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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Dec. 13, 2023.
Claims 1-20 are pending in the case. Claims 1, 12 and 19 are independent claims.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Rucker et al. (hereinafter Rucker) U.S. Patent Publication No. 2013/0131868 in view of Terjek (hereinafter Terjek) U.S. Patent Publication No. 2023/0120256.
With respect to independent claim 1, Rucker teaches a method for robotic control of a tendon driven continuum mechanism (see e.g., Fig. 1 Para [28] - “FIG. 1 is an illustration of the results of simulations of a continuum robot with single, straight, tensioned tendons with in-plane and out-of-plane forces applied at the tip. These illustrate the difference between the model proposed in this paper which includes distributed tendon wrenches, and the commonly used point moment approximation. ”), the method comprising:
receiving a user command to operate the tendon driven continuum mechanism (see e.g., Para [106][107][110]-“such robots would be useful for carry out procedures in confined spaces, as the increased dexterity would allow the user to maneuver the tip around obstructions in such spaces. For example, such robots could be used to reduce the invasiveness of some existing surgical procedures which currently cannot be performed using conventional robotic tools.”” To enable user interaction with the computing device 1500, an input device 1590 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 1570 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with the computing device 1500. “); and
operating a motor with the motor control, the operating of the motor operating the tendon driven continuum mechanism (see e.g., Para [8][104][105] - “The robot also includes an actuator for applying a tension to the at least one tendon; and a processing element for using a system of equations for controlling a shape of the elastic member and the tension.”).
Rucker does not expressly show predicting a motor control by an artificial intelligence, the artificial intelligence predicting in response to input of the user command. However, Terjek teaches similar feature (see e.g., Fig. 4 Claim 11 and Para [60]-[70]-“an artificial neural network is trained, using training data sets (x.sub.1 to x.sub.t+h), to predict future sequential time series (x.sub.t+1 to x.sub.t+h) in time steps (t+1 to t+h) as a function of past sequential time series (x.sub.1 to x.sub.t) to control an engineering system, ”). Both Rucker and Terjek are directed to robotic systems (see e.g., Terjet Para [48] – “For the purposes of the present invention, the engineering system may comprise, inter alia, a robot, a vehicle, a tool or a machine tool.”). Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Rucker and Terjek in front of them to modify the system of Rucker to include the above feature. The motivation to combine Rucker and Terjek comes from Terjek. Terjek discloses the motivation to apply AI to create a prediction ability for robotic system (see e.g., Fig. 4 Claim 11 and Para [48] [60]-[70]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 2, the modified Rucker teaches operating the motor with the user command wherein the motor control is added in addition to the user command as a fine tuning of the motor operation (see e.g., Para [104]-[106] -“the actuator/sensor 1410 can generate signals indicative of a current tension on the tendon 1408. This signal can be recited by the control system 1412. The control system 1412 can then use the equations described above, particularly the governing equations at (17), to estimate a current or resulting shape of the member 1404. In particular, the governing equations at (17) can be solved to extract the shape of the member 1404. Additional sensors 1414, such as video sensors, can also be coupled to the control system 1412 to allow verification of this estimated shape. In a second mode of operation, the control system 1412 can also use the equations described above, particularly the governing equations at (17), to determine an amount of tension required for the member 1404 to achieve a desired shape. Thereafter, the control system 1412 can cause the actuator/sensor 1410 to adjust the tension on the tendon 1408.” Also see Terjek Para [75]-78][91] – AI generates further adjustment.).
With respect to dependent claim 3, the modified Rucker teaches operating the motor with the user command comprises operating the motor with the user command input to a kinematics and hysteresis compensator (see e.g., Para [49][139] - “Frictional forces are expected to increase as the curvature of the robot increases due to larger normal forces, but the assumption of zero friction is valid if low friction materials are used”” the iterative model equations described above can be iteratively solved to determine the external forces and moments (f.sub.e and l.sub.e) which result in the model-predicted shape that is close to the actual sensed shape. The resulting loads based on the model can then be used as an estimate of the loads acting on the elastic member.”).
With respect to dependent claim 4, the modified Rucker teaches predicting comprises predicting a change as the motor control to account for friction caused by a current curvature and placement of the tendon driven continuum mechanism (see e.g., Para [49]-[58] – “A convenient way to mathematically describe the tendon routing path is to define the tendon location within the robot cross section as a function of the reference parameter s. “).
With respect to dependent claim 5, the modified Rucker teaches predicting comprises predicting in response to current draw of the motor and distance from an encoder of the motor input to the artificial intelligence with the user command (see e.g., Para [86][104]-[106]- Rucker does not expressly show an encoder. However, an encoder is a software module that performs the calculation. Rucker expressly shows such calculation.).
With respect to dependent claim 6, the modified Rucker teaches predicting comprises predicting in response to a temporal sequence of motor position and velocity input to the artificial intelligence with the user command (see e.g., Terjek Para [2][3][20]-[21][63]-[70] – “Behavior prediction may be associated with the more general problem of predicting sequential time series, a problem which may in turn be considered a case of generative modeling. Generative modeling relates to the approximation of probability distributions, e.g. to learn a probability distribution in data-controlled manner with the assistance of artificial neural networks (ANNs)”).
With respect to dependent claim 7, the modified Rucker teaches predicting comprises predicting future states by the artificial intelligence in response to past states and a current state, the artificial intelligence comprising a recurrent neural network, the past, current, and future states comprising tip position of the tendon driven continuum mechanism (see e.g., Terjek Para [26]-[39] – “The training method is also suitable for training a recurrent, artificial neural network, in particular for a Virtual Recurrent Neural Network (VRNN) according to the related art outlined above.” Tip position is not expressly stated. However, it would have been obvious to include the tip position because Terjek is directed to state/position prediction in general).
With respect to dependent claim 8, the modified Rucker teaches predicting comprises predicting by the artificial intelligence comprising a recurrent neural network outputting a sequence of the motor controls as motor motions minimizing a cost under constraints (see e.g., Terjek Para [33]-[39] – maximizing ELBO translates to minimizing cost).
With respect to dependent claim 9, the modified Rucker teaches outputting environment information for the tendon driven continuum mechanism based on a magnitude of the motor control and/or a probability output by the artificial intelligence for the motor control (see e.g., Terjek Para [10][33]-[39]).
With respect to dependent claim 10, the modified Rucker teaches outputting safety information for the tendon driven continuum mechanism based on a magnitude of the motor control and/or a probability output by the artificial intelligence for the motor control (see e.g., Terjek Para [10][33]-[39]).
With respect to dependent claim 11, the modified Rucker teaches predicting a future boundary contact by the tendon driven continuum mechanism based on a magnitude of the motor control and/or a probability output by the artificial intelligence for the motor control (see e.g., Terjek Para [33]-[39]).
Claims 12-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jolaeimoghaddam (hereinafter Jol) U.S. Patent Publication No. 2022/0218418 in view of in view of Terjek.
With respect to independent claim 12, Jol teaches a control system for a steerable catheter, the control system comprising: a robotic manipulator for operation of the steerable catheter, the robotic manipulator comprising an actuator configured to steer the steerable catheter (see e.g. Fig. 1 Para [55]-[63][190] – “The control system was implemented in parallel in two Arduino Uno microprocessors hooked to four rotational encoders for the tendons length feedbacks and stepper motor driving. Also, the neural network and image acquisition systems (used for documentation) were implemented in the user interface in .Net C# environment. Also, a six-DoF ATI Mini40 force sensor was used as the benchmark for the comparison. Moreover, a linear motor was used to change the height of the phantom tissue to simulate the motion of the heart. Two markers, red and blue, were used to measure the relative distance of the catheter tip (as a measure of indentation) in real-time.”); and a control processor configured to control the actuator (see e.g., Para [62] – “The catheter controller comprises a processing unit 201 for executing instructions or programs stored in memory 203 or received from the console 109. The processing unit 201 can be a microprocessor or any processor device capable of executing the operations of the present technology, such a processor device is well known to those skilled in the art.“).
Jol does not expressly show the control using application of a machine-learned model configured to predict a position and/or velocity of the actuator to implement a user command given an environment of the steerable catheter. However, Terjek teaches similar feature (see e.g., Fig. 4 Claim 11 and Para [60]-[70]-“an artificial neural network is trained, using training data sets (x.sub.1 to x.sub.t+h), to predict future sequential time series (x.sub.t+1 to x.sub.t+h) in time steps (t+1 to t+h) as a function of past sequential time series (x.sub.1 to x.sub.t) to control an engineering system, ”). Both Jol and Terjek are directed to robotic systems (see e.g., Terjet Para [48] – “For the purposes of the present invention, the engineering system may comprise, inter alia, a robot, a vehicle, a tool or a machine tool.”). Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Jol and Terjek in front of them to modify the system of Jol to include the above feature. The motivation to combine Jol and Terjek comes from Terjek. Terjek discloses the motivation to apply AI to create a prediction ability for robotic system (see e.g., Fig. 4 Claim 11 and Para [48] [60]-[70]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 13, the modified Jol teaches the steerable catheter comprises an intracardiac echocardiography catheter with a tendon connected from a tip to the actuator (see e.g., Fig. 1 Para [16]-[17][55]-[57]).
With respect to dependent claim 14, the modified Jol teaches the steerable catheter has a tip, and wherein the machine-learned model comprises a recurrent neural network configured to predict in response to input of a sequence of past actuator states and/or past positions of the tip, the prediction being output of a future sequence of the positions and velocities of the actuator (see e.g., Terjek Para [2][3][20]-[21][63]-[70]).
With respect to dependent claim 16, the modified Jol teaches the control processor is configured to output environment information about the environment of the steerable catheter based on a magnitude of the position and/or velocity of the actuator and/or a probability output by the machine-learned model for the position and/or velocity (see e.g., Terjek Fig. 4 Claim 11 and Para [60]-[70]).
With respect to dependent claim 17, the modified Jol teaches the control processor is configured to output safety information of the steerable catheter based on a magnitude of the position and/or velocity of the actuator and/or a probability output by the machine-learned model for the position and/or velocity (see e.g., Terjek Fig. 4 Claim 11 and Para [60]-[70]).
With respect to dependent claim 18, the modified Jol teaches the control processor is configured to predict a future boundary contact by the steerable catheter based on a magnitude of the position and/or velocity of the actuator and/or a probability output by the machine-learned model for the position and/or velocity (see e.g., Terjek Para [33]-[39]).
With respect to independent claim 19, the modified Jol teaches a method for predictive control of a robotic manipulator for a catheter (see e.g. Fig. 1 Para [55]-[63][190] – “The control system was implemented in parallel in two Arduino Uno microprocessors hooked to four rotational encoders for the tendons length feedbacks and stepper motor driving. Also, the neural network and image acquisition systems (used for documentation) were implemented in the user interface in .Net C# environment. Also, a six-DoF ATI Mini40 force sensor was used as the benchmark for the comparison. Moreover, a linear motor was used to change the height of the phantom tissue to simulate the motion of the heart. Two markers, red and blue, were used to measure the relative distance of the catheter tip (as a measure of indentation) in real-time.”), the method comprising: predicting a sequence of future states of operation of a motor of the robotic manipulator based on a sequence of past states of the motor operation by a machine-learned model; and controlling the motor of the robotic manipulator based on at least one of the predicted future states (see e.g., Terjek Fig. 4 Claim 11 and Para [60]-[70] The motivation to combine is discussed above with respect to claim 12).
Claims 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jol in view of in view of Terjek and further in view of Rucker.
With respect to dependent claim 15, Jol-Terjek does not expressly show the control processor is configured to control the actuator with a kinematics and hysteresis model in response to the user command, and wherein the machine-learned model outputs a fine-tuning of the control by the kinematics and hysteresis model to account for the environment. However, Rucker teaches similar feature (see e.g., Para [49][139] - “Frictional forces are expected to increase as the curvature of the robot increases due to larger normal forces, but the assumption of zero friction is valid if low friction materials are used”” the iterative model equations described above can be iteratively solved to determine the external forces and moments (f.sub.e and l.sub.e) which result in the model-predicted shape that is close to the actual sensed shape. The resulting loads based on the model can then be used as an estimate of the loads acting on the elastic member.”) Both Jol and Rucker are directed to tendon driven robotic systems. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Jol and Rucker in front of them to modify the system of Jol to include the above feature. The motivation to combine Jol and Rucker comes from Rucker. Rucker discloses the motivation to use kinematics and hysteresis model to provide improved performance (see e.g., Para [139]).
With respect to dependent claim 20, the modified Jol teaches controlling comprises controlling by a kinematics and/or hysteresis model wherein the at least one of the predicted future states fine-tunes the control by the kinematics and/or hysteresis model, the fine-tuning accounting for friction of a tendon of the catheter due to curvature of the catheter in a patient (see e.g., Rucker Para [49][139] - “Frictional forces are expected to increase as the curvature of the robot increases due to larger normal forces, but the assumption of zero friction is valid if low friction materials are used”” the iterative model equations described above can be iteratively solved to determine the external forces and moments (f.sub.e and l.sub.e) which result in the model-predicted shape that is close to the actual sensed shape. The resulting loads based on the model can then be used as an estimate of the loads acting on the elastic member.” The motivation to combine is discussed above).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/ Primary Examiner, Art Unit 2141
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