Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The Office Action is in response to the application filed on 08/04/2025. Claims 1-3, 6-15, and 18-26 are presently pending and are presented for examination.
Response to Arguments
Applicant’s arguments, see page 2, filed 08/04/2025, with respect to the objection to claims 3 and 15 have been fully considered and are persuasive. The amendments to the claims have overcome the objection. The objection to claims 3 and 15 has been withdrawn.
Applicant’s arguments, see pages 2-4, filed 08/04/2025, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 102 as being anticipated by Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) have been fully considered and are persuasive. The amendments have distinguished the claims from the disclosure of Landi. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103 in view of Landi and Horesh et al. US 11188616 B2 (“Horesh”). Applicant argues on pages 2-4 that Horesh does not teach the elements “reduced order model for the robot” that “uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot”. However, as disclosed in further detail below, Horesh teaches these elements in Column 9, lines 39-67, Column 10, lines 1-28.
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.
Claim(s) 1, 3, 6, 10, 13, 15, 18, 22, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”).
Regarding Claim 1. Landi teaches a robot, comprising:
a set of one or more actuators (FIG. 1 shows a number of potential robots with a set of actuators);
at least one sensor (Several exteroceptive sensors can be placed to stop the robot and avoid collisions [first page, middle of left column]. If robot velocities are low, external proximity/contact-force sensors [8] or robot skin (e.g. Fraunhofer IFF Tactile Sensor Systems [9]) can detect collisions with human operators and safely stop the robot); and
a controller comprising a set of one or more processors and a memory containing a controller application (A controller brings the system back inside the safe region [page 2, top of left column]), wherein the controller application configures the set of processors to control the robot by performing the steps of:
defining a configuration space comprising position variables that characterize physical positions of a robot (FIG. 3 shows a 3D plot of the trajectories recorded and executed under optimization-based control, which reads on a configuration space [page 5, bottom of left column]);
defining a safe set within the configuration space identifying positions where the robot is safe (the safe region of page 2);
computing a safe velocity in the configuration space and a current position of the robot such that the robot remains in the safe set (“The basic idea is to define a set of safe states and to use the control barrier functions to guarantee the forward invariance”, which is done using Control Barrier Functions around the robot end-effector to avoid collisions with the environment [First page, bottom of left column]); and
instructing a tracking controller to cause an actual velocity of the robot to track to the safe velocity (Positions and velocities were used to compute the desired end-effector pos and velocity, to reproduce the recorded motion by tracking the desired values with a PD controller [Page 5, left column]. The controller is described in page 2, top of left column, and brings the robot back inside the safe region when the safety constraint is violated).
Landi does not teach:
determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and
the robot movement is computed based on the reduced order model.
However, Horesh teaches:
determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and
the robot movement is computed based on the reduced order model (Proper orthogonal decomposition (POD) (a type of reduced order model) lowers the computational cost by reducing the order of the system. This involves a process described [Column 9, lines 39-57], and is used to project the discretized dynamic system of equations to a lower dimension. Then, the lower dimensional system is evolved in time, resulting in substantial computational savings over evolution of the discretized system at the original scale. Estimates of the full scale solution are then computed through projections onto the space of the left singular vectors [Column 9, lines 58-67, Column 10, lines 1-28]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and the robot movement is computed based on the reduced order model as taught by Horesh so as to reduce the computational cost on the system, as described by Horesh.
Regarding Claim 3. Landi in combination with Horesh teaches the robot of claim 1.
Landi also teaches:
wherein computing the safe velocity comprises computing a new safe velocity when the sensor indicates that the robot is approaching a boundary of the safe set such that the velocity is unsafe (The trajectory replanning is performed so as to minimize the difference between the nominal velocity and the commanded one [page 2, left column]. The CBF, which is meant to keep the robot inside a safe region, applies a smooth constraint, described in the formulas on page 2, right column. On page 4, a solution is shown to an optimization problem in which the safety is ensured by means of the safety barrier functions, shown in equations 15-17. Joint acceleration and velocity bounds are included as constraints α and β. The resulting controller modifies the behavior (including the mentioned constraints) when a collision between a link and an obstacle is approaching).
Regarding Claim 6. Landi in combination with Horesh teaches the robot of claim 1.
Landi also teaches:
wherein the safe velocity is tracked at a rate higher than the rate at which the robot approaches a boundary of the safe set (This is common in the art, as referenced by Landi in page 1, top of right column).
Regarding Claim 10. Landi in combination with Horesh teaches the robot of claim 1.
Landi also teaches:
wherein instructing the robot to track to the safe velocity comprises directing the set of one or more actuators to spin at a rate that drives the robot to track to the safe velocity (Page 3, both columns).
Regarding Claim 13. Landi teaches a method for model-free safe control of robotics platforms comprising:
defining a configuration space comprising position variables that characterize physical positions of a robot (FIG. 3 shows a 3D plot of the trajectories recorded and executed under optimization-based control, which reads on a configuration space [page 5, bottom of left column]);
defining a safe set within the configuration space that represents positions where the platform is safe (the safe region of page 2);
computing a safe velocity in the configuration space and a current position of the robot such that the robot remains in the safe set (“The basic idea is to define a set of safe states and to use the control barrier functions to guarantee the forward invariance”, which is done using Control Barrier Functions around the robot end-effector to avoid collisions with the environment [First page, bottom of left column]); and
instructing a tracking controller to cause an actual velocity of the robot to track to the safe velocity (Positions and velocities were used to compute the desired end-effector pos and velocity, to reproduce the recorded motion by tracking the desired values with a PD controller [Page 5, left column]. The controller is described in page 2, top of left column, and brings the robot back inside the safe region when the safety constraint is violated).
Landi does not teach:
determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and
the robot movement is computed based on the reduced order model.
However, Horesh teaches:
determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and
the robot movement is computed based on the reduced order model (Proper orthogonal decomposition (POD) (a type of reduced order model) lowers the computational cost by reducing the order of the system. This involves a process described [Column 9, lines 39-57], and is used to project the discretized dynamic system of equations to a lower dimension. Then, the lower dimensional system is evolved in time, resulting in substantial computational savings over evolution of the discretized system at the original scale. Estimates of the full scale solution are then computed through projections onto the space of the left singular vectors [Column 9, lines 58-67, Column 10, lines 1-28]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with determining a reduced order model for the robot based on the safe set, wherein the reduced order model is defined over the configuration space as a reduced-order representation of the robot's dynamics, wherein the reduced order model uses kinematics of a lower dynamic order than the kinematics used by a full-order physical model of the robot; and the robot movement is computed based on the reduced order model as taught by Horesh so as to reduce the computational cost on the system, as described by Horesh.
Regarding Claim 15. Landi in combination with Horesh teaches the method of claim 13.
Landi also teaches:
wherein computing the safe velocity comprises computing a new safe velocity when the sensor indicates that the robot is approaching a boundary of the safe set such that the velocity is unsafe (The trajectory replanning is performed so as to minimize the difference between the nominal velocity and the commanded one [page 2, left column]. The CBF, which is meant to keep the robot inside a safe region, applies a smooth constraint, described in the formulas on page 2, right column. On page 4, a solution is shown to an optimization problem in which the safety is ensured by means of the safety barrier functions, shown in equations 15-17. Joint acceleration and velocity bounds are included as constraints α and β. The resulting controller modifies the behavior (including the mentioned constraints) when a collision between a link and an obstacle is approaching).
Regarding Claim 18. Landi in combination with Horesh teaches the method of claim 13.
Landi also teaches:
wherein the safe velocity is tracked at a rate higher than the rate at which the platform approaches a boundary of the safe set (This is common in the art, as referenced by Landi in page 1, top of right column).
Regarding Claim 22. Landi in combination with Horesh teaches the method of claim 13.
Landi also teaches:
wherein instructing the platform to track to the safe velocity comprises directing a set of one or more actuators to spin at a rate that drives the platform to track to track to the safe velocity (Page 3, both columns).
Regarding Claim 25. Landi in combination with Horesh teaches the robot of claim 1.
Landi also teaches:
wherein the model is a control barrier function (CBF) (Control Barrier Functions (CBFs) presented in [18], [second page, top of left column]).
Landi does not teach:
wherein the model is a reduced order model (A method for solving a dynamical system, comprising deriving a reduced order model of a dynamical system, which reads on a reduced-order dynamical model [Column 1, lines 66-67, Column 2, lines 1-10]. Dynamic models of this nature can be used to map complex systems and processes, such as autonomous agents (robots) [Column 1, lines 18-31]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the model is a reduced order model as taught by Horesh so as to streamline the model and reduce consumption of computing resources for the CBF.
Regarding Claim 26. Landi in combination with Horesh teaches the method of claim 13.
Landi also teaches:
wherein the model is a control barrier function (CBF) (Control Barrier Functions (CBFs) presented in [18], [second page, top of left column]).
Landi does not teach:
wherein the model is a reduced order model (A method for solving a dynamical system, comprising deriving a reduced order model of a dynamical system, which reads on a reduced-order dynamical model [Column 1, lines 66-67, Column 2, lines 1-10]. Dynamic models of this nature can be used to map complex systems and processes, such as autonomous agents (robots) [Column 1, lines 18-31]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the model is a reduced order model as taught by Horesh so as to streamline the model and reduce consumption of computing resources for the CBF.
Claim(s) 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”) as applied to claims 1 and 13 above, and further in view of Sampedro et al. US 10131053 B1 (“Sampedro”).
Regarding Claim 2. Landi in combination with Horesh teaches the robot of claim 1.
Landi does not teach:
wherein the computing of the safe velocity and instructing the actuators to track to the safe velocity are repeated until the robot reaches a target.
However, Sampedro teaches:
wherein the computing of the safe velocity and instructing the actuators to track to the safe velocity are repeated until the robot reaches a target (The target motion state 580 defines a desired motion state to be reached by the actuators of a robot, such as robot 100. For example, the target motion state may define particular positions of the actuators and zero velocity, zero acceleration, etc. The target motion state may define particular positions of the actuators and non-zero velocities, accelerations, and/or jerks for one or more of the actuators. For instance, the target motion state may be a motion state that causes an end effector to be at a particular position and arrive at that position with a particular velocity [Column 18, lines 28-40]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the computing of the safe velocity and instructing the actuators to track to the safe velocity are repeated until the robot reaches a target as taught by Sampedro so as to allow the robot to reach a target location while maintaining a safe velocity.
Regarding Claim 14. Landi in combination with Horesh teaches the method of claim 13.
Landi does not teach:
the computing of the safe velocity and instructing a set of one or more actuators to track to the safe velocity are repeated until the platform reaches a target.
However, Sampedro teaches:
the computing of the safe velocity and instructing a set of one or more actuators to track to the safe velocity are repeated until the platform reaches a target (The target motion state 580 defines a desired motion state to be reached by the actuators of a robot, such as robot 100. For example, the target motion state may define particular positions of the actuators and zero velocity, zero acceleration, etc. The target motion state may define particular positions of the actuators and non-zero velocities, accelerations, and/or jerks for one or more of the actuators. For instance, the target motion state may be a motion state that causes an end effector to be at a particular position and arrive at that position with a particular velocity [Column 18, lines 28-40]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with the computing of the safe velocity and instructing a set of one or more actuators to track to the safe velocity are repeated until the platform reaches a target as taught by Sampedro so as to allow the robot to reach a target location while maintaining a safe velocity.
Claim(s) 7, 12, 19, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”) as applied to claims 1 and 13 above, and further in view of Ariyur et al. US 20070286456 A1 (“Ariyur”).
Regarding Claim 7. Landi in combination with Horesh teaches the robot of claim 1.
Landi does not teach:
wherein tracking the safe velocity is exponentially stable.
However, Ariyur teaches:
wherein tracking the safe velocity is exponentially stable (Paragraph 49 describes monitoring the velocity of an object in an exponentially stable system).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein tracking the safe velocity is exponentially stable as taught by Ariyur so as to ensure the stability of the tracking system.
Regarding Claim 12. Landi in combination with Horesh teaches the robot of claim 1.
Landi does not teach:
wherein computing the safe velocity further comprises adjusting for errors in continuous approximations of distances.
However, Ariyur teaches:
wherein computing the safe velocity further comprises adjusting for errors in continuous approximations of distances (Paragraph 49).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein computing the safe velocity further comprises adjusting for errors in continuous approximations of distances as taught by Ariyur so as to compensate for errors caused by noise or vibrations in the robot system.
Regarding Claim 19. Landi in combination with Horesh teaches the method of claim 13.
Landi does not teach:
wherein tracking the safe velocity is exponentially stable.
However, Ariyur teaches:
wherein tracking the safe velocity is exponentially stable (Paragraph 49 describes monitoring the velocity of an object in an exponentially stable system).
Regarding Claim 24. Landi in combination with Horesh teaches the method of claim 13.
Landi does not teach:
wherein computing the safe velocity further comprises adjusting for errors in continuous approximations of distances (Paragraph 49).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein computing the safe velocity further comprises adjusting for errors in continuous approximations of distances as taught by Ariyur so as to compensate for errors caused by noise or vibrations in the robot system.
Claim(s) 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”) as applied to claims 1 and 13 above, and further in view of Mitra et al. US 20160179999 A1 (“Mitra”).
Regarding Claim 8. Landi in combination with Horesh teaches the robot of claim 1.
Landi does not teach:
wherein tracking the safe velocity is input-to-state stable.
However, Mitra teaches:
wherein tracking the safe velocity is input-to-state stable (Paragraph 19 describes a method of bounded verification with various ways of integrating computation of discrepancy functions to verify safety of a large, non-linear and possibly hybrid model of a control system interacting with a physical process (or plant). In one embodiment, a non-input-to-state (non-IS) method determines a local discrepancy function of a larger system that nonetheless verifies safety of the larger system. In another embodiment, the non-IS discrepancy function of a subsystem is turned into an input-to-state (IS) discrepancy function that can be combined with IS discrepancy function(s) of other subsystem(s) of the overall model, where safety can be verified from a reduced-sized model that is constructed from these IS discrepancy functions [paragraph 19]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein tracking the safe velocity is input-to-state stable as taught by Mitra so as to ensure the stability of the tracking of the safe velocity, particularly if the accelerationg of the velocity is nonlinear.
Regarding Claim 20. Landi in combination with Horesh teaches the method of claim 13.
Landi does not teach:
wherein tracking the safe velocity is input-to-state stable (Paragraph 19 describes a method of bounded verification with various ways of integrating computation of discrepancy functions to verify safety of a large, non-linear and possibly hybrid model of a control system interacting with a physical process (or plant). In one embodiment, a non-input-to-state (non-IS) method determines a local discrepancy function of a larger system that nonetheless verifies safety of the larger system. In another embodiment, the non-IS discrepancy function of a subsystem is turned into an input-to-state (IS) discrepancy function that can be combined with IS discrepancy function(s) of other subsystem(s) of the overall model, where safety can be verified from a reduced-sized model that is constructed from these IS discrepancy functions [paragraph 19]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein tracking the safe velocity is input-to-state stable as taught by Mitra so as to ensure the stability of the tracking of the safe velocity, particularly if the accelerationg of the velocity is nonlinear.
Claim(s) 9 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”) in view of Mitra et al. US 20160179999 A1 (“Mitra”) as applied to claims 8 and above, and further in view of Tucker et al. US 20210027877 A1 (“Tucker”).
Regarding Claim 9. Landi in combination with Horesh and Mitra teaches the robot of claim 8.
Landi does not teach:
wherein the stability of the tracking satisfies control Lyapunov functions (CLFs).
However, Tucker teaches:
wherein the stability of the tracking satisfies control Lyapunov functions (CLFs) (Paragraph 60).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the stability of the tracking satisfies control Lyapunov functions (CLFs) as taught by Tucker because this is a common method of tracking stability that is well-known in the art.
Regarding Claim 21. Landi in combination with Horesh and Mitra teaches the method of claim 20.
Landi does not teach:
wherein the stability of the tracking satisfies control Lyapunov functions (CLFs).
However, Tucker teaches:
wherein the stability of the tracking satisfies control Lyapunov functions (CLFs) (Paragraph 60).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the stability of the tracking satisfies control Lyapunov functions (CLFs) as taught by Tucker because this is a common method of tracking stability that is well-known in the art.
Claim(s) 11 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Chiara Talignani Landi, Federica Ferraguti, Silvia Costi, Marcello Bonfe, Cristian Secchi, “Safety Barrier Functions for Human-Robot Interaction with Industrial Manipulators”, 2019, 18th European Control Conference (ECC), 2019, Page(s): 2565-2570 (Landi”) in view of Horesh et al. US 11188616 B2 (“Horesh”) as applied to claims 1 and 13 above, and further in view of Tucker et al. US 20210027877 A1 (“Tucker”).
Regarding Claim 11. Landi in combination with Horesh teaches the robot of claim 1.
Landi does not teach:
wherein the controller is further configured to define a modified safe set based on CLFs.
However, Tucker teaches:
wherein the controller is further configured to define a modified safe set based on CLFs (Paragraph 60).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the controller is further configured to define a modified safe set based on CLFs as taught by Tucker because this is a common method of tracking stability that is well-known in the art.
Regarding Claim 23. Landi in combination with Horesh teaches the method of claim 13.
Landi does not teach:
further comprising defining a modified safe set based on CLFs.
However, Tucker teaches:
further comprising defining a modified safe set based on CLFs (Paragraph 60).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the invention of Landi with wherein the controller is further configured to define a modified safe set based on CLFs as taught by Tucker because this is a common method of tracking stability that is well-known in the art.
Conclusion
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 AARON G CAIN whose telephone number is (571)272-7009. The examiner can normally be reached Monday: 7:30am - 4:30pm EST to Friday 7:30pm - 4:30am.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.G.C./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656