 disclose the attributes of the object).
As per claims 7-8, Yap et al. disclose the controller is configured to: assign a value to each of the characteristics; generate a score based, at least in part, on the values of the characteristics; and select the at least one of the end effectors to utilize for the part based, at least in part, on the score; and the controller is configured to generate the score using a weighted summation the controller is configured to generate the score using a weighted summation (see at least columns 25-26, lines 30-48 disclose determined the percentage probability of success map and select at least one of the end effector to utilize for the part).
As per claim 9, Yap et al. disclose the controller is configured to: select more than one of the end effectors to utilize for the part where the score is above a first threshold; and select all of the end effectors to utilize for the part where the score is above a second threshold (see at least columns 24-26, lines 45-48 disclose select the motion primitive to utilize for the part).
As per claim 10, Yap et al. disclose the robot comprises an articulating arm; the end effectors are located at a distal end of the articulating arm; and each of the one or more machine vision components comprises a camera (see at least column 18, lines 16-54 disclose the robot comprises and articulating arm, the end effector are located at a distal end of the articulating arm and vision system with cameras).
As per claims 11-12, Yap et al. disclose at least one of the cameras is connected to the articulating arm of the robot in view of the end effectors; and at least one other of the cameras is mechanically independent of the robot and positioned overhead to view the workspace (see at least column 18, lines 16-54 disclose plurality of cameras).
As per claim 13, Yap et al. disclose the controller is configured to command operation of the robot causing manipulation of the part within the workspace using the at least one of the end effectors (see at least column 19, lines 1-58 disclose the control system determines an object to sort or a location that has a high probability of being a suitable grasp spot).
As per claim 14, Yap et al. disclose the controller is configured to, following command of the operations of the robot causing manipulation of the part: receive further image data from the one or more machine vision components of the part at the workspace for handling; analyze said further image data to determine if the part was satisfactorily manipulated at the workspace, including a comparison of data indicating post-manipulation part orientation with pre-determined data indicating expected post-manipulation part orientation; and based, at least in part, on said analyzed further image data, including said comparison, provide negative or positive feedback (see at least columns 19-20, lines 35-44 disclose confirming the correctness of the contents of the receptacles after some objects have been placed in them; and columns 24-25, lines 15-67 disclose the probability maps describe the change of a successful grasp at various locations in a scene containing one or more of objects).
As per claim 15, Yap et al. disclose the controller is configured to: assign a value to each of the characteristics; select the at least one of the end effectors to utilize for the part based, at least in part, on the values; and adjust the values assigned to each of the characteristics for the part based, at least in part, on the feedback (see at least columns 24-25, lines 45-67 disclose select the plurality of motion primitive and percentage probability of success).
As per claims 16-17, Yap et al. disclose the controller comprises one or more artificial intelligence (AI) algorithms; and the values are stored in tables in association with the characteristics (see at least column 26, lines 1-48 disclose machine learning algorithm).
As per claim 18, Yap et al. disclose a reorientation bracket configured to receive the part in a first one of the orientations, wherein said operations of the robot causing manipulation of the part within the workspace using the at least one of the end effectors includes grasping the part using the at least one of the end effectors, releasing the part at the reorientation bracket in the first one of the orientations from the at least one of the end effectors, further operating the robot to reorient the end effectors, and grasping the part at the reorientation bracket a second time (see at least columns 7-8, lines 20-18 disclose a plurality of chutes to direct an object of the plurality of objects into a container of the plurality of containers; and column 20, lines 4-44 disclose a processor determines a planned placement and a planned orientation of a first object from a tote to a receptacle)
As per claim 19, Yap et al. disclose a system for automatically selecting an end effector function for material handling, said system comprising: a robot comprising an articulating arm and end effectors of different type located at a distal end of the articulating arm (see at least columns 7-8, lines 48-18; and columns 23-24, lines 35-44, all para. disclose end effector comprises suction gripper, magnetic connector, and vacuum subsystem), wherein: said effectors comprises a mechanical gripper, a vacuum subsystem, and one or more magnets integrated into a single component (see at least columns 7-8, lines 48-18; and columns 23-24, lines 35-44, all para. disclose end effector comprises suction gripper, magnetic connector, and vacuum subsystem); said mechanical gripper comprises a first mechanical gripping component, a second mechanical gripping component, and at least one motor for moving the first mechanical gripping component relative to the second mechanical gripping component; said one or more magnets comprise one or more magnets located at the first mechanical gripping component; and said vacuum subsystem comprises tubes extending through the first mechanical gripping component (see at least columns 7-8, lines 47-17; and columns 23-24, lines 35-44, all para. disclose the end effector is coupled to the motion device via one or more magnetic components on the end effector); one or more machine vision components including at least one camera mechanically affixed to the articulating arm of the robot in view of the end effectors and at least one other camera mechanically independent of the robot and positioned overhead to view a workspace (see at least column 18, lines 16-54 disclose plurality of cameras); a controller in electronic communication with the one or more machine vision components and the robot, said controller comprising software instructions and at least one artificial intelligence algorithm, which when executed, configure the controller to: receive image data from the one or more machine vision components of a part at the workspace for handling (see at least column 19, lines 1-43 disclose using the vision system, the control system determines and object to sort or a location that has a high probability of being a suitable grasp spot); analyze said image data to determine characteristics of said part, said characteristics comprising a shape, a material, and an orientation of the part (see at least columns 20-21, lines 35-30 disclose the attributes of object, such as its weight, center of gravity, and flexibility); wherein said analysis includes: assigning a value to each of the characteristics, wherein the values are stored in tables in association with the characteristics; and generating a score based, at least in part, on the values of the characteristics using a weighted summation (see at least columns 25-26, lines 1-48 disclose determine probability of success); and based, at least in part, on said analyzed image data, selecting at least one of the end effectors to utilize for the part, including selecting the at least one of the end effectors to utilize for the part based, at least in part, on the score, including by selecting more than one of the end effectors to utilize for the part where the score is above a first threshold and selecting all of the end effectors to utilize for the part where the score is above a second threshold (see at least columns 24-25, lines 10-67 disclose plurality of select motion primitive); a reorientation bracket configured to receive the part in a first orientation; command operation of the robot causing manipulation of the part within the workspace using the at least one of the end effectors, including grasping the part using the at least one of the end effectors, releasing the part at the reorientation bracket in the first orientation, further operating the robot to reorient the end effectors, and grasping the part at the reorientation bracket a second time (see at least columns 7-8, lines 23-17 disclose a plurality of chutes to direct an object of the plurality of objects into a container of the plurality of containers; and column 20, lines 4-34 disclose a processor determines a planned placement and a planned orientation of a first object from a tote to a receptacle); receive further image data from the one or more machine vision components of the part at the workspace for handling; analyze said further image data to determine if the part was satisfactorily manipulated at the workspace, including a comparison of data indicating post-manipulation part orientation with pre-determined data indicating expected post-manipulation part orientation; based, at least in part, on said analyzed further image data, including said comparison, provide negative or positive feedback; and adjust the values assigned to each of the characteristics for the part based, at least in part, on the feedback (see at least columns 19-20, lines 35-44 disclose confirming the correctness of the contents of the receptacles after some objects have been placed in them; and columns 24-25, lines 15-67 disclose the probability maps describe the change of a successful grasp at various locations in a scene containing one or more objects).
Claim 20 is a method claim corresponding to system claim 1 above. Therefore, it is rejected for the same rationales set forth as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
. Stuckey et al. (11833670)
. High et al. (10017322)
. Theobald (9764675)
. Curtis (7188821)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DALENA TRAN whose telephone number is (571)272-6968. The examiner can normally be reached M-F 7AM-5PM.
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/DALENA TRAN/Primary Examiner, Art Unit 3657
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