DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Response to Amendment
Claim 1 has been amended. No claims have been added or cancelled.
Response to Arguments
Applicant's arguments filed 02/18/2026 have been fully considered and are partially persuasive in that the current rejection has been withdrawn and been replaced with an updated rejection with new art. The arguments presented concerning the previous rejection are considered moot as the rejection has been withdrawn and an updated grounds of rejection has been presented below. As a result of the new grounds of rejection, this action is NOT final.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Romano et al. (US20190134821, referred to as Romano)
Regarding claim 1: Romano discloses: A controller for positioning an interventional device the controller comprising: a memory that stores at least one of: a forward predictive model configured with embedded kinematics of the interventional device to receive commanded positioning motion of the interventional device and to output data related to a prediction of a navigated pose of a device portion of the interventional device based on the commanded positioning motion and the embedded kinematics, or a control predictive model configured with kinematics of the interventional device to receive target pose data of the device portion of the interventional device and to output data related to a prediction of a positioning motion of the interventional device based on the target pose data and the kinematics; and ([0071] The processing unit 640 in the illustrated embodiment is also configured to control the arm using the perception information and the predicted position information. The predicted position information may be determined at the control rate and used for modifying subsequent control instructions. In the illustrated embodiment, the processing unit 640 utilizes the predicted position information for feedback and control of the arm 610 between acquisitions of perception information, thereby utilizing predicted position information when current perception information is not available, and utilizing perception information for reliability and accuracy when current perception information is available) at least one processor in communication with the memory, wherein the at least one processor is configured to at least one of: (i) apply the forward predictive model to the commanded positioning motion of the interventional device to predict a navigated pose of the device portion, and generate positioning data to control positioning of the device portion to a target pose based on the predicted navigated pose of the device portion, and controlling positioning of the device portion to the target pose; or ii) apply the control predictive model to the target pose data of the device portion to predict a positioning motion of the interventional device, and generate positioning commands to control the positioning of the device portion to the target pose based on the predicted positioning motion of the interventional device, and controlling positioning of the device portion to the target pose. ([0071] The predicted position information may be determined at the control rate and used for modifying subsequent control instructions. In the illustrated embodiment, the processing unit 640 utilizes the predicted position information for feedback and control of the arm 610 between acquisitions of perception information, thereby utilizing predicted position information when current perception information is not available, and utilizing perception information for reliability and accuracy when current perception information is available.)
Examiner’s Note: Romano first discloses in at least 0071 both the control predictive model in the first half of the claim and option ii) in the second half of the claim.
Regarding claim 2: Romano discloses: The controller of claim 1,
Romano further discloses: wherein the forward predictive model is trained on forward kinematics of the interventional device and the control predictive model is an inverse predictive model trained on inverse kinematics of the interventional device. ([0099] The depicted task planning block 804 is configured to receive a task as an input (e.g., from the user interface 802 and/or the communication block 806) and to provide task primitives as an output. Task planning, as used herein, may refer to scheduling a sequence or multiple parallel sequences of tasks for a robot to perform. The task planning block 804 may utilize modern control architecture, such as Task-Based Reactive Control (TBRC), may be used to formulate a control problem as a constrained minimization of task errors, and may efficiently exploit the redundancy of platforms provided in various embodiments. It may be noted that inverse TBRC and Cost Optimization may be utilized to plan the task in order to achieve certain task targets, if the goal of the tasks is considered, to achieve a final state of a control algorithm. In some embodiments, the planning for a typical brake bleeding task is a typical single task-based planning problem with several requirements.)
Regarding claim 3: Romano discloses: The controller of claim 1,
Romano further discloses: wherein the device portion is an end-effector of the interventional device. ([0070] The depicted manipulator unit 620 is mounted proximate an end of the arm 610 and is configured to grasp the target object 602. As seen in FIG. 6, the perception acquisition unit 630 of the illustrated embodiment is mounted to the arm (e.g., proximate an end of the arm 610 with the manipulator unit 620 attached thereto).)
Regarding claim 4: Romano discloses: The controller of claim 1,
Romano further discloses: wherein the at least one processor is further configured to generate at least one of the positioning data continuously or the positioning commands continuously ([0018] a control method of a surgical robot system, the surgical robot system including a slave robot having a robot arm to which a main surgical tool and an auxiliary surgical tool are coupled, and … a controller that predicts a basic motion to be performed by an operator based on the motion data detected via the motion data detector and results of learning a plurality of motions constituting a surgical task, and controls the auxiliary surgical tool so as to correspond to the operator basic motion based on the predicted basic motion.)
Regarding claim 5: Romano discloses: The controller of claim 1,
Romano further discloses: wherein the forward predictive model includes: a neural network base having an input layer configured to input joint variables of an interventional robot representative of the commanded positioning motion of the interventional device, and an output layer configured to output at least one of a translation, a rotation, or a pivoting of the device portion derived from a regression of the joint variables of the interventional robot, wherein the at least one processor is further configured to infer the at least one of the translation, the rotation or the pivoting of the device portion based on the predicted navigated pose of the device portion. ([0034] The architecture 200 is composed of three layers: a physical layer 202, a processing layer 204, and a planning layer 206. The physical layer 202 includes the robotic vehicle 102 (including the propulsion system 104, shown as “Grizzly Robot” in FIG. 2), the sensors 108-112 (e.g., the “RGB Camera” as the sensors 109, 111 and the “Kinect Sensor” as the sensors 108, 110 in FIG. 2), and the manipulator arm 114 (e.g., the “SIA20F Robot” in FIG. 2).
[0035] The processing layer 204 is embodied in the controller 106, and dictates operation of the robotic system 100. The processing layer 204 performs or determines how the robotic system 100 will move or operate to perform various tasks in a safe and/or efficient manner. The operations determined by the processing layer 204 can be referred to as modules. These modules can represent the algorithms or software used by the processing layer 204 to determine how to perform the operations of the robotic system 100, or optionally represent the hardware circuitry of the controller 106 that determines how to perform the operations of the robotic system 100.)
Regarding claim 6: Romano discloses: The controller of claim 1,
Romano further discloses: wherein the control predictive model includes: a neural network base having an input layer configured to input at least one of a translation, a rotation or a pivoting of the device portion, and an output layer configured to output joint variables of the interventional robot derived from a regression of least one of the translation, the rotation, or the pivoting of the device portion, wherein the at least one processor is configured to infer the joint variables of the interventional robot infer the predicted positioning motion of the interventional device. ([0054] The processing layer 204 of the controller 106 can determine the trajectories 240 of the arm 114 to safely and efficiently move the arm 114 toward the component (e.g., brake lever) to be actuated by the arm 114. The trajectories 240 that are determined can include one or more linear trajectories in joint space, one or more linear trajectories in Cartesian space, and/or one or more point-to-point trajectories in joint space.)
Regarding claim 7: Romano discloses: The positioning controller of claim 1,
Romano further discloses: wherein the forward predictive model includes: a neural network base having an input layer configured to input joint velocities of the interventional device representative of the commanded positioning motion of the interventional device, and ([0055] The starting position and target position of the motion of the arm 114 can be defined by the processing layer 204 based on the planned arm movements 238. Using an algorithm such as an artificial potential field algorithm, one or more waypoints for movement of the arm 114 can be determined. These waypoints can be located along lines in six degrees of freedom, but be located along non-linear lines in the Cartesian space. The processing layer 204 can assign velocities to each waypoint depending on the task requirements.) an output layer configured to output at least one of a linear velocity or an angular velocity of the device portion from a regression of the joint velocities of the interventional device, wherein at least one of a linear velocity or an angular velocity of the device portion infers the predicted navigated pose of the interventional device. ([0058] The trajectories 240 that are determined can be defined as one or more sequences of waypoints in the joint space. Each waypoint can include the information of multiple (e.g., seven) joint angles, timing stamps (e.g., the times at which the arm 114 is to be at the various waypoints), and velocities for moving between the waypoints. The joint angles, timing stamps, and velocities are put into a vector of points to define the trajectories 240. The processing layer 204 can use the trajectories 240 to determine control signals 242 that are communicated to the manipulator arm 114 (the other “Motion Control” in FIG. 2). The control signals 242 can be communicated to the motors or other moving components of the arm 114 to direct how the arm 114 is to move.)
Regarding claim 8: Romano discloses: The positioning controller of claim 1,
Romano further discloses: wherein the control predictive model includes: a neural network base having an input layer configured to input at least one of a linear velocity or an angular velocity of the device portion to the target pose and ([0055] The starting position and target position of the motion of the arm 114 can be defined by the processing layer 204 based on the planned arm movements 238. Using an algorithm such as an artificial potential field algorithm, one or more waypoints for movement of the arm 114 can be determined. These waypoints can be located along lines in six degrees of freedom, but be located along non-linear lines in the Cartesian space. The processing layer 204 can assign velocities to each waypoint depending on the task requirements.) an output layer configured to output joint velocities of the interventional device from a regression at least one of a linear velocity or an angular velocity of the device portion to the target pose, wherein the joint velocities of the interventional device infer the predicted positioning motion of the interventional device. ([0058] The trajectories 240 that are determined can be defined as one or more sequences of waypoints in the joint space. Each waypoint can include the information of multiple (e.g., seven) joint angles, timing stamps (e.g., the times at which the arm 114 is to be at the various waypoints), and velocities for moving between the waypoints. The joint angles, timing stamps, and velocities are put into a vector of points to define the trajectories 240. The processing layer 204 can use the trajectories 240 to determine control signals 242 that are communicated to the manipulator arm 114 (the other “Motion Control” in FIG. 2). The control signals 242 can be communicated to the motors or other moving components of the arm 114 to direct how the arm 114 is to move.)
Regarding claim 9: Romano discloses: The positioning controller of claim 1,
Romano further discloses: wherein the forward predictive model includes: a neural network base having an input layer configured to input a preceding sequence of shapes of the interventional device representative of the commanded positioning motion of the interventional device, and an output layer configured to output a succeeding sequence of shapes of the interventional device derived from a time series prediction of the preceding sequence of shapes of the interventional device, wherein the at least one processor is configured to infer the succeeding sequence of shapes of the interventional device based on the predicted navigated pose of the device portion. ([0034] The architecture 200 is composed of three layers: a physical layer 202, a processing layer 204, and a planning layer 206. The physical layer 202 includes the robotic vehicle 102 (including the propulsion system 104, shown as “Grizzly Robot” in FIG. 2), the sensors 108-112 (e.g., the “RGB Camera” as the sensors 109, 111 and the “Kinect Sensor” as the sensors 108, 110 in FIG. 2), and the manipulator arm 114 (e.g., the “SIA20F Robot” in FIG. 2).
[0035] The processing layer 204 is embodied in the controller 106, and dictates operation of the robotic system 100. The processing layer 204 performs or determines how the robotic system 100 will move or operate to perform various tasks in a safe and/or efficient manner. The operations determined by the processing layer 204 can be referred to as modules. These modules can represent the algorithms or software used by the processing layer 204 to determine how to perform the operations of the robotic system 100, or optionally represent the hardware circuitry of the controller 106 that determines how to perform the operations of the robotic system 100.)
Regarding claim 10: Romano discloses:
Romano further discloses: wherein the forward predictive model includes: a neural network base having an input layer configured to input a preceding sequence of shapes of the interventional device representative of the commanded positioning motion of the interventional device, and an output layer configured to output a succeeding shape of the interventional device derived from a time series prediction of the preceding sequence of shapes of the interventional device, wherein the at least one processor is configured to infer the succeeding shape of the interventional device based on the predicted navigated pose of the device portion.
Regarding claim 11: Romano discloses: The positioning controller of claim 2,
Romano further discloses: wherein at least one of: the forward predictive model is further trained on at least one navigation parameter of the interventional device auxiliary to the forward kinematics of the interventional device predictive of the pose of the device portion, wherein the at least one navigation parameter include at least one of a navigation command communicated to the interventional device, an actuation signal communicated to the interventional device, and a navigation force imposed onto the interventional device, or the control predictive model is further trained on the at least one navigation parameter of the interventional device auxiliary to the inverse kinematics of the interventional device predictive of the positioning motion of the interventional device; and ([0040] A state machine can tie the layers 202, 204, 206 together and transfer signals between the navigation module 212 and the perception module 210, and then to the manipulation module 214. If there is an emergency stop signal generated or there is error information reported by one or more of the modules, the controller 106 may responsively trigger safety primitives such as stopping movement of the robotic system 100 to prevent damage to the robotic system 100 and/or surrounding environment.) wherein the at least one processor is further configured to at least one of: (i') apply the forward predictive model to both the commanded positioning motion of the interventional device and the at least one navigation parameter auxiliary to the forward kinematics of the interventional device to predict the navigated pose of the device portion; or (ii') apply the control predictive model to both the target pose of the device portion and the at least one navigation parameter auxiliary to the inverse kinematics of the interventional device to predict the positioning motion of the interventional device. ([0048] The movements and/or sequence of movements determined by the planning layer 206 of the controller 106 may be referred to as movement tasks 226. These movement tasks 226 can dictate the order of different movements, the magnitude (e.g., distance) of the movements, the speed and/or acceleration involved in the movements, etc. The movement tasks 226 can then be assigned to various components of the robotic system 100 (“Task Assignment” in FIG. 2). For example, the planning layer 206 can communicate the movement tasks 226 and the different components that are to perform the movement tasks 226 to the processing layer 204 of the controller 106 as assigned movement tasks 228. The assigned movement tasks 228 can indicate the various movement tasks 226 as well as which component (e.g., the robotic vehicle 102 and/or the manipulator arm 114) is to perform the various movement tasks 226.)
Regarding claim 12: Romano discloses: The positioning controller of claim 1,
Romano further discloses: wherein at least one of: the forward predictive model is configured to further receive at least one auxiliary navigation parameter of the interventional device, and further process it to output the prediction of navigated pose of the device portion, wherein the at least one auxiliary navigation parameter include at least one of an image of the interventional device, shape of the interventional device, strain of the interventional device, twist of the interventional device, or temperature of the interventional device, or the control predictive model is configured to further receive at least one auxiliary navigation parameter of the interventional device, and further process it to output the prediction of positioning motion of the interventional device; and ([0040] A state machine can tie the layers 202, 204, 206 together and transfer signals between the navigation module 212 and the perception module 210, and then to the manipulation module 214. If there is an emergency stop signal generated or there is error information reported by one or more of the modules, the controller 106 may responsively trigger safety primitives such as stopping movement of the robotic system 100 to prevent damage to the robotic system 100 and/or surrounding environment.) wherein the at least one processor is configured to at least one of: (i') apply the forward predictive model to both the commanded positioning motion of the interventional device and at least one auxiliary navigation parameter to render the predicted navigated pose of the device portion; or (ii') apply the control predictive model to both the target pose of the device portion and at least one auxiliary navigation parameter to render the predicted positioning motion of the interventional device. ([0048] The movements and/or sequence of movements determined by the planning layer 206 of the controller 106 may be referred to as movement tasks 226. These movement tasks 226 can dictate the order of different movements, the magnitude (e.g., distance) of the movements, the speed and/or acceleration involved in the movements, etc. The movement tasks 226 can then be assigned to various components of the robotic system 100 (“Task Assignment” in FIG. 2). For example, the planning layer 206 can communicate the movement tasks 226 and the different components that are to perform the movement tasks 226 to the processing layer 204 of the controller 106 as assigned movement tasks 228. The assigned movement tasks 228 can indicate the various movement tasks 226 as well as which component (e.g., the robotic vehicle 102 and/or the manipulator arm 114) is to perform the various movement tasks 226.)
Regarding claim 13: Rejected using the same rationale as claim 1.
Regarding claim 14: Rejected using the same rationale as claim 1.
Regarding claim 15: Rejected using the same rationale as claim 7.
Regarding claim 16: Rejected using the same rationale as claim 8.
Regarding claim 17: Rejected using the same rationale as claim 9.
Regarding claim 18: Rejected using the same rationale as claim 7 and 15.
Regarding claim 19: Rejected using the same rationale as claim 8 and 16.
Regarding claim 20: Rejected using the same rationale as claim 9 and 17.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant' s disclosure or directed to the state of art is listed on the enclosed PTO-892.
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/ATTICUS A CAMERON/ /JASON HOLLOWAY/ Primary Examiner, Art Unit 3658 Examiner, Art Unit 3658A
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