DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-12 are presented for examination based on the amended claims in the application filed on January 15, 2026.
Claims 1 and 6 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by US 2020/0183363 A1 Wang, Zhigang et al. [herein “Wang”].
Claims 2-5 and 11 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of US 2020/0171620 A1 Aubin, John et al. [herein “Aubin”].
Claim 7 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of CN 110653801 A Wu, Wei-Guo [herein “Wu”].
Claims 8-10 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of Ferraguti, Federica, Fabio Pini, Thomas Gale, Franck Messmer, Chiara Storchi, Francesco Leali, and Cesare Fantuzzi. "Augmented reality based approach for on-line quality assessment of polished surfaces." Robotics and Computer-Integrated Manufacturing 59 (2019): 158-167 [herein “Ferraguti”].
This action is made Final.
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 .
Response to Amendment
The amendment filed January 15, 2026 has been entered. Claims 1-12 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome each and every objection and 112(b) rejections previously set forth in the Non-Final Office Action mailed October 21, 2025, with the exception of the objection(s) to the claims as provided below. The claim interpretations of the 112(f) limitations found in claims 1-4, 7, and 10-11 is moot due the amendments on the claims which now avoids the claims from being interpreted under 35 U.S.C. 112(f).
Examiner’s Note: The examiner has found some of claims have been amended but do not properly show text that has been deleted, e.g., “The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived”).
Claim Objections
Claims 5, 8, and 10 are objected to because of the following informalities:
Claim 5, which recites “a state in which the polishing tool is in contact with the target workpiece” in Ln. 5, is improper because there is a previous recitation “a state in which the polishing tool is in contact with the target workpiece” in claim 3 Ln. 5 and claim 4 Ln. 6-7. For the purpose of examination, “a state in which the polishing tool is in contact with the target workpiece” will be interpreted as “the [[a]] state in which the polishing tool is in contact with the target workpiece” to properly refer back to the previous recitation. Claim 5 is also objected to for incorporating the deficiency of its dependent claim 4.
Claim 8, which cites “the image of the workspace” in Ln. 6, is improper because there has been no previous recitation of “the image of the workspace”. For the purpose of examination, “the image of the workspace” will be interpreted as “the image of the actual workspace”. Claim 10 is also objected to for incorporating the deficiency of its dependent claim 8.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. § 102 and 103 (or as subject to pre-AIA 35 U.S.C. § 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 6 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by US 2020/0183363 A1 Wang, Zhigang et al. [herein “Wang”].
As per claim 1, Wang teaches “A controller for estimating a polishing amount in a polishing operation which is performed by bringing a polishing tool mounted on a robot manipulator into contact with a target workpiece by force control”. (Para. 0019, “The predicted burr characteristics include the predicted burr size, predicted burr type, and predicted burr location” [e.g., a polishing amount]. Para. 0017, “FIG. 4 is a flowchart of method 100 for operating robotic deburring apparatus 10. Step 102 includes predicting, by burr characteristic prediction module 56, burr characteristics on workpiece 28 using manufacturing data 26” [for estimating a polishing amount]. Para. 0010, “FIG. 1 is a schematic diagram of robotic deburring apparatus 10, which includes robot 12, robot arm 14, deburring tool 16, and controller 18. Robot arm 14 includes force sensors 20 attached to robot arm 14, which produce force sensor data 22. Controller 18 receives, stores, and processes data, including force sensor data 22, computer aided manufacturing (CAM) data 24, and manufacturing data 26. FIG. 1 further illustrates workpiece 28 including burr 30”. Para. 0018, “As mentioned above, controller 18 has several functions, including but not limited to: predicting burr characteristics on workpiece 28 using manufacturing data 26; calculating joint positions of robot arm 14 using CAM data 24; establishing the trajectory of deburring tool 16 using CAM data 24; compensating for insufficient stiffness of robot arm 14 using feedback provided by force sensor data 22; communicating the joint positions of robot arm 14 to robot 12; communicating the trajectory of deburring tool 16 to robot 12; and directing the motion of robot 12 based on the predicted burr characteristics” [A controller for estimating a polishing amount in a polishing operation which is performed by bringing a polishing tool mounted on a robot manipulator by force control]. Para. 0011, “Robot arm 14 is attached to and holds deburring tool 16” [polishing tool mounted on a robot manipulator]. Para. 0022, “Joint position module 58 calculates the joint positions of robot arm 14 using reverse kinematics to determine the necessary movements of robot arm 14 that allow deburring tool 16 to reach each and every edge of workpiece 28 without contacting and damaging workpiece 28 in a location that was not intended to be contacted” [a polishing operation which is performed by bringing a polishing tool mounted on a robot manipulator into contact with a target workpiece]. Abstract, “A robotic deburring process that automatically, accurately, and efficiently removes burrs from a workpiece. The robotic deburring process uses CAM location data to establish deburring trajectory, physics based machining models to predict burr type and size, and force control functions to compensate inaccuracies due of inaccuracies of robots arms” [by force control]. Further see Para. 0010-0011, 0017-0019, 0022, and the Abstract. The examiner has interpreted that using a robot deburring apparatus with a control to predict burr size on the edge of a workpiece to be removed by a robot arm with a deburring tool in contact with the workpiece using force control functions and feedback to direct the motion of the robot based on predicted burr characteristics as a controller for estimating a polishing amount in a polishing operation which is performed by bringing a polishing tool mounted on a robot manipulator into contact with a target workpiece by force control.)
Wang teaches “the controller comprising: a memory which stores a motion program”. (Para. 0017, “Step 110 includes directing, by controller 18, the motion of robot 12 during the removal of burr 30 on workpiece 28” [e.g., a motion program]. Para. 0014, “Memory 54 can be configured to store information within controller 18 during operation” [the controller comprising: a memory which stores a motion program]. Further see Para. 0017 and 0014. The examiner has interpreted that a memory that stores information within the controller which directs the motion of the robot as the controller comprising: a memory which stores a motion program.)
Wang teaches “a processor configured to: estimate the polishing amount based on at least one of a motion trajectory of the polishing tool, a movement speed of the polishing tool, and a pressing force of the polishing tool against the target workpiece, which are obtained based on the motion program.” (Para. 0013, “FIG. 3 is a block diagram of controller 18 which includes processor(s) 50, communications device(s) 52, and memory 54. In other embodiments, controller 18 can include more or fewer components than components 50, 52, and 54. Processor(s) 50, in one example, are configured to implement functionality and/or process instructions for execution within controller 18. For instance, processor(s) 50 can be capable of processing instructions stored in memory 54” [a processor configured to]. Para. 0019, “Controller 18 is configured to predict burr characteristics on workpiece 28 using manufacturing data 26 and burr characteristic prediction module 56. Controller 18 receives manufacturing data 26, processes manufacturing data 26 using burr characteristics prediction module 56, and outputs the predicted burr characteristics using communication device(s) 52. Specifically, controller 18 processes manufacturing data 26 by extracting geometrical material removal and contact area data, calculating machining cutting conditions, calculating cutting forces and cutting temperatures, and estimating burr characteristics for all contacted edges. The predicted burr characteristics include the predicted burr size, predicted burr type, and predicted burr location” [estimate the polishing amount based a pressing force]. Para. 0035, “The manufacturing data includes at least one of the workpiece material, chip load, coolant properties, machining tool cutting depth, machining tool cutting speed, machining tool feed rate, machining tool cutting force, machining tool material, machining tool geometry, and machining tool cutting temperature” [based on a movement speed of the polishing tool, and a pressing force of the polishing tool]. Para. 0022, “Joint position module 58 calculates the joint positions of robot arm 14 using reverse kinematics to determine the necessary movements of robot arm 14 that allow deburring tool 16 to reach each and every edge of workpiece 28 without contacting and damaging workpiece 28 in a location that was not intended to be contacted” [polishing tool against the target workpiece]. Para. 0017, “Step 110 includes directing, by controller 18, the motion of robot 12 during the removal of burr 30 on workpiece 28” [e.g., a motion program used for estimating polishing amount]. Para. 0010, “Controller 18 receives, stores, and processes data, including force sensor data 22, computer aided manufacturing (CAM) data 24, and manufacturing data 26” [e.g., obtained based on the motion program]. Further see Para. 0010, 0013, 0017-0019, 0022, and 0035. The examiner has interpreted that a processor that predicts burr size using manufacturing data which includes machining tool cutting speed and force that is received by the controller directing the motion of the robot that allows a deburring tool to contact a workpiece to remove the burr on the workpiece as a processor configured to: estimate the polishing amount based on at least one of a motion trajectory of the polishing tool, a movement speed of the polishing tool, and a pressing force of the polishing tool against the target workpiece, which are obtained based on the motion program.)
Wang teaches “generate a recommended adjustment value for at least one of a teaching trajectory, a teaching speed and a force control parameter based on a result of comparison between an estimated polishing amount and a predetermined polishing amount reference value”. (Para. 0025, “If the force being applied to workpiece 28 is insufficient to remove burr 30, controller 18 will send a communication, using communication device(s) 52, to robot 12 indicating robot 12 should increase the force being applied by robot arm 14” [e.g., generate a recommended adjustment value for a force control parameter based on a result of comparison between an estimated polishing amount and a predetermined polishing amount reference value]. “Further, if robot arm 14 deflects when being pressed against workpiece 28, deburring tool 16 is likely not in its intended orientation/angle for removing burr 30 from workpiece 28. Force control module 62 further processes force sensor data 22 from force sensors 20 to verify and adjust the orientation/angle of deburring tool 16 to achieve proper removal of burr 30 from workpiece 28” [generate a recommended adjustment value for a teaching trajectory based on a result of comparison between the estimated polishing amount and a predetermined polishing amount reference value]. Para. 0024, “Controller 18 is configured to communicate to robot 12, using communication device(s) 52, the joint positions of robot arm 14, the trajectory of deburring tool 16, the speed of deburring tool 16, and the feed rates of deburring tool 16. After the information has been communicated from controller 18 to robot 12, controller 18 directs the motion of robot 12 and the operation of deburring tool 16 during the deburring process. Robot 12 uses the joint positions of robot arm 14, the trajectory of deburring tool 16, the speed of deburring tool 16, and the feed rates of deburring tool 16 to control robot arm 14 and complete the deburring process.” [generate a recommended adjustment value for at least one of a teaching trajectory, a teaching speed]. Further see Para. 0023-0025. The examiner has interpreted that determining the burr is not sufficiently removed to indicate an increase in the force applied by the robot arm, an adjustment in the orientation and angle of the deburring tool, and speed of the deburring tool as a generate a recommended adjustment value for at least one of a teaching trajectory, a teaching speed and a force control parameter based on a result of comparison between an estimated polishing amount and a predetermined polishing amount reference value.)
Wang teaches “control the robot manipulator based on at least one of an adjusted teaching trajectory, an adjusted teaching speed and an adjusted force control parameter”. (Para. 0025, “If the force being applied to workpiece 28 is insufficient to remove burr 30, controller 18 will send a communication, using communication device(s) 52, to robot 12 indicating robot 12 should increase the force being applied by robot arm 14” [e.g., control the robot manipulator based on at least one of an adjusted force control parameter]. “Further, if robot arm 14 deflects when being pressed against workpiece 28, deburring tool 16 is likely not in its intended orientation/angle for removing burr 30 from workpiece 28. Force control module 62 further processes force sensor data 22 from force sensors 20 to verify and adjust the orientation/angle of deburring tool 16 to achieve proper removal of burr 30 from workpiece 28” [control the robot manipulator based on at least one of an adjusted teaching trajectory]. Para. 0024, “Controller 18 is configured to communicate to robot 12, using communication device(s) 52, the joint positions of robot arm 14, the trajectory of deburring tool 16, the speed of deburring tool 16, and the feed rates of deburring tool 16. After the information has been communicated from controller 18 to robot 12, controller 18 directs the motion of robot 12 and the operation of deburring tool 16 during the deburring process. Robot 12 uses the joint positions of robot arm 14, the trajectory of deburring tool 16, the speed of deburring tool 16, and the feed rates of deburring tool 16 to control robot arm 14 and complete the deburring process.” [control the robot manipulator based on an adjusted teaching speed]. Further see Para. 0023-0025. The examiner has interpreted that sending communication to the robot to increase the force applied, adjust the orientation and angle of the deburring tool, and speed to complete the deburring process when it is determined that the burr is not sufficiently removed as control the robot manipulator based on at least one of an adjusted teaching trajectory, an adjusted teaching speed and an adjusted force control parameter.)
As per claim 6, Wang teaches “estimate the polishing amount based on any of a model in which a correlation between the motion trajectory of the polishing tool and the polishing amount is linearly or curvedly approximated based on actual measurement data, a calculation model in which a correlation between the movement speed of the polishing tool and the polishing amount is linearly or curvedly approximated based on actual measurement data, and a calculation model in which a correlation between the pressing force of the polishing tool and the polishing amount is linearly or curvedly approximated based on actual measurement data.” (Para. 0019, “Controller 18 is configured to predict burr characteristics on workpiece 28 using manufacturing data 26 and burr characteristic prediction module 56. Controller 18 receives manufacturing data 26, processes manufacturing data 26 using burr characteristics prediction module 56, and outputs the predicted burr characteristics using communication device(s) 52. Specifically, controller 18 processes manufacturing data 26 by extracting geometrical material removal and contact area data, calculating machining cutting conditions, calculating cutting forces and cutting temperatures, and estimating burr characteristics for all contacted edges. The predicted burr characteristics include the predicted burr size, predicted burr type, and predicted burr location” [estimate the polishing amount based a pressing force]. Para. 0025, “If the force being applied to workpiece 28 is insufficient to remove burr 30, controller 18 will send a communication, using communication device(s) 52, to robot 12 indicating robot 12 should increase the force being applied by robot arm 14” [estimates the polishing amount based on a calculation model in which a correlation between the pressing force of the polishing tool and the polishing amount is linearly or curvedly approximated]. “Further, if robot arm 14 deflects when being pressed against workpiece 28, deburring tool 16 is likely not in its intended orientation/angle for removing burr 30 from workpiece 28. Force control module 62 further processes force sensor data 22 from force sensors 20 to verify and adjust the orientation/angle of deburring tool 16 to achieve proper removal of burr 30 from workpiece 28. Controller 18 uses force sensor data 22 from force sensors 20 to verify the appropriate process parameters are being achieved during the deburring process” [based on actual measurement data]. Further see Para. 0019 and 0025. The examiner has interpreted that predicting burr size using manufacturing data which includes machining tool cutting speed and force that estimates the polishing amount based on increasing the force to sufficient remove the burr and verified through force sensor data obtained through force sensors as estimate the polishing amount based on a calculation model in which a correlation between the pressing force of the polishing tool and the polishing amount is linearly or curvedly approximated based on actual measurement data.)
Claim Rejections - 35 U.S.C. § 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.
Claims 2-5 and 11 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of US 2020/0171620 A1 Aubin, John et al. [herein “Aubin”].
As per claim 2, Wang teaches “wherein the memory further stores the force control parameter, which is a parameter related to the force control”. (Para. 0014, “Memory 54 can be configured to store information within controller 18 during operation” [a memory further stores]. Para. 0019, “Controller 18 receives manufacturing data 26”. Para. 0035, “The manufacturing data includes at least one of the workpiece material, chip load, coolant properties, machining tool cutting depth, machining tool cutting speed, machining tool feed rate, machining tool cutting force, machining tool material, machining tool geometry, and machining tool cutting temperature” [stores the force control parameter, which is a parameter related to the force control]. Further see Para. 0014, 0019, and 0035. The examiner has interpreted that a memory that stores information within the controller that receives machining tool cutting force as wherein the memory further stores the force control parameter, which is a parameter related to the force control.)
Wang teaches “execute a simulation of the force control based on the motion program and the force control parameter”. (Para. 0019, “Specifically, controller 18 processes manufacturing data 26 by extracting geometrical material removal and contact area data, calculating machining cutting conditions, calculating cutting forces and cutting temperatures, and estimating burr characteristics for all contacted edges” [execute a simulation of the force control based on the force control parameter]. Para. 0010 “Controller 18 receives, stores, and processes data, including force sensor data 22, computer aided manufacturing (CAM) data 24, and manufacturing data 26” [e.g., obtained based on the motion program]. Further see Para. 0010 and 0019. The examiner has interpreted that using manufacturing data which includes machining tool cutting force that is received by the controller directing the motion of the robot to calculate the cutting force as further comprises a execute a simulation of the force control based on the motion program and the force control parameter.)
Wang does not specifically teach “determines the motion trajectory, the movement speed, and the pressing force based on position information of the polishing tool obtained from results of the simulation of the force control.”
However, in the same field of endeavor namely optimizing the control of a robotic arm to polish materials, Aubin teaches “determines the motion trajectory, the movement speed, and the pressing force based on position information of the polishing tool obtained from results of the simulation of the force control.” (Para. 0101, “In an example of the disclosed sanding operation when the sanding force 128 is monitored and selectively modified (i.e., when the sanding force 128 is variable), a model sanding-force value (e.g., theoretical or threshold parameter value) of the sanding force 128 is determined (e.g., computationally) that achieves the model material removal rate 124” [the simulation of the force control to determine the pressing force]. Para. 0099, “Linear movement of the sanding tool 102, in the direction approximately perpendicular to the surface 202 of the structure 200, enables selective control of the sanding force 128, applied to the surface 202 by the sanding tool 102, by increasing or decreasing the magnitude of the sanding force 128 resulting from a change in spatial location 186 of the sanding tool 102 closer to or farther from the surface 202” [e.g., based on position information of the polishing tool obtained from results of the simulation of the force control]. Para. 0041, “the sanding position refers to a particular spatial location 186 (FIG. 1) of the sanding tool 102 and a particular spatial angular orientation 188” [e.g., position and trajectory]. Para. 0110, “the speed of the robotic manipulator 104 and, thus, the sanding-tool velocity 134 is computationally determined based on a change in a spatial position of the robotic manipulator 104 over time” [determining the motion trajectory and movement speed]. Further see Para. 0041, 0099-0101, and 0110. The examiner has interpreted that determining a model sanding force value computationally that varies with position to generate a force that increases or decreases with position as well as determining the speed of the robotic manipulator as a function of the location and angular orientation over time as determines the motion trajectory, the movement speed, and the pressing force based on position information of the polishing tool obtained from results of the simulation of the force control.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “determines the motion trajectory, the movement speed, and the pressing force based on position information of the polishing tool obtained from results of the simulation of the force control” as conceptually seen from the teaching of Aubin, into that of Wang because this modification of determining the location, direction, and force of the polishing tool for the advantageous purpose of modifying the observed polishing parameters to obtain the desired removal amount of material from an object (Aubin, Para. 0005). Further motivation to combine be that Wang and Aubin are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 3, Wang does not specifically teach “virtually generate, based on the position information of the polishing tool obtained from the results of the simulation of the force control, a pressing force exerted on the target workpiece from the polishing tool in a state in which the polishing tool is in contact with the target workpiece.”
However, Aubin teaches “virtually generate, based on the position information of the polishing tool obtained from the results of the simulation of the force control, a pressing force exerted on the target workpiece from the polishing tool in a state in which the polishing tool is in contact with the target workpiece.” (Para. 0101, “In an example of the disclosed sanding operation when the sanding force 128 is monitored and selectively modified (i.e., when the sanding force 128 is variable), a model sanding-force value (e.g., theoretical or threshold parameter value) of the sanding force 128 is determined (e.g., computationally) that achieves the model material removal rate 124. During the sanding operation, the force sensor 108 detects the sanding force 128 and the control unit 112 determines an actual sanding-force value (e.g., measured or instantaneous parameter value) of the sanding force 128” [virtually generate a pressing force exerted on the target workpiece from the polishing tool in a state in which the polishing tool is in contact with the target workpiece]. Para. 0099, “Linear movement of the sanding tool 102, in the direction approximately perpendicular to the surface 202 of the structure 200, enables selective control of the sanding force 128, applied to the surface 202 by the sanding tool 102, by increasing or decreasing the magnitude of the sanding force 128 resulting from a change in spatial location 186 of the sanding tool 102 closer to or farther from the surface 202” [e.g., based on the position information of the polishing tool obtained from the results of the simulation of the force control]. Further see Para. 0099-0101. The examiner has interpreted that determining a model sanding force value computationally that varies with position to generate a force that increases or decreases with position of the actual force during the sanding process as virtually generate, based on the position information of the polishing tool obtained from the results of the simulation of the force control, a pressing force exerted on the target workpiece from the polishing tool in a state in which the polishing tool is in contact with the target workpiece.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “virtually generate, based on the position information of the polishing tool obtained from the results of the simulation of the force control, a pressing force exerted on the target workpiece from the polishing tool in a state in which the polishing tool is in contact with the target workpiece” as conceptually seen from the teaching of Aubin, into that of Wang because this modification of determining the force of the polishing tool for the advantageous purpose of modifying the observed polishing parameters to obtain the desired removal amount of material from an object (Aubin, Para. 0005). Further motivation to combine be that Wang and Aubin are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 4, Wang teaches “execute, using an equation of motion representing the robot manipulator, a physics simulation of motion of the robot manipulator based on the force control parameter.” (Para. 0018, “As mentioned above, controller 18 has several functions, including but not limited to: predicting burr characteristics on workpiece 28 using manufacturing data 26; calculating joint positions of robot arm 14 using CAM data 24; establishing the trajectory of deburring tool 16 using CAM data 24; compensating for insufficient stiffness of robot arm 14 using feedback provided by force sensor data 22; communicating the joint positions of robot arm 14 to robot 12” [e.g. execute, using an equation of motion representing the robot manipulator]. Para. 0025, “Controller 18 is further configured to compensate for insufficient stiffness of robot arm 14 using force sensor data 22” [a physics simulation of motion of the robot manipulator based on the force control parameter]. Further see Para. 0018 and 0025. The examiner has interpreted that calculating the joint positions of the robot and the trajectory of the deburring tool that is then compensated using the force senor data as execute, using an equation of motion representing the robot manipulator, a physics simulation of motion of the robot manipulator based on the force control parameter.)
Wang does not specifically teach “determine the pressing force based on the position information of the polishing tool obtained by the physics simulation in the state in which the polishing tool is in contact with the target workpiece.”
However, Aubin teaches “determine the pressing force based on the position information of the polishing tool obtained by the physics simulation in the state in which the polishing tool is in contact with the target workpiece.” (Para. 0101, “In an example of the disclosed sanding operation when the sanding force 128 is monitored and selectively modified (i.e., when the sanding force 128 is variable), a model sanding-force value (e.g., theoretical or threshold parameter value) of the sanding force 128 is determined (e.g., computationally) that achieves the model material removal rate 124. During the sanding operation, the force sensor 108 detects the sanding force 128 and the control unit 112 determines an actual sanding-force value (e.g., measured or instantaneous parameter value) of the sanding force 128” [determine the pressing force]. Para. 0099, “Linear movement of the sanding tool 102, in the direction approximately perpendicular to the surface 202 of the structure 200, enables selective control of the sanding force 128, applied to the surface 202 by the sanding tool 102, by increasing or decreasing the magnitude of the sanding force 128 resulting from a change in spatial location 186 of the sanding tool 102 closer to or farther from the surface 202” [e.g., based on the position information of the polishing tool obtained by the physics simulation in the state in which the polishing tool is in contact with the target workpiece]. Further see Para. 0099-0101. The examiner has interpreted that determining a model sanding force value computationally that varies with position to generate a force that increases or decreases with position of the actual force during the sanding process as determine the pressing force based on the position information of the polishing tool obtained by the physics simulation in the state in which the polishing tool is in contact with the target workpiece.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “determine the pressing force based on the position information of the polishing tool obtained by the physics simulation in the state in which the polishing tool is in contact with the target workpiece” as conceptually seen from the teaching of Aubin, into that of Wang because this modification of determining the force of the polishing tool for the advantageous purpose of modifying the observed polishing parameters to obtain the desired removal amount of material from an object (Aubin, Para. 0005). Further motivation to combine be that Wang and Aubin are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 5, Wang teaches “determine the pressing force based on any of a coefficient related to rigidity of the target workpiece, a coefficient related to rigidity of the polishing tool, and a spring constant of the polishing tool, as well as a distance of the polishing tool to the target workpiece in a state in which the polishing tool is in contact with the target workpiece.” (Para. 0018, “As mentioned above, controller 18 has several functions, including but not limited to: predicting burr characteristics on workpiece 28 using manufacturing data 26; calculating joint positions of robot arm 14 using CAM data 24; establishing the trajectory of deburring tool 16 using CAM data 24; compensating for insufficient stiffness of robot arm 14 using feedback provided by force sensor data 22; communicating the joint positions of robot arm 14 to robot 12”. Para. 0025, “Controller 18 is further configured to compensate for insufficient stiffness of robot arm 14 using force sensor data 22” [e.g., determine the pressing force based on a coefficient related to rigidity of the polishing tool]. Para. 0021, “The CAM software generates and stores CAM data 24 regarding the machining tool centerline trajectory and the machining tool location history during the machining and finishing operations for workpiece 28. Controller 18 uses the stored machining tool centerline trajectory and location history to establish the trajectory of deburring tool” [e.g., as well as a distance of the polishing tool to the target workpiece in a state in which the polishing tool is in contact with the target workpiece]. Further see Para. 0018, 0021, and 0025. The examiner has interpreted that compensating using the force senor data to account for insufficient stiff of the robot arm in the projection of the joint position and trajectory of the deburring tool using CAM data to store the machining tool trajectory and location history during operations as determine the pressing force based on a coefficient related to rigidity of the polishing tool as well as a distance of the polishing tool to the target workpiece in a state in which the polishing tool is in contact with the target workpiece.)
As per claim 11, Wang teaches “select a polishing tool to be used from a plurality of types of polishing tools based on information indicating a required polishing amount or polishing area”. (Para. 0002, “The type of burr formed and its characteristics depend on many factors including, but not limited to, the machining process, tool properties, coolant properties, and workpiece material” [select a polishing tool to be used based on information indicating a required polishing area]. Para. 0012, “FIG. 2 illustrates a few types of burr 30 formations on workpiece 28, including a Poisson burr, a roll-over burr, and a tear burr. A Poisson burr is a burr that is formed when the machined material bulges outwards when the machining tool is applied to the workpiece under a downward pressure. A roll-over burr is typically produced when a cutting tool exits a material and the sharp tip of the tool pushes the material rather than cutting through or chipping it, thus creating the burr. A tear burr is created when material is torn away from a workpiece, rather than being sheared; this usually occurs during a punching or drilling process. A cut-off burr (not shown in FIG. 2) is produced when a portion of material falls away from the main workpiece, tearing it and leaving behind a burr” [select a polishing tool to be used from a plurality of types of polishing tools based on information indicating a polishing area]. Further see Para. 0002 and 0012. The examiner has interpreted that forming a specific type of burr formation on a workpiece based on the tool properties as select a polishing tool to be used from a plurality of types of polishing tools based on information indicating a required polishing amount or polishing area.)
Wang teaches “virtually installs the selected polishing tool to the robot manipulator and executes a simulation of the force control”. (Para. 0019, “Specifically, controller 18 processes manufacturing data 26 by extracting geometrical material removal and contact area data, calculating machining cutting conditions, calculating cutting forces and cutting temperatures, and estimating burr characteristics for all contacted edges” [e.g., virtually installs the selected polishing tool to the robot manipulator and executes a simulation of the force control]. Further see Para. 0019. The examiner has interpreted that using manufacturing data which includes machining tool cutting force to estimate burr characteristics at all contacted edges as virtually installs the selected polishing tool to the robot manipulator and executes a simulation of the force control.)
Claim 7 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of CN 110653801 A Wu, Wei-Guo [herein “Wu”].
As per claim 7, Wang does not specifically teach “execute machine learning based on training data composed of input data including the motion trajectory of the polishing tool, the movement speed of the polishing tool and the pressing force of the polishing tool against the target workpiece, and response data, which is an actual polishing amount corresponding to the input data, and estimate the polishing amount using a learning model constructed by the machine learning.”
However, in the same field of endeavor namely optimizing the control of a robotic arm to polish materials, Wu teaches “execute machine learning based on training data composed of input data including the motion trajectory of the polishing tool, the movement speed of the polishing tool and the pressing force of the polishing tool against the target workpiece, and response data, which is an actual polishing amount corresponding to the input data, and estimate the polishing amount using a learning model constructed by the machine learning.” (Pg. 6, “The state quantities of the guidance control system are defined as: the position and attitude amount X of the end manipulator” [the motion trajectory of the polishing tool] “the operating force vector FE and the moment amount vector ME” [the pressing force of the polishing tool against the target workpiece], “and the manipulated change amount XO of the work object (5), XO includes the work object (5 ) 'S geometric size, rough surface state, and geometric shape quantity” [e.g., the polishing amount]. Pg. 6, “Vector XD and velocity constitute a behavior space”. Pg. 7, “The fourth layer is the prediction evaluation layer, which predicts the system status at the next moment based on the current system state Sj and behavior Aj” [the movement speed of the polishing tool]. Pg. 13, “the learning algorithm of each layer of agents can choose different learning algorithms such as cerebellar neural network (CMAC), feedforward neural network (FFN), fuzzy algorithm (FA), and reinforcement learning algorithm (RL)” [e.g., machine learning]. Pg. 7, “Learning is carried out using a hierarchical deep learning system, which refers to the four levels of empirical memory layer, skill extraction layer, behavior generalization layer, and predictive evaluation layer. The first layer in the above deep learning system is the empirical memory layer. The training data is a time series composed of the system state Sj, behavior Aj, and instant evaluation Pj at each moment, j = 1,2, ..., m. The learning result is obtained by The empirical value mapping of the current state Sj and the current behavior Aj to the value function QE (Sj, Aj)” [e.g., execute machine learning based on training data composed of input data and estimate the polishing amount using a learning model constructed by the machine learning]. Pg. 7, “The fourth layer is the prediction evaluation layer, which predicts the system status at the next moment based on the current system state Sj and behavior Aj, and generates corresponding real-time evaluation feedback on the performance of the first three layers of agents” [predicting size of work object in real-time, e.g., response data, which is an actual polishing amount corresponding to the input data]. Further see Pg. 6-7 and 13. The examiner has interpreted that having a learning algorithm carried out by a hierarchical deep learning system using a feedforward neural network that takes training data of the position and attitude amount of the end manipulator, operating force vector, and velocity to predict the system state which is the change amount of the work object size to generated evaluation feedback of the robot in real-time as execute machine learning based on training data composed of input data including the motion trajectory of the polishing tool, the movement speed of the polishing tool and the pressing force of the polishing tool against the target workpiece, and response data, which is an actual polishing amount corresponding to the input data, and estimate the polishing amount using a learning model constructed by the machine learning.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “execute machine learning based on training data composed of input data including the motion trajectory of the polishing tool, the movement speed of the polishing tool and the pressing force of the polishing tool against the target workpiece, and response data, which is an actual polishing amount corresponding to the input data, and estimate the polishing amount using a learning model constructed by the machine learning” as conceptually seen from the teaching of Wu, into that of Wang because this modification of using a machine learning algorithm for the advantageous purpose of providing an efficient and automatic means for predicting and correcting the movements of the robot (Wu, Pg. 7). Further motivation to combine be that Wang and Wu are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
Claims 8-10 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang as applied to claim 1 above, and in view of Ferraguti, Federica, Fabio Pini, Thomas Gale, Franck Messmer, Chiara Storchi, Francesco Leali, and Cesare Fantuzzi. "Augmented reality based approach for on-line quality assessment of polished surfaces." Robotics and Computer-Integrated Manufacturing 59 (2019): 158-167 [herein “Ferraguti”].
As per claim 8, Wang does not specifically teach “an imaging device which captures an image of an actual workspace including the robot manipulator and the target workpiece” and “a display which superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image.”
However, in the same field of endeavor namely optimizing the control of a robotic arm to polish materials, Ferraguti teaches “an imaging device which captures an image of an actual workspace including the robot manipulator and the target workpiece”. (Pg. 162 Sect. 3, “An example of a detection position for a Controller Xbox is shown in Fig. 5(a). The detection position is typically chosen with a diagonal view, such that two or three sides of the object can be visualized (see e.g. Fig. 5(b) where the angle of the camera that corresponds to the selected detection position of Fig. 5(a) is shown)” [image of the target workpiece]. “The virtual object is superimposed and anchored to the real one, and the tracking continues even though the object is moved to another place, obviously inside the field of view of the camera” [an imaging device which captures an image]. Fig. 12 and 13 show the target workpiece in the actual workspace along with the robot manipulator in the background as captured by the augmented reality devices. Further Pg. 164 Sect. 4, “Figs. 10(b) and (c) respectively show the offline programming environment in the configuration to provide measurement toolpaths and a real measurement stage over the surfaces of the demo mold. For the subsequent quality assessment stage with the enhancements by AR devices, the alignment of the virtual mold directly on the real object is performed according to the procedure described in Section 3.1.2” Fig. 10(c) captures an image of the robot manipulator in the actual workspace which would be similar for captures for the enhancements by the augmented reality devices. Further see Sect. 3 and Fig. 10, 12, and 13. The examiner has interpreted that visualizing a virtual object to be superimposed to a real object inside the field of view of a camera of the augmented reality devices which also captures the target workpiece in its actual workspace as shown in Figure 12 and 13 with the robot manipulator in the background of the images and for subsequent quality assessment stage with the enhancements by AR devices as shown in Figure 10(c) as an imaging device which captures an image of an actual workspace including the robot manipulator and the target workpiece.)
Ferraguti teaches “a display which superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image”. (Pg. 160 Sect. 2, “The method consists of some steps and it is based on the use of a surface measurement system and augmented reality devices, as depicted in Fig. 2” [a display]. Pg. 161, Sect. 3, “the proposed methodology can be applied in polishing processes to visualize surface data using augmented reality, directly overlaying these data on the actual component. As a result, the operator can assess the quality achieved with the polishing steps by looking at the surface data that are directly projected on the real component through an AR device” [a display device which superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image]. Furthermore, Pg. 165, Sect. 4, “The virtual mold is superimposed and anchored to the real one, and the tracking continues even though the mold is moved to another place, obviously inside the field of view of the HoloLens. The data is displayed to the HoloLens users with the interface outlined in Section 3.2. Assuming that the initial manual alignment or automatic alignment has functioned correctly, the virtual data overlays the real physical component at a 1:1 scale Fig. 12”. Fig. 12 shows an augmented reality view of the estimated polishing amount on the image of the workpiece, e.g., superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image. Further see Sect. 2-4. The examiner has interpreted that using an augmented reality (AR) device to overlay surface data for polishing and automatic alignment on the real component through an AR device as a display which superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “an imaging device which captures an image of an actual workspace including the robot manipulator and the target workpiece” and “a display which superimposes an image representing the estimated polishing amount on the image of the workspace as an augmented reality image” as conceptually seen from the teaching of Ferraguti, into that of Wang because this modification of an augmented reality capture and display device for the advantageous purpose of increasing work quality and production through an enhanced quality assessment experience (Ferraguti, Pg. 158 Abstract). Further motivation to combine be that Wang and Ferraguti are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 9, Wang teaches “wherein the memory further stores model data representing shapes of the robot manipulator, the polishing tool and the target workpiece and information on arrangement positions of the robot manipulator, the polishing tool and the target workpiece”. (Para. 0014, “Memory 54 can be configured to store information within controller 18 during operation” [the memory]. Para. 0010, “Controller 18 receives, stores, and processes data, including force sensor data 22, computer aided manufacturing (CAM) data 24, and manufacturing data 26” [the memory further stores model data and information on arrangement positions]. Para. 0046, “The manufacturing data includes at least one of the workpiece material, chip load, coolant properties, machining tool cutting depth, machining tool cutting speed, machining tool feed rate, machining tool cutting force, machining tool material, machining tool geometry, and machining tool cutting temperature” [representing shape of the robot manipulator and the polishing tool]. Para. 0019, “controller 18 processes manufacturing data 26 by extracting geometrical material removal” [representing shape of target workpiece]. Para. 0022, “Controller 18 is also configured to calculate the joint positions of robot arm 14 using CAM data 24 and joint position module 58. As discussed above, the CAM software generates and stores CAM data 24 regarding the machining tool centerline trajectory and the machining tool location history during the machining and finishing operations for workpiece 28” [information on arrangement positions of the polishing tool]. “Therefore, CAM data 24 includes data regarding the perimeter of workpiece 28 and the trajectory of deburring tool 16” [information on arrangement positions of the polishing tool and the target workpiece]. “With the trajectory of the deburring tool known, controller 18 is configured to calculate, using joint position module 58, the joint positions of robot arm 14 that allow deburring tool 16 to reach every edge of workpiece 28” [information on arrangement positions of the robot manipulator]. Further see Para. 0010, 0014, 0019, 0022, and 0046. The examiner has interpreted that a memory that stores information within the controller which includes manufacturing data which extracts geometrical material of the workpiece and machining tool geometry as well as computer aided manufacturing data which stores machining tool centerline, machining tool location history, the perimeter of workpiece, and the trajectory of deburring tool as wherein the memory further stores model data representing shapes of the robot manipulator, the polishing tool and the target workpiece and information on arrangement positions of the robot manipulator, the polishing tool and the target workpiece.)
Wang does not specifically teach “comprises a display which superimposes an image representing the estimated polishing amount on a virtual reality image arranged in a virtual workspace including the polishing tool and the target workpiece using the model data and the information on the arrangement positions”.
However, Ferraguti teaches “comprises a display which superimposes an image representing the estimated polishing amount on a virtual reality image arranged in a virtual workspace including the polishing tool and the target workpiece using the model data and the information on the arrangement positions.” (Pg. 160 Sect. 2, “The method consists of some steps and it is based on the use of a surface measurement system and augmented reality devices, as depicted in Fig. 2” [comprises a display device]. Pg. 161, Sect. 3, “the proposed methodology can be applied in polishing processes to visualize surface data using augmented reality, directly overlaying these data on the actual component. As a result, the operator can assess the quality achieved with the polishing steps by looking at the surface data that are directly projected on the real component through an AR device” [a display device which superimposes an image representing the estimated polishing amount on a virtual reality image in a virtual workspace]. Furthermore, Pg. 165, Sect. 4, “The virtual mold is superimposed and anchored to the real one, and the tracking continues even though the mold is moved to another place, obviously inside the field of view of the HoloLens. The data is displayed to the HoloLens users with the interface outlined in Section 3.2. Assuming that the initial manual alignment or automatic alignment has functioned correctly, the virtual data overlays the real physical component at a 1:1 scale Fig. 12”. Fig. 12 shows an augmented reality view of the estimated polishing amount on the image of the workpiece, e.g., superimposes an image representing the estimated polishing amount on a virtual reality image arranged in a virtual workspace. Pg. 162 Sect. 4, “The demonstrator workcell integrates two robot-based polishing technologies, respectively Abrasive Finishing, AF, and Fluidjet Polishing, FP with the human intervention for final tuning through manual operations for finest finishing polishing of the surfaces [41]. The robot that is used to perform the robotic polishing is a 6 DOF ABB-IRB 4600-60 industrial robot” [e.g., robot manipulator having a polishing tool]. Pg. 165 Sect. 4, “The robot is used also as positioning system for the CWS optical head” [robot manipulator having a polishing tool also having the measurement system] “while dedicated software coordinate the robot movements and data acquisition” [the information on the arrangement positions]. Pg. 163 Sect. 4, “As in the scheme, on the same CWS server PC have been implemented HoloLens alignment and AR metrology applications who respectively manage the alignment of the 3D model and the real component and the connection to track the metrology data collected by the CWS system through HoloLens devices” [using the model data]. Further see Sect. 2-4. The examiner has interpreted that using an augmented reality (AR) device to overlay surface data for polishing and automatic alignment on the real component through an AR device through superimposing the virtual data on the real object where the AR devices capture the workspace having the robot polishing system which contains the measurement system in addition to tracking and coordinating the robot movements and positioning to align the 3D model of the object with the real metrology data as comprises a display which superimposes an image representing the estimated polishing amount on a virtual reality image arranged in a virtual workspace including the polishing tool and the target workpiece using the model data and the information on the arrangement positions.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “comprises a display which superimposes an image representing the estimated polishing amount on a virtual reality image arranged in a virtual workspace including the polishing tool and the target workpiece using the model data and the information on the arrangement positions” as conceptually seen from the teaching of Ferraguti, into that of Wang because this modification of an augmented reality display device for the advantageous purpose of increasing work quality and production through an enhanced quality assessment experience (Ferraguti, Pg. 158 Abstract). Further motivation to combine be that Wang and Ferraguti are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 10, Wang does not specifically teach “wherein the display further superimposes an image representing the recommended adjustment value on the image of the actual”.
However, Ferraguti teaches “wherein the display further superimposes an image representing the recommended adjustment value on the image of the actual workspace.” (Pg. 160 Sect. 2, “The method consists of some steps and it is based on the use of a surface measurement system and augmented reality devices, as depicted in Fig. 2” [display device]. Pg. 161, Sect. 3, “the proposed methodology can be applied in polishing processes to visualize surface data using augmented reality, directly overlaying these data on the actual component. As a result, the operator can assess the quality achieved with the polishing steps by looking at the surface data that are directly projected on the real component through an AR device” [a display device further superimposes an image representing the recommended adjustment value on the image]. Furthermore, Pg. 165, Sect. 4, “The virtual mold is superimposed and anchored to the real one, and the tracking continues even though the mold is moved to another place, obviously inside the field of view of the HoloLens. The data is displayed to the HoloLens users with the interface outlined in Section 3.2. Assuming that the initial manual alignment or automatic alignment has functioned correctly, the virtual data overlays the real physical component at a 1:1 scale Fig. 12” [e.g., wherein the display device further superimposes an image representing the recommended adjustment value on the image]. Fig. 12 and 13 show the target workpiece in the actual workspace along with the robot manipulator in the background as captured by the augmented reality devices and show superimposition of the mold on the actual mold. Further see Sect. 2-4. The examiner has interpreted that using an augmented reality (AR) device to overlay surface data for polishing and automatic alignment on the real component through an AR device through superimposing the virtual data on the real object in the physical workspace as wherein the display device further superimposes an image representing the recommended adjustment value on the image of the actual workspace or the virtual reality image.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the display further superimposes an image representing the recommended adjustment value on the image of the actual workspace” as conceptually seen from the teaching of Ferraguti, into that of Wang because this modification of an augmented reality display device for the advantageous purpose of increasing work quality and production through an enhanced quality assessment experience (Ferraguti, Pg. 158 Abstract). Further motivation to combine be that Wang and Ferraguti are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
As per claim 12, Wang does not specifically teach “wherein the display further superimposes an image representing the recommended adjustment value on the virtual reality image”.
However, Ferraguti teaches “wherein the display further superimposes an image representing the recommended adjustment value on the virtual reality image.” (Pg. 160 Sect. 2, “The method consists of some steps and it is based on the use of a surface measurement system and augmented reality devices, as depicted in Fig. 2” [comprises a display device]. Pg. 161, Sect. 3, “the proposed methodology can be applied in polishing processes to visualize surface data using augmented reality, directly overlaying these data on the actual component. As a result, the operator can assess the quality achieved with the polishing steps by looking at the surface data that are directly projected on the real component through an AR device” [a display device further superimposes an image representing the recommended adjustment value on virtual reality image]. Furthermore, Pg. 165, Sect. 4, “The virtual mold is superimposed and anchored to the real one, and the tracking continues even though the mold is moved to another place, obviously inside the field of view of the HoloLens. The data is displayed to the HoloLens users with the interface outlined in Section 3.2. Assuming that the initial manual alignment or automatic alignment has functioned correctly, the virtual data overlays the real physical component at a 1:1 scale Fig. 12” [e.g., wherein the display device further superimposes an image representing the recommended adjustment value on the virtual reality image]. Further see Sect. 2-4. The examiner has interpreted that using an augmented reality (AR) device to overlay surface data for polishing and automatic alignment on the real component through an AR device through superimposing the virtual data on the real object as wherein the display further superimposes an image representing the recommended adjustment value on the virtual reality image.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add “wherein the display further superimposes an image representing the recommended adjustment value on the virtual reality image” as conceptually seen from the teaching of Ferraguti, into that of Wang because this modification of an augmented reality display device for the advantageous purpose of increasing work quality and production through an enhanced quality assessment experience (Ferraguti, Pg. 158 Abstract). Further motivation to combine be that Wang and Ferraguti are analogous art to the current claim are directed to optimizing the control of a robotic arm to polish materials.
Response to Arguments
Applicant's arguments filed on January 15, 2026 have been fully considered but they are not persuasive.
Applicant argues that reference does not teach each and every limitation in the amend claim 1 because cited reference fails to teach “a processor configured to: estimate the polishing amount based on at least one of a motion trajectory of the polishing tool, a movement speed of the polishing tool, and a pressing force of the polishing tool against the target workpiece” (See Applicant’s response, Pg. 9-10).
MPEP § 2143.03 states “All words in a claim must be considered in judging the patentability of that claim against the prior art” and “Examiners must consider all claim limitations when determining patentability of an invention over the prior art.”
As above in the rejection of claim 1, Wang discloses “estimate the polishing amount based on at least one of a motion trajectory of the polishing tool, a movement speed of the polishing tool, and a pressing force of the polishing tool against the target workpiece” as a processor that predicts burr size using manufacturing data which includes machining tool cutting speed and force that is received by the controller directing the motion of the robot that allows a deburring tool to contact a workpiece to remove the burr on the workpiece. Determining the size of the burr to removed is estimating the amount of polish required. This determination is based on the machine tool cutting speed, force, and motion. Thus, the claimed limitation is taught. Additional emphasis has been added to this mapping in the rejection above to the amended limitation.
Therefore, all of the limitations of the amended claims 1 are disclosed in Wang. Therefore, applicant’s arguments are not persuasive and the rejection of claim 1 as anticipated by Wang is maintained.
Applicant’s arguments, see Pg. 7-9, filed January 15, 2026, with respect to the rejection(s) of claims 1-11 under 35 U.S.C. § 101 have been fully considered and are persuasive with regards to the amended independent claim integrating the claimed invention into a practical application. Therefore, the rejection has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2021/ 0394328 A1 Hemes, Brett et al. teaches a pressure controller configured to control and apply the desired pressure based upon the measured rotational velocity of the backup pad or the measured debris from the substrate that results from abrading from a robotic device configured to manipulate the tool.
US 2021/0237221 A1 Takada, Nobuyuki et al. teaches adjusting at least one of a height and a distance to the substrate of a supporting member 300 while polishing the substrate.
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.
Examiner’s Note: The examiner has cited particular columns and line numbers in the reference that applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. In the case of amending the claimed invention, the applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for the proper interpretation and also to verify and ascertain the metes and bound of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Simeon P Drapeau whose telephone number is (571)-272-1173. The examiner can normally be reached Monday - Friday, 8 a.m. - 5 p.m. ET.
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/SIMEON P DRAPEAU/ Examiner, Art Unit 2188
/RYAN F PITARO/ Supervisory Patent Examiner, Art Unit 2188