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 09/14/2026. Claims 1-3, 7-15, and 19-24 are presently pending and are presented for examination.
Response to Arguments
Applicant’s arguments, see pages 1-4, filed 09/14/2026, with respect to the rejection(s) of claim(s) 1-3, 7-15, and 19-24 under 35 U.S.C. 112(a) and 112(b) have been fully considered and are persuasive. The amendments to the claims, which fall within the disclosure of the applicant’s specification, have overcome the previous 112 rejections. Therefore, the rejection has been withdrawn.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 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”).
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, 10, 13, 15, and 22 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”).
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]);
determining a tracking controller configured to track a velocity (positions and velocities are tracked using a PD controller [page 5, top left column]), wherein the tracking controller has a tracking characterization characterizing how fast the tracking controller tracks the velocity (Column 5, top left column);
defining a control barrier function (CBF) that defines a safe set within the configuration space identifying positions where the robot is safe (Page 2, right column, particularly the top to middle part of the column), wherein the CBF includes a CBF constraint limiting the robot to approach a boundary of the safe set at a velocity that is less than or equal to a distance from the robot to the boundary multiplied by a positive constant (Formula 2, shown page 2, top of the right column);
computing a safe control input based on the CBF, the tracking characterization, the CBF constraint and a current position of the robot, wherein the safe control input satisfies the CBF constraint, and wherein the tracking characterization is greater than the positive constraint, such that, when the tracking controller tracks the safe control input based on the tracking characterization, the robot remains in the safe set according to the full-order dynamics of the robot (“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]. Combined with Formula 2 on page 2, top of right column, and the disclosure reads on the claim language);
providing the safe control input to the tracking controller (A control input can be used to keep the robot system state inside a safe set, wherein U is the set of admissible control values [Page 2, bottom of left column]. Additionally, velocity constraints are described in page 4, bottom of right column. While this does not explicitly teach providing the safe control input as the velocity as included in set U, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to include velocity as one of the control input values); and
instructing a tracking controller to track to the velocity (Positions and velocities were used to compute the desired end-effector pose 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).
Regarding Claim 3. Landi 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 the 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 10. Landi teaches the robot of claim 1.
Landi also teaches:
wherein instructing the robot to track to the velocity comprises directing the set of one or more actuators to spin at a rate that drives the robot to track to the 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]);
determining a tracking controller configured to track a velocity (positions and velocities are tracked using a PD controller [page 5, top left column]), wherein the tracking controller has a tracking characterization characterizing how fast the tracking controller tracks the velocity (Column 5, top left column);
defining a control barrier function (CBF) that defines a safe set within the configuration space identifying positions where the robot is safe (Page 2, right column, particularly the top to middle part of the column), wherein the CBF includes a CBF constraint limiting the robot to approach a boundary of the safe set at a velocity that is less than or equal to a distance from the robot to the boundary multiplied by a positive constant (Formula 2, shown page 2, top of the right column);
computing a safe control input based on the CBF, the tracking characterization, the CBF constraint and a current position of the robot, wherein the safe control input satisfies the CBF constraint, and wherein the tracking characterization is greater than the positive constraint, such that, when the tracking controller tracks the safe control input based on the tracking characterization, the robot remains in the safe set according to the full-order dynamics of the robot (“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]. Combined with Formula 2 on page 2, top of right column, and the disclosure reads on the claim language);
providing the safe control input to the tracking controller (A control input can be used to keep the robot system state inside a safe set, wherein U is the set of admissible control values [Page 2, bottom of left column]. Additionally, velocity constraints are described in page 4, bottom of right column. While this does not explicitly teach providing the safe control input as the velocity as included in set U, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to include velocity as one of the control input values); and
instructing a tracking controller to track to the velocity (Positions and velocities were used to compute the desired end-effector pose 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).
Regarding Claim 15. Landi 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 the 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 22. Landi teaches the method of claim 13.
Landi also teaches:
wherein instructing the platform to track to the velocity comprises directing a set of one or more actuators to spin at a rate that drives the platform to track to the velocity (Page 3, both columns).
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”) as applied to claims 1 and 13 above, and further in view of Sampedro et al. US 10131053 B1 (“Sampedro”).
Regarding Claim 2. Landi 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 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 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 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 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 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 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 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”) as applied to claims 1 and 13 above, and further in view of Ariyur et al. US 20070286456 A1 (“Ariyur”).
Regarding Claim 7. Landi 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 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 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 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”) as applied to claims 1 and 13 above, and further in view of Mitra et al. US 20160179999 A1 (“Mitra”).
Regarding Claim 8. Landi 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 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 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 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 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”) as applied to claims 1 and 13 above, and further in view of Tucker et al. US 20210027877 A1 (“Tucker”).
Regarding Claim 11. Landi 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 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
THIS ACTION IS MADE FINAL. 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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/AARON G CAIN/Examiner, Art Unit 3656
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