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 is the first Office action on the merits. Claims 11/18/2024 are currently pending and addressed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/27/2024 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 05/20/2025 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 7 is objected to because of the following informalities:
“in the X, Y and Z-plane” appears to be a typographical error and should read “in the X, Y and Z-planes”
Claim 12 is objected to because of the following informalities:
“wherein tracking markers are” appears to be a typographical error and should read “wherein the tracking markers are”
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 recites the limitation "the cameras" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the sensor tracked backbone curve" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the sensing fibre backbone curve" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the X, Y and Z plane" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the closest match" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the camera measured curve" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the root mean square error (RMSE)" in 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the two curves" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the movement control programs" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the cameras" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Multiple computers are introduced without clarification as to whether this is the same computer or a new computer as previously introduced. Clarification on the record is earnestly solicited.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-6, and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polido et al. (US 11912513 B1), hereinafter Polido in view of Larkin et al. (US 20220047341 A1), hereinafter Larkin and Ha et al. (Shape Sensing of Flexible Robots Based on Deep Learning), hereinafter Ha.
Regarding claim 1, Polido teaches:
1. A method of determining the position and shape of … having at least one tracking marker (Column 3 Lines 34-51, "Embodiments of the disclosure include methods and systems for robotic system camera calibration and localization using robot-mounted registered patterns that may improve processing and fulfillment of orders, or other object aggregation tasks. Certain embodiments include robotic manipulators with picking assemblies that use end of arm tool-mounted registered patterns, which may be in the form of calibration plates, for robotic system camera calibration and localization. Although described in example herein of a calibration “plate,” calibration patterns may be in different forms, such as three-dimensional markers coupled directly on an end of arm tool housing, painted markers or other forms of two-dimensional markers applied directly on an end of arm tool housing, thin sheets of material adhered to an end of arm tool housing, and so forth. Some embodiments may be disposed on end of arm tool housings without requiring flat surfaces on which to mount the calibration pattern.") and … , the method comprising:
moving the flexible arm robot through a series of movements; (Column 11 Lines 23-50, "At block 410 of the process flow 400, computer-executable instructions stored on a memory of a device, such as a remote server or a user device, may be executed to cause a robotic manipulator to move to a first predetermined position. For example, a control module executed at a controller or computer system associated with a robotic system may cause a robotic manipulator to move to a first predetermined position. To calibrate the robotic manipulator, the controller may cause the robotic manipulator to move through a series of one or more predetermined positions or waypoints, at which images of a calibration plate coupled to the robotic manipulator may be captured. For example, the robotic manipulator may move the calibration plate through a number of predetermined positions and orientations (where “pose” refers to both a position and orientation), and may optionally stop briefly at the different positions to allow time for images of the calibration plate to be captured. The images may be used to determine an actual position of the robotic manipulator, and may be compared to position data and orientation data associated with the pose of the robotic manipulator at the time the image was captured to determine whether there are any discrepancies. If so, the robotic manipulator may need to undergo calibration via one or more calibration adjustments. Accordingly, the controller may cause the robotic manipulator to move to a first predetermined position and orientation, which may be in a two-dimensional or three-dimensional space, as discussed with respect to FIG. 5A.")
tracking the movements of the flexible arm robot using at least two sensors (Column 6 Lines 33-54, "The robotic system 200 may include one or more cameras, such as a first camera 250, a second camera 260, a third camera 270, and so forth. Any number of cameras may be used. The cameras may be oriented at different angles, such as downwards towards the picking assembly 220, upwards towards the picking assembly 220, and/or other orientations. As depicted at a second point in time 280, the robotic manipulator 210 may move the picking assembly 220 from a first location to a second location so as to retrieve an item, transport an item, release an item, calibrate the robotic manipulator 210, and so forth.") and recording positional data of at least a section of the flexible arm robot as a ground truth (Column 11 Lines 23-50, "At block 410 of the process flow 400, computer-executable instructions stored on a memory of a device, such as a remote server or a user device, may be executed to cause a robotic manipulator to move to a first predetermined position. For example, a control module executed at a controller or computer system associated with a robotic system may cause a robotic manipulator to move to a first predetermined position. To calibrate the robotic manipulator, the controller may cause the robotic manipulator to move through a series of one or more predetermined positions or waypoints, at which images of a calibration plate coupled to the robotic manipulator may be captured. For example, the robotic manipulator may move the calibration plate through a number of predetermined positions and orientations (where “pose” refers to both a position and orientation), and may optionally stop briefly at the different positions to allow time for images of the calibration plate to be captured. The images may be used to determine an actual position of the robotic manipulator, and may be compared to position data and orientation data associated with the pose of the robotic manipulator at the time the image was captured to determine whether there are any discrepancies. If so, the robotic manipulator may need to undergo calibration via one or more calibration adjustments. Accordingly, the controller may cause the robotic manipulator to move to a first predetermined position and orientation, which may be in a two-dimensional or three-dimensional space, as discussed with respect to FIG. 5A.") …
and applying the calibration function to software used to control the movement of the flexible arm robot; (Column 11 Lines 4-22, "In one example embodiment, the process flow 400 may be executed to determine calibration adjustments for a robotic arm and/or other components of a robotic system, such as camera calibration. Calibration adjustments account for differences between where the robotic arm is supposed to be positioned, and where the robotic arm is actually positioned. Calibration adjustments for cameras may include color correction and other features. Some embodiments may use images of one or more calibration plates rigidly mounted on an end of arm tool coupled to the robotic arm in order to determine calibration adjustments. Some embodiments may capture images during a dedicated calibration session (e.g., when the robotic arm is taken offline for normal tasks, etc.), whereas other embodiments may capture images during actual usage of the robotic arm, for on-the-move calibration without a dedicated calibration process. The process flow 400 may be used to calibrate the robotic manipulator and/or positioning of a picking assembly coupled to the robotic manipulator.")
the method characterised in that the at least two sensors are angularly and spatially distanced from each other. (Column 6 Lines 33-54, "The robotic system 200 may include one or more cameras, such as a first camera 250, a second camera 260, a third camera 270, and so forth. Any number of cameras may be used. The cameras may be oriented at different angles, such as downwards towards the picking assembly 220, upwards towards the picking assembly 220, and/or other orientations. As depicted at a second point in time 280, the robotic manipulator 210 may move the picking assembly 220 from a first location to a second location so as to retrieve an item, transport an item, release an item, calibrate the robotic manipulator 210, and so forth.")
Polido does not specifically teach the robotic arm being a flexible arm robot, having Bragg reflectors integrated, or using a machine learning algorithm to determine the calibration adjustments to make. However, Larkin, in the same field of endeavor of robotics, teaches:
… a flexible arm robot … having at least one distributed Bragg reflector integrated into the arm (Paragraph 0043, "In accordance with embodiments of the present invention, a surgical instrument is provided. This surgical instrument includes an elongate body having a positionable distal end and at least one bendable region, such as a joint region or a flexible region. An optical fiber bend sensor comprising one or more optical fibers is provided in the bendable region of the body. Each of these optical fibers includes a Fiber Bragg Grating, preferably a collinear array of Fiber Bragg Gratings. A strain sensor system comprising a light source and a light detector is used to measure strain in the optical fibers in order to determine a position and shape of the body. This shape and position information can be used to assist in controlling movement of the robotic manipulator and/or surgical instrument. This position information may include both translational and rotational position.") … and interrogating the distributed Bragg reflector during the series of movements; (Paragraph 0053, "There are a variety of ways of multiplexing the Fiber Bragg Gratings so that a single fiber core can carry many sensors and the readings of each sensor can be distinguished. In some embodiments, Optical Frequency Domain Reflectometry (OFDR) may be used in which the Fiber Bragg Gratings, all with the same grating period, are placed along each of the cores, and each core is terminated at the proximal end with a partially reflecting mirror. The Fiber Bragg Gratings are placed in such a way that the distance from each grating to the reflector is unique, which causes the reflection spectrum of each Fiber Bragg Grating to be modulated with a unique modulation frequency, thereby allowing the individual reflection spectra to be determined. In addition, OFDR may be used to interrogate the array of Fiber Bragg Gratings with sufficiently low delays such that that the bending data can be used as a feedback signal in a real-time motion control loop.") …
However, Ha, in the same field of endeavor of robotics, teaches:
… inputting the data from the interrogation of the distributed Bragg reflector and the cameras into a machine learning algorithm; using the output of the machine learning algorithm to determine a calibration function for the position of the distributed Bragg reflector within the flexible arm robot; … (Section IV A. "In this article, a multilayer perceptron (MLP) is used to auto-calibrate the shape sensing functionality of the robot. An MLP is a class of feedforward ANNs that consists of an input layer, several hidden layers, and an output layer [28]. Except for the neurons in the input layer, each neuron adopts a nonlinear activation function. These nonlinear activation functions tum the MLP into a nonlinear perception, thus distinguishing itself from linear approaches, such as linear regression. Thanks to a large number of neurons and the possibility to make use of multiple types of nonlinear activation functions. MLPS can model complex nonlinear behavior [29]. The training of the MLP is based on a supervised learning algorithm called back propagation [30]. The training procedure requires sufficient data containing various, patterns. The MLPs developed here relate the measured wavelength shifts from FBGs directly to the curvature and angle of the catheter's bending plane without the explicit knowledge of the correspondence between the fiber's position and the catheter centerline or explicit knowledge of the characteristic parameters of the fiber.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and operating methods as taught by Polido with the flexible arm and Bragg reflectors as taught by Larkin and with the machine learning model as taught by Ha. Incorporating these elements would ensure an accurate calibration adjustment may be determined and a versatile robotic arm would be able to operate efficiently and effectively without the need for operators to perform tedious calibrating operations.
Regarding claim 2, where all the limitations of claim 1 are discussed above, Polido further teaches:
2. The method according to claim 1, wherein at least two tracking markers are positioned on the flexible arm. (Column 3 Lines 34-51, "Embodiments of the disclosure include methods and systems for robotic system camera calibration and localization using robot-mounted registered patterns that may improve processing and fulfillment of orders, or other object aggregation tasks. Certain embodiments include robotic manipulators with picking assemblies that use end of arm tool-mounted registered patterns, which may be in the form of calibration plates, for robotic system camera calibration and localization. Although described in example herein of a calibration “plate,” calibration patterns may be in different forms, such as three-dimensional markers coupled directly on an end of arm tool housing, painted markers or other forms of two-dimensional markers applied directly on an end of arm tool housing, thin sheets of material adhered to an end of arm tool housing, and so forth. Some embodiments may be disposed on end of arm tool housings without requiring flat surfaces on which to mount the calibration pattern.")
Regarding claim 4, where all the limitations of claim 1 are discussed above, Polido further teaches:
4. The method according to claim 1, wherein the movements that are tracked are a series of movements input by an operator. (Column 11 Lines 4-22, "In one example embodiment, the process flow 400 may be executed to determine calibration adjustments for a robotic arm and/or other components of a robotic system, such as camera calibration. Calibration adjustments account for differences between where the robotic arm is supposed to be positioned, and where the robotic arm is actually positioned. Calibration adjustments for cameras may include color correction and other features. Some embodiments may use images of one or more calibration plates rigidly mounted on an end of arm tool coupled to the robotic arm in order to determine calibration adjustments. Some embodiments may capture images during a dedicated calibration session (e.g., when the robotic arm is taken offline for normal tasks, etc.), whereas other embodiments may capture images during actual usage of the robotic arm, for on-the-move calibration without a dedicated calibration process. The process flow 400 may be used to calibrate the robotic manipulator and/or positioning of a picking assembly coupled to the robotic manipulator.")
Regarding claim 5, where all the limitations of claim 1 are discussed above, Polido further teaches:
5. The method according to claim 1, wherein the movements are a pre-programmed series of movements programmed into the software used to control the flexible arm. (Column 11 Lines 4-22, "In one example embodiment, the process flow 400 may be executed to determine calibration adjustments for a robotic arm and/or other components of a robotic system, such as camera calibration. Calibration adjustments account for differences between where the robotic arm is supposed to be positioned, and where the robotic arm is actually positioned. Calibration adjustments for cameras may include color correction and other features. Some embodiments may use images of one or more calibration plates rigidly mounted on an end of arm tool coupled to the robotic arm in order to determine calibration adjustments. Some embodiments may capture images during a dedicated calibration session (e.g., when the robotic arm is taken offline for normal tasks, etc.), whereas other embodiments may capture images during actual usage of the robotic arm, for on-the-move calibration without a dedicated calibration process. The process flow 400 may be used to calibrate the robotic manipulator and/or positioning of a picking assembly coupled to the robotic manipulator.")
Regarding claim 6, where all the limitations of claim 1 are discussed above, Polido does not specifically teach a machine learning model being a neural network or non-linear regression model. However, Ha, in the same field of endeavor of robotics, teaches:
6. The method according to claim 1, wherein the machine learning algorithm may be a neural network or a non-linear regression model. (Section IV A. "In this article, a multilayer perceptron (MLP) is used to auto-calibrate the shape sensing functionality of the robot. An MLP is a class of feedforward ANNs that consists of an input layer, several hidden layers, and an output layer [28]. Except for the neurons in the input layer, each neuron adopts a nonlinear activation function. These nonlinear activation functions tum the MLP into a nonlinear perception, thus distinguishing itself from linear approaches, such as linear regression. Thanks to a large number of neurons and the possibility to make use of multiple types of nonlinear activation functions. MLPS can model complex nonlinear behavior [29]. The training of the MLP is based on a supervised learning algorithm called back propagation [30]. The training procedure requires sufficient data containing various, patterns. The MLPs developed here relate the measured wavelength shifts from FBGs directly to the curvature and angle of the catheter's bending plane without the explicit knowledge of the correspondence between the fiber's position and the catheter centerline or explicit knowledge of the characteristic parameters of the fiber.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and operating methods as taught by Polido with the machine learning model as taught by Ha. Incorporating these elements would ensure an accurate calibration adjustment may be determined and the robotic arm would be able to operate efficiently and effectively without the need for operators to perform tedious calibrating operations.
Regarding claim 9, where all the limitations of claim 1 are discussed above, Polido further teaches:
9. The method according to claim 1, wherein the … is done once the movement control programs have been completed. (Column 10 Line 59 - Column 11 Line 22, "FIG. 4 depicts an example process flow 400 for camera calibration and localization using robot-mounted registered patterns in accordance with one or more example embodiments of the disclosure. While example embodiments of the disclosure may be described in the context of robotic arm calibration, it should be appreciated that the disclosure is more broadly applicable to any type of robotic manipulator. Some or all of the blocks of the process flows in this disclosure may be performed in a distributed manner across any number of devices. The operations of the process flow 400 may be optional and may be performed in a different order.
In one example embodiment, the process flow 400 may be executed to determine calibration adjustments for a robotic arm and/or other components of a robotic system, such as camera calibration. Calibration adjustments account for differences between where the robotic arm is supposed to be positioned, and where the robotic arm is actually positioned. Calibration adjustments for cameras may include color correction and other features. Some embodiments may use images of one or more calibration plates rigidly mounted on an end of arm tool coupled to the robotic arm in order to determine calibration adjustments. Some embodiments may capture images during a dedicated calibration session (e.g., when the robotic arm is taken offline for normal tasks, etc.), whereas other embodiments may capture images during actual usage of the robotic arm, for on-the-move calibration without a dedicated calibration process. The process flow 400 may be used to calibrate the robotic manipulator and/or positioning of a picking assembly coupled to the robotic manipulator.")
Polido does not specifically teach a machine learning model. However, Ha, in the same field of endeavor of robotics, teaches:
… inputting of the data into the machine learning algorithm (Section IV A. "In this article, a multilayer perceptron (MLP) is used to auto-calibrate the shape sensing functionality of the robot. An MLP is a class of feedforward ANNs that consists of an input layer, several hidden layers, and an output layer [28]. Except for the neurons in the input layer, each neuron adopts a nonlinear activation function. These nonlinear activation functions tum the MLP into a nonlinear perception, thus distinguishing itself from linear approaches, such as linear regression. Thanks to a large number of neurons and the possibility to make use of multiple types of nonlinear activation functions. MLPS can model complex nonlinear behavior [29]. The training of the MLP is based on a supervised learning algorithm called back propagation [30]. The training procedure requires sufficient data containing various, patterns. The MLPs developed here relate the measured wavelength shifts from FBGs directly to the curvature and angle of the catheter's bending plane without the explicit knowledge of the correspondence between the fiber's position and the catheter centerline or explicit knowledge of the characteristic parameters of the fiber.") …
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and operating methods as taught by Polido with the machine learning model as taught by Ha. Incorporating these elements would ensure an accurate calibration adjustment may be determined and the robotic arm would be able to operate efficiently and effectively without the need for operators to perform tedious calibrating operations.
Regarding claim 10, where all the limitations of claim 1 are discussed above, Polido further teaches:
10. The method according to claim 1, wherein the … is done live as the movements performed. (Column 10 Line 59 - Column 11 Line 22, "FIG. 4 depicts an example process flow 400 for camera calibration and localization using robot-mounted registered patterns in accordance with one or more example embodiments of the disclosure. While example embodiments of the disclosure may be described in the context of robotic arm calibration, it should be appreciated that the disclosure is more broadly applicable to any type of robotic manipulator. Some or all of the blocks of the process flows in this disclosure may be performed in a distributed manner across any number of devices. The operations of the process flow 400 may be optional and may be performed in a different order.
In one example embodiment, the process flow 400 may be executed to determine calibration adjustments for a robotic arm and/or other components of a robotic system, such as camera calibration. Calibration adjustments account for differences between where the robotic arm is supposed to be positioned, and where the robotic arm is actually positioned. Calibration adjustments for cameras may include color correction and other features. Some embodiments may use images of one or more calibration plates rigidly mounted on an end of arm tool coupled to the robotic arm in order to determine calibration adjustments. Some embodiments may capture images during a dedicated calibration session (e.g., when the robotic arm is taken offline for normal tasks, etc.), whereas other embodiments may capture images during actual usage of the robotic arm, for on-the-move calibration without a dedicated calibration process. The process flow 400 may be used to calibrate the robotic manipulator and/or positioning of a picking assembly coupled to the robotic manipulator.")
Polido does not specifically teach a machine learning model. However, Ha, in the same field of endeavor of robotics, teaches:
… inputting of the data into the machine learning algorithm (Section IV A. "In this article, a multilayer perceptron (MLP) is used to auto-calibrate the shape sensing functionality of the robot. An MLP is a class of feedforward ANNs that consists of an input layer, several hidden layers, and an output layer [28]. Except for the neurons in the input layer, each neuron adopts a nonlinear activation function. These nonlinear activation functions tum the MLP into a nonlinear perception, thus distinguishing itself from linear approaches, such as linear regression. Thanks to a large number of neurons and the possibility to make use of multiple types of nonlinear activation functions. MLPS can model complex nonlinear behavior [29]. The training of the MLP is based on a supervised learning algorithm called back propagation [30]. The training procedure requires sufficient data containing various, patterns. The MLPs developed here relate the measured wavelength shifts from FBGs directly to the curvature and angle of the catheter's bending plane without the explicit knowledge of the correspondence between the fiber's position and the catheter centerline or explicit knowledge of the characteristic parameters of the fiber.") …
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and operating methods as taught by Polido with the machine learning model as taught by Ha. Incorporating these elements would ensure an accurate calibration adjustment may be determined and the robotic arm would be able to operate efficiently and effectively without the need for operators to perform tedious calibrating operations.
Regarding claim 11, Polido further teaches:
11. A system for accurately determining the shape of a flexible arm robot, the system comprising:
… robotic arm being coupled to an actuator pack, (Column 9 Lines 42-57, "As depicted in the second perspective view 350, the picking assembly may include a first piston subassembly that has a first air cylinder 356, a first sliding rail 354 that slides outwards of the housing 330 relative to the first air cylinder 356, and a first suction cup. The first piston subassembly may be configured to independently actuate from a retracted position to an extended position. The picking assembly 300 may include a second piston subassembly that has a second air cylinder 360, a second sliding rail 358 that slides relative to the second air cylinder 360, and a second suction cup, where the second piston subassembly may also be configured to independently actuate from the retracted position to the extended position. Any number of piston subassemblies may be included. The respective air chambers may be fixed relative to the suction cups 340 and may be load bearing components.") which in turn is connected to a computer with software for controlling the robot, (Column 18 Lines 18-39, "In an illustrative configuration, the computer system(s) 800 may include one or more processors (processor(s)) 802, one or more memory devices 804 (also referred to herein as memory 804), one or more input/output (I/O) interface(s) 806, one or more network interface(s) 808, one or more sensor(s) or sensor interface(s) 810, one or more transceiver(s) 812, one or more optional display(s) 814, one or more optional microphone(s) 816, and data storage 820. The computer system(s) 800 may further include one or more bus(es) 818 that functionally couple various components of the computer system(s) 800. The computer system(s) 800 may further include one or more antenna(s) 830 that may include, without limitation, a cellular antenna for transmitting or receiving signals to/from a cellular network infrastructure, an antenna for transmitting or receiving Wi-Fi signals to/from an access point (AP), a Global Navigation Satellite System (GNSS) antenna for receiving GNSS signals from a GNSS satellite, a Bluetooth antenna for transmitting or receiving Bluetooth signals, a Near Field Communication (NFC) antenna for transmitting or receiving NFC signals, and so forth. These various components will be described in more detail hereinafter.") … robot has at least two tracking markers positioned on an outside surface thereof; (Column 3 Lines 34-51, "Embodiments of the disclosure include methods and systems for robotic system camera calibration and localization using robot-mounted registered patterns that may improve processing and fulfillment of orders, or other object aggregation tasks. Certain embodiments include robotic manipulators with picking assemblies that use end of arm tool-mounted registered patterns, which may be in the form of calibration plates, for robotic system camera calibration and localization. Although described in example herein of a calibration “plate,” calibration patterns may be in different forms, such as three-dimensional markers coupled directly on an end of arm tool housing, painted markers or other forms of two-dimensional markers applied directly on an end of arm tool housing, thin sheets of material adhered to an end of arm tool housing, and so forth. Some embodiments may be disposed on end of arm tool housings without requiring flat surfaces on which to mount the calibration pattern.")
at least two sensors used to track and record the movement and deformation of the flexible arm robot, (Column 6 Lines 33-54, "The robotic system 200 may include one or more cameras, such as a first camera 250, a second camera 260, a third camera 270, and so forth. Any number of cameras may be used. The cameras may be oriented at different angles, such as downwards towards the picking assembly 220, upwards towards the picking assembly 220, and/or other orientations. As depicted at a second point in time 280, the robotic manipulator 210 may move the picking assembly 220 from a first location to a second location so as to retrieve an item, transport an item, release an item, calibrate the robotic manipulator 210, and so forth.") and a computer system connected to the interrogator and the cameras, (Column 18 Lines 18-39, "In an illustrative configuration, the computer system(s) 800 may include one or more processors (processor(s)) 802, one or more memory devices 804 (also referred to herein as memory 804), one or more input/output (I/O) interface(s) 806, one or more network interface(s) 808, one or more sensor(s) or sensor interface(s) 810, one or more transceiver(s) 812, one or more optional display(s) 814, one or more optional microphone(s) 816, and data storage 820. The computer system(s) 800 may further include one or more bus(es) 818 that functionally couple various components of the computer system(s) 800. The computer system(s) 800 may further include one or more antenna(s) 830 that may include, without limitation, a cellular antenna for transmitting or receiving signals to/from a cellular network infrastructure, an antenna for transmitting or receiving Wi-Fi signals to/from an access point (AP), a Global Navigation Satellite System (GNSS) antenna for receiving GNSS signals from a GNSS satellite, a Bluetooth antenna for transmitting or receiving Bluetooth signals, a Near Field Communication (NFC) antenna for transmitting or receiving NFC signals, and so forth. These various components will be described in more detail hereinafter.") …
the system characterised in that the at least two sensors are angularly and spatially distanced from each other. (Column 6 Lines 33-54, "The robotic system 200 may include one or more cameras, such as a first camera 250, a second camera 260, a third camera 270, and so forth. Any number of cameras may be used. The cameras may be oriented at different angles, such as downwards towards the picking assembly 220, upwards towards the picking assembly 220, and/or other orientations. As depicted at a second point in time 280, the robotic manipulator 210 may move the picking assembly 220 from a first location to a second location so as to retrieve an item, transport an item, release an item, calibrate the robotic manipulator 210, and so forth.")
Polido does not specifically teach the robotic arm being a flexible arm robot, having Bragg reflectors integrated, or using a machine learning algorithm to determine the calibration adjustments to make. However, Larkin, in the same field of endeavor of robotics, teaches:
… a flexible arm robot having a distributed Bragg reflector integrated into the arm, with the flexible … and the distributed Bragg reflector being connected to an interrogator measuring the change in Bragg wavelength as the robot bends, and wherein the flexible arm (Paragraph 0043, "In accordance with embodiments of the present invention, a surgical instrument is provided. This surgical instrument includes an elongate body having a positionable distal end and at least one bendable region, such as a joint region or a flexible region. An optical fiber bend sensor comprising one or more optical fibers is provided in the bendable region of the body. Each of these optical fibers includes a Fiber Bragg Grating, preferably a collinear array of Fiber Bragg Gratings. A strain sensor system comprising a light source and a light detector is used to measure strain in the optical fibers in order to determine a position and shape of the body. This shape and position information can be used to assist in controlling movement of the robotic manipulator and/or surgical instrument. This position information may include both translational and rotational position.") …
However, Ha, in the same field of endeavor of robotics, teaches:
… the computer system having a machine learning system loaded onto the computer system, the machine learning software being used to determine a calibration function for the position of the distributed Bragg reflector; … (Section IV A. "In this article, a multilayer perceptron (MLP) is used to auto-calibrate the shape sensing functionality of the robot. An MLP is a class of feedforward ANNs that consists of an input layer, several hidden layers, and an output layer [28]. Except for the neurons in the input layer, each neuron adopts a nonlinear activation function. These nonlinear activation functions tum the MLP into a nonlinear perception, thus distinguishing itself from linear approaches, such as linear regression. Thanks to a large number of neurons and the possibility to make use of multiple types of nonlinear activation functions. MLPS can model complex nonlinear behavior [29]. The training of the MLP is based on a supervised learning algorithm called back propagation [30]. The training procedure requires sufficient data containing various, patterns. The MLPs developed here relate the measured wavelength shifts from FBGs directly to the curvature and angle of the catheter's bending plane without the explicit knowledge of the correspondence between the fiber's position and the catheter centerline or explicit knowledge of the characteristic parameters of the fiber.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and operating methods as taught by Polido with the flexible arm and Bragg reflectors as taught by Larkin and with the machine learning model as taught by Ha. Incorporating these elements would ensure an accurate calibration adjustment may be determined and a versatile robotic arm would be able to operate efficiently and effectively without the need for operators to perform tedious calibrating operations.
Regarding claim 12, where all the limitations of claim 1 are discussed above, Polido further teaches:
12. The system according to claim 11, wherein tracking markers are positioned at regular spatial and angular positions on the flexible arm robot. (Column 3 Lines 34-51, "Embodiments of the disclosure include methods and systems for robotic system camera calibration and localization using robot-mounted registered patterns that may improve processing and fulfillment of orders, or other object aggregation tasks. Certain embodiments include robotic manipulators with picking assemblies that use end of arm tool-mounted registered patterns, which may be in the form of calibration plates, for robotic system camera calibration and localization. Although described in example herein of a calibration “plate,” calibration patterns may be in different forms, such as three-dimensional markers coupled directly on an end of arm tool housing, painted markers or other forms of two-dimensional markers applied directly on an end of arm tool housing, thin sheets of material adhered to an end of arm tool housing, and so forth. Some embodiments may be disposed on end of arm tool housings without requiring flat surfaces on which to mount the calibration pattern.")
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polido in view of Larkin and Ha and in further view of Alt et al. (US 20210023719 A1), hereinafter Alt.
Regarding claim 3, where all the limitations of claim2 are discussed above, Polido further teaches:
3. The method according to claim 2, wherein at least one tracking marker (Column 3 Lines 34-51, "Embodiments of the disclosure include methods and systems for robotic system camera calibration and localization using robot-mounted registered patterns that may improve processing and fulfillment of orders, or other object aggregation tasks. Certain embodiments include robotic manipulators with picking assemblies that use end of arm tool-mounted registered patterns, which may be in the form of calibration plates, for robotic system camera calibration and localization. Although described in example herein of a calibration “plate,” calibration patterns may be in different forms, such as three-dimensional markers coupled directly on an end of arm tool housing, painted markers or other forms of two-dimensional markers applied directly on an end of arm tool housing, thin sheets of material adhered to an end of arm tool housing, and so forth. Some embodiments may be disposed on end of arm tool housings without requiring flat surfaces on which to mount the calibration pattern.") …
Polido does not specifically teach applying a marking at each joint on the robotic arm. However, Alt, in the same field of endeavor of robotics, teaches:
… is positioned on every joint of the flexible arm. (Paragraph 0092, "In embodiments as shown in FIG. 4, one or several cameras 41 are mounted around the robot arm 1, within or nearby the operating workspace 22 of the robot arm. The external camera 41 observes visual markers/features 34 for direct angle measurement mounted around the axis of at least one joint 11. Thus, rotational visual markers 34 are mounted on some or all joints 11 of the robot arm 1, which are also called “marked joints”. These markers consist of two parts, each of which is rigidly connected to one of the two links 15 connected by the joint 11. The relative orientation between these two parts, specifically the angle of rotation around the axis of joint 11, directly indicates the state of the joint 11. This allows for direct visual reading of the joint angle by the camera and a connected computing unit 5, much like a goniometer.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the robotic system and calibration methods as taught by Polido with the markers at each joint of the arm as taught by Alt. This would allow for highly accurate determination of the pose of the robotic arm thereby ensuring the system may determine a proper calibration adjustment for accurate operation.
Allowable Subject Matter
Claims 7-8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The Examiner has cited particular paragraphs or columns and line numbers in the referencesapplied to the claims above for the convenience of the Applicant. Although the specified citations arerepresentative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the Applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2141.02 [R-07.2015] VI. A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed Invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
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/H.J.K./Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657