DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/21/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-8, 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. “Shakebot: A low-cost, open source shake table for ground motion seismic studies”. December 21, 2022 (as seen in the IDS. This NPL has a set of inventors that is different from the set of inventors listed in the instant application. This inventors in the NPL include Madugo and Kottke, both of which are not listed in the instant application, therefore the rejection is considered to be invented “by another” set of inventors.
As to claim 1, Chen teaches “A method for operating a robotic shake table (Abstract), comprising: receiving a specimen placed on top of a pedestal (I. Introduction), the pedestal slidably coupled to two linear shafts of a chassis (page 2, section Hardware Mechanism teaches “Two main components of the Shakebot are a chassis and a control box, as shown in Fig. 1. The chassis is constructed of an extruded aluminum T-slot frame that supports two linear shafts. A carriage is mounted on the linear shafts with ball bearings, which reduce the friction between the linear shafts and the carriage. A flatbed (pedestal) is attached to the carriage to hold scale-model PBRs.”); receiving an input motion data at a microcontroller (page 3, A. Hardware Mechanism teaches “and a Raspberry Pi that serves as the processing unit and handles trajectory generation and low-level system control), the microcontroller communicatively coupled to a motion system and a perception system (page 3, Perception System teaches “The perception system estimates the status of the bed using a camera and an accelerometer. As shown in Fig. 1b and Fig. 2, four fiducial markers are affixed to the corners of the bed. A top-down view FLIR Chameleon®3 Color CameraCM3-U3-13Y3C-CS 1/2" captures the position and orientation of the bed using the ROS package [22,23]. Additionally, an accelerometer (Wit Motion HWT905-TTL) attached underneath the bed measures acceleration (ground motion acceleration). We directly obtain ground motion displacement and acceleration from the fiducial marker detection and accelerometer, respectively. We additionally estimate the ground motion velocities by fusing the information from the fiducial marker detection and accelerometer”) , wherein the motion system comprises a stepper motor, a transmission, and a motor driver communicatively coupled to a stepper motor (page 3, section hardware Mechanism, teaches “The stepper motor controller, contained in the control box, drives this stepper motor), with the stepper motor coupled to the pedestal through the transmission, the stepper motor also fixedly coupled to the chassis and wherein the perception system comprises at least one of an accelerometer (page 3, Perception System) coupled to the pedestal and a camera positioned above the pedestal to view a plurality of fiducial markers affixed to the pedestal (Introduction teaches “our Shakebot also leverages a top-down view camera to estimate ground motion. The perception system fuses accelerations and camera-based displacements for better ground velocity estimation); producing a set of translational velocities based upon the input motion data; converting the set of translational velocities into a control signal for the motor driver; driving the stepper motor according to the control signal to move the pedestal along the two linear shafts while the specimen is on top of the pedestal and while recording perception data from the perception system; and estimating a ground motion velocity of the pedestal using the perception data obtained from the perception system (pages 2 and 3, sections A. Hardware Mechanism and B. Motor Selection. Page 5, left column).”
As to claim 2, Chen teaches “wherein the input motion data comprises a peak ground acceleration and a peak ground velocity (Section A. Motor Control).”
As to claim 3, Chen teaches “wherein producing the set of translational velocities based upon the input motion data comprises: defining a ground velocity function derived from a single-pulse cosine displacement function that comprises the peak ground acceleration and the peak ground velocity (Section V. Motion System); and producing the set of translational velocities by evaluating the ground velocity function at different times (Section B. Motor Selection; Section C. Velocity Estimation).”
As to claim 4, Chen teaches “wherein the input motion data comprises a seismometer record having raw acceleration data (Section A. Motor Control).”
As to claim 5, Chen teaches “wherein producing the set of translational velocities based upon the input motion data comprises: removing high-frequency noise from the raw acceleration data using a low-pass filter; numerically integrating the raw acceleration data to obtain velocity data; producing the set of translational velocities by removing low-frequency noise from the velocity data using a high-pass filter (Section A. Motor Control).”
As to claim 6, Chen teaches “wherein estimating the ground motion velocity of the pedestal using the perception data obtained from the perception system comprises at least one of measuring ground motion accelerations of the pedestal using perception data from the accelerometer and estimating ground motion displacements of the pedestal using perception data from the camera observing the plurality of fiducial markers (introduction; Section IV Perception System, subsection A. Displacement Estimation).”
As to claim 7, Chen teaches “wherein measuring ground motion accelerations of the pedestal using perception data from the accelerometer comprises: obtaining raw acceleration data from the accelerometer; applying a low-pass filter to the raw acceleration data; and calculating an absolute value of an acceleration vector defined by the raw acceleration data; wherein estimating the ground motion velocity comprises numerically integrating the ground motion acceleration (Section IV Perception System, subsection B. Acceleration Estimation).”
As to claim 8, Chen teaches “wherein estimating ground motion displacements of the pedestal using the perception data from the camera observing the plurality of fiducial markers comprises averaging a relative displacement of each fiducial marker of the plurality of fiducial markers that is visible to the camera; and wherein estimating the ground motion velocity comprises numerically differentiating the ground motion displacements (Section IV Perception System).”
As to claim 13, Chen teaches “wherein the perception system further comprises an encoder, and wherein the perception system and the motion system operate together in a closed loop (Section A. Motor Control).”
As to claim 14, Chen teaches “wherein the specimen is a model of a precariously balanced rock (Introduction teaches “dynamics of precariously balanced rocks”).”
As to claim 15, Chen teaches “comprising recording an overturn response describing the specimen after the specimen has stopped moving (Introduction).”
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.
Claim(s) 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. “Shakebot: A low-cost, open source shake table for ground motion seismic studies”. December 21, 2022 (as seen in the IDS. This NPL has a set of inventors that is different from the set of inventors listed in the instant application. This inventors in the NPL include Madugo and Kottke, both of which are not listed in the instant application, therefore the rejection is considered to be invented “by another” set of inventors.
As to claim 16, Chen teaches “A robotic shake table (Title) comprising: a chassis comprising two linear shafts and a pedestal slidably coupled to the two linear shafts (page 2, section Hardware Mechanism teaches “Two main components of the Shakebot are a chassis and a control box, as shown in Fig. 1. The chassis is constructed of an extruded aluminum T-slot frame that supports two linear shafts. A carriage is mounted on the linear shafts with ball bearings, which reduce the friction between the linear shafts and the carriage. A flatbed (pedestal) is attached to the carriage to hold scale-model PBRs.”); a motion system comprising a stepper motor (page 3, section hardware Mechanism, teaches “The stepper motor controller, contained in the control box, drives this stepper motor), a transmission (page 1, Introduction teaches “The hardware mechanism is simplified by design. The Shakebot adopts a closed-loop stepper motor with a toothed belt and pulley transmission mechanism), and a motor driver communicatively coupled to the stepper motor, with the stepper motor coupled to the pedestal through the transmission and the stepper motor also fixedly coupled to the chassis (page 3, left column teaches “The stepper motor controller, contained in the control box, drives this stepper motor. The control box also houses the power supply (S-350-60), stepper motor drive (CL86T), an emergency stop button, a touchscreen for the user interface, and a Raspberry Pi that serves as the processing unit and handles trajectory generation and low-level system control.B. Motor SelectionTo select a stepper motor, we considered the stepper motor torque at its maximum angular velocity to satisfy the required ground motion acceleration and velocity. Because stepper motor torque decreases with velocity, if the torque at the maximum velocity satisfies the acceleration requirement, the stepper motor can also achieve so at any smaller velocities); a perception system comprising a camera, an accelerometer coupled to the pedestal, and a plurality of fiducial markers coupled to the pedestal (page 3, Perception System teaches “The perception system estimates the status of the bed using a camera and an accelerometer. As shown in Fig. 1b and Fig. 2, four fiducial markers are affixed to the corners of the bed. A top-down view FLIR Chameleon®3 Color CameraCM3-U3-13Y3C-CS 1/2" captures the position and orientation of the bed using the ROS package [22,23]. Additionally, an accelerometer (Wit Motion HWT905-TTL) attached underneath the bed measures acceleration (ground motion acceleration). We directly obtain ground motion displacement and acceleration from the fiducial marker detection and accelerometer, respectively. We additionally estimate the ground motion velocities by fusing the information from the fiducial marker detection and accelerometer”); and a microcontroller (page 3, A. Hardware Mechanism teaches “and a Raspberry Pi that serves as the processing unit and handles trajectory generation and low-level system control) communicatively coupled to the motor driver and the perception system, the microcontroller comprising a processor and a memory (a raspberry pi has RAM for running programs and can operate microSD cards. Therefore it would have been obvious to one of ordinary skill to use a microSD card or external USB drive to store the operating system and files), the processor configured to: receive an input motion data; produce a set of translational velocities based upon the input motion data; and convert the set of translational velocities into a control signal that will cause the stepper motor to move the pedestal along the two linear shafts according to the set of translational velocities when the control signal is sent to the motor driver (pages 2 and 3, sections A. Hardware Mechanism and B. Motor Selection. Page 5, left column).” Chen does not explicitly teach “a memory”
It would have been obvious to one of ordinary skill in the art before the filing of the invention to use a memory attached to a Raspberry Pi. It is known to attach an external memory to these types of microcontroller since they are designed to be compact. The memory allows for the storage of files and programs.
As to claim 17, Chen teaches “wherein the processor is further configured to receive perception data from the perception system; wherein the perception data comprises raw acceleration data from the accelerometer (Section A. Motor Control) and relative displacements of fiducial markers observed by the camera; and wherein the raw acceleration data and the relative displacements are asynchronously aligned (Section A. Perception System Calibration).”
As to claim 18, Chen teaches “wherein the perception system further comprises an encoder, and wherein the motion system is a closed loop (Section A. Motor Control).”
As to claim 19, Chen teaches “wherein the input motion data comprises a seismometer record having raw acceleration data; and wherein the processor produces the set of translational velocities based upon the input motion data by: removing high-frequency noise from the raw acceleration data using a low-pass filter, numerically integrating the raw acceleration data to obtain velocity data, and producing the set of translational velocities by removing low-frequency noise from the velocity data using a high-pass filter (Section A. Motor Control).”
As to claim 20, Chen teaches “wherein the input motion data comprises a peak ground acceleration and a peak ground velocity (Section A. Motor Control) ; and wherein the processor produces the set of translational velocities based upon the input motion data by: defining a ground velocity function derived from a single-pulse cosine displacement function that comprises the peak ground acceleration and the peak ground velocity (Section V. Motion System); and producing the set of translational velocities by evaluating the ground velocity function at different times (Section B. Motor Selection; Section C. Velocity Estimation).”
Allowable Subject Matter
Claims 9-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 9 teaches “combining ground motion velocities estimated using ground motion accelerations obtained using perception data from the accelerometer with ground motion velocities estimated using ground motion displacements estimated using perception data from the camera, forming a combined set of ground motion velocities.” These method steps are not taught by the prior art. Claims 10, 11 and 12 depend from claim 9. The advantage is that “estimating ground motion velocity of the pedestal may include minimizing an error between the combined set of ground motion velocities based on perception data and an estimated velocity function by applying a regression model.”
Conclusion
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/TARUN SINHA/Primary Examiner, Art Unit 2855
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