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 .
Response to Arguments
The amendment filed on March 2, 2026 has been entered. Claims 1-3, 5-8, and 13 and 15 have been amended. Claims 20-30 are new. The remaining claims are in original or previously presented form. Therefore, claims 1-3, 5-8, and 13,15, 20-30 are pending in the application.
Applicant’s arguments with respect to claims 1-19 have been considered but are moot because
the new ground of rejection does not rely on any reference applied in the prior rejection of record for
any teaching or matter specifically challenged in the argument.
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.
Claims 1,2,5,6,21,22,26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Close (US 20060074525 A1) (hereinafter Close) in view of Dhayalkar (US 20210294328 A1).
Regarding claim 1, Close teaches a method(Close, paragraph 2, systems, devices and methods related to the remote control of robots) for comprising:
determining, by one or more processors(Close discloses a remote computer that corresponds to the processor. Close, paragraph 19, the robot is also communicatively connected to a remote computer) and based on an initial point of origin in a space- constrained environment(Under broadest reasonable interpretation, space-constrained environment can be interpreted as a confined space, indoor space, or a pipeline. Close’s robot operates in a confined space of a pipeline indicating a space-constrained environment. Close, paragraph 40, the present invention finds particular applicability with respect to pipeline robots. Close, paragraph 97, position markers can be used to register the official starting point datum. The purpose is to designate the zero position from which all odometry is recorded for a particular main and its laterals ), an initial scan frame of reference(Close discloses determining reference frame based on “fiducial ” point which corresponds to initial point of origin. Close, paragraph 96, position marking is a method of identifying a point (a feature of interest) in a pipe which may serve as a "fiducial" so that the pipe is referenced to a coordinate reference frame );
extracting, by the one or more processors, a feature based on a reading from at least one sensor(Close discloses identifying feature of interest based on data captured by a sensor like camera. Close, paragraph 114, Identify and record for later use the lateral (or feature of interest) dimensions via captured 3D pipe data or cut path by sensing through a laser scan, camera object recognition, or haptic (touching) method. Close, paragraph 101, a marker reader is the sensor that senses the marker location, giving at a minimum an indication of the presence/absence of the marker), wherein the reading indicates a modification of the space-constrained environment(The modification of the environment can be narrowing of a pipe caused by a liner, and Close discloses identifying marker of a feature after the pipe is modified by a liner. Close, paragraph 98, lateral markers (hereafter "markers") are devices that can be installed at any location on the inside surface of a pipe, prior to re-lining, nearby a lateral (or any other feature of interest) that can be accurately re-located after being covered over with a lining material…markers must be able to be blindly located using some non-contact and non-visual method, such as magnetics, radio frequency (RF), or metallic identity that is detectable through the liner material);
calculating, by the one or more processors and using the feature, a new point of origin(As discussed above, the modification of the environment can be narrowing of a pipe caused by a liner, and Close discloses identifying marker of a feature after the pipe is modified by a liner. Close discloses its robot identifying a marker of a feature of interest and returning to align itself with new and old reference frames, indicating the reestablishing of new starting point(origin ) based on feature of interest. Close, paragraph 96, Position marking is a method of identifying a point (a feature of interest) in a pipe which may serve as a "fiducial" so that the pipe is referenced to a coordinate reference frame. After marking a point and leaving the area, the robot can later sense and return to the marked point, and align itself with the new and old reference frames. Close, paragraph 98, lateral markers (hereafter "markers") are devices that can be installed at any location on the inside surface of a pipe, prior to re-lining, nearby a lateral (or any other feature of interest) that can be accurately re-located after being covered over with a lining material. In other words, markers must be able to be blindly located using some non-contact and non-visual method, such as magnetics, radio frequency (RF), or metallic identity that is detectable through the liner material );
While Close teaches about determining initial point of origin in a space- constrained environment, it fails to disclose determining, by the one or more processors and based on a difference between the initial point of origin and the new point of origin, a new scan frame of reference.
However, Dhayalkar, which is in the same analogous art and that teaches about determining a pose of a sensor on a robot discloses determining, by the one or more processors and based on a difference between the initial point of origin and the new point of origin, a new scan frame of reference(Dhayalkar discloses a transformation matrix that determines the change in coordinate system from first coordinate system (frame of reference) to a new(second) coordinate system based on the change of origin. Dhayalkar, paragraph 37, transformation matrix may comprise a matrix which may be added or multiplied to a set of localization coordinates or data (e.g., points of a point cloud) within a first reference frame to redefine the same set localization coordinates or data in a second reference frame. For example, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin. ).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close with Dhayalkar to determine a second(new ) coordinate system(reference frame) based on change of origin. By determining new coordinate system, it is possible to perform real time adjustments of sensors to enable robots to calibrate their own sensors.( Dhayalkar, paragraph 44, the systems and methods of this disclosure at least: (i) enable robots to determine, in real time, poses of one or more of their sensors; (ii) enable robots to preform real time adjustments to sensors to enhance calibration; and (iii) enhance navigation and task performance of robots by enabling the robots to calibrate their own sensors).
Regarding claim 2, the combination of Close and Dhayalkar teaches the method of claim 1(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the initial scan frame of reference includes an initial scan frame of reference for each sensor based on a pose of the at least one sensor(Dhayalkar discloses the transformation from one coordinate system with one origin to another based on the pose of the sensor, the change coordinate system indicates the existence of initial scan frame of reference. Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206…the sensor transform 216 comprises a spatial transformation of coordinates from an origin 214 of the base link frame 212 to an origin 218 of a sensor 206 based on a position of the origin 218 with respect to the origin 214 of the base link frame 212. This transformation 216 is dependent on a pose of the sensor 206, and thereby a pose of the sensor origin 218 with respect to the origin 214 of the base link frame 212), and wherein determining the new scan frame of reference includes determining a new scan frame of reference for each sensor(As discussed above, Dhayalkar discloses the transformation from one coordinate system with one origin to another based on the pose of the sensor, indicating a coordinate system(new frame of reference) based on the changed origin and sensor pose. Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206…the sensor transform 216 comprises a spatial transformation of coordinates from an origin 214 of the base link frame 212 to an origin 218 of a sensor 206 based on a position of the origin 218 with respect to the origin 214 of the base link frame 212. This transformation 216 is dependent on a pose of the sensor 206, and thereby a pose of the sensor origin 218 with respect to the origin 214 of the base link frame 212 ).
Regarding Claim 5, the combination of Close and Dhayalkar teaches the method of claim 1(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the at least one sensor comprises an inertial measurement unit (IMU)( Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”) ) and the method further comprises calculating the new point of origin based on the IMU's position within the space-constrained environment(Dhayalkar discloses its robot can be floor cleaner which can be operated indoor, which corresponds to space-constrained environment. Dhayalkar, paragraph 34, robots may include autonomous and/or semi-autonomous cars, floor cleaners Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206. Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”) ).
Regarding claim 6, the combination of Close and Dhayalkar teaches the method of claim 5(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206.), further comprising calculating, based on the IMU's position, the new point of origin while traversing the space-constrained environment(Dhayalkar discloses its robot can be floor cleaner( that traverses indoor spaces), implying it can operate in a space-constrained environment. Furthermore, Dhayalkar teaches its navigation system continuously localizes an origin, indicating the origin being determined while it’s traversing the environment. Dhayalkar, paragraph 34, robots may include autonomous and/or semi-autonomous cars, floor cleaners Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206. Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”). Dhayalkar, paragraph 92, navigation units 106 may continuously localize an origin 214 of the robot 102 within its environment with respect to an origin 208 of a world frame 202 as the robot 102 utilizes the test sensor and second sensor to collect the first and second scans).
Regarding Claim 21, Close teaches a system(Close, paragraph 2, systems, devices and methods related to the remote control of robots) comprising:
at least one sensor(Close, paragraph 35, the robotic device includes a universal interface for attaching a variety of different tool heads and sensors );
one or more processors(Close discloses a remote computer that corresponds to the processor. Close, paragraph 19, the robot is also communicatively connected to a remote computer); and
one or more storage devices that store instructions, that, when executed by the one or more processors(Close, paragraph 83, intelligent controls present in the functional components of the robot (local non-volatile storage identifying the robot component and its configuration parameters. Close, paragraph 88,a script could be written to "pre-program" a button on the GUI to perform a repeated function on pipe), cause the one or more processors to:
determine, based on an initial point of origin in an environment, an initial scan frame of reference(Close, paragraph 97, position markers can be used to register the official starting point datum. The purpose is to designate the zero position from which all odometry is recorded for a particular main and its laterals);
extract a feature based on a reading from the at least one sensor(Close discloses identifying feature of interest based on data captured by a sensor like camera. Close, paragraph 114, Identify and record for later use the lateral (or feature of interest) dimensions via captured 3D pipe data or cut path by sensing through a laser scan, camera object recognition, or haptic (touching) method. Close, paragraph 101, a marker reader is the sensor that senses the marker location, giving at a minimum an indication of the presence/absence of the marker), wherein the reading indicates a modification of the environment(The modification of the environment can be narrowing of a pipe caused by a liner, and Close discloses identifying marker of a feature after the pipe is modified by a liner. Close, paragraph 98, lateral markers (hereafter "markers") are devices that can be installed at any location on the inside surface of a pipe, prior to re-lining, nearby a lateral (or any other feature of interest) that can be accurately re-located after being covered over with a lining material…markers must be able to be blindly located using some non-contact and non-visual method, such as magnetics, radio frequency (RF), or metallic identity that is detectable through the liner material);
calculate, using the feature, a new point of origin and (The modification of the environment can be narrowing of a pipe caused by a liner, and Close discloses identifying marker of a feature after the pipe is modified by a liner. Close discloses its robot identifying a marker of a feature of interest and returning to align itself with new and old reference frames, indicating the reestablishing of new starting point(origin ) based on feature of interest. Close, paragraph 96, Position marking is a method of identifying a point (a feature of interest) in a pipe which may serve as a "fiducial" so that the pipe is referenced to a coordinate reference frame. After marking a point and leaving the area, the robot can later sense and return to the marked point, and align itself with the new and old reference frames. Close, paragraph 98, lateral markers (hereafter "markers") are devices that can be installed at any location on the inside surface of a pipe, prior to re-lining, nearby a lateral (or any other feature of interest) that can be accurately re-located after being covered over with a lining material. In other words, markers must be able to be blindly located using some non-contact and non-visual method, such as magnetics, radio frequency (RF), or metallic identity that is detectable through the liner material );
While Close teaches about determining initial point of origin in a space- constrained environment, it fails to disclose determine, based on a difference between the initial point of origin and the new point of origin, a new scan frame of reference.
However, Dhayalkar, which is in the same analogous art and that teaches about determining a pose of a sensor on a robot discloses determine, based on a difference between the initial point of origin and the new point of origin, a new scan frame of reference(Dhayalkar discloses a transformation matrix that determines the change in coordinate system from first coordinate system (frame of reference) to a new(second) coordinate system based on the change of origin. Dhayalkar, paragraph 37, transformation matrix may comprise a matrix which may be added or multiplied to a set of localization coordinates or data (e.g., points of a point cloud) within a first reference frame to redefine the same set localization coordinates or data in a second reference frame. For example, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close with Dhayalkar to determine a second(new ) coordinate system(reference frame) based on change of origin. By determining new coordinate system, it is possible to perform real time adjustments of sensors to enable robots to calibrate their own sensors.( Dhayalkar, paragraph 44, the systems and methods of this disclosure at least: (i) enable robots to determine, in real time, poses of one or more of their sensors; (ii) enable robots to preform real time adjustments to sensors to enhance calibration; and (iii) enhance navigation and task performance of robots by enabling the robots to calibrate their own sensors).
Regarding Claim 22, the combination of Close and Dhayalkar the system of claim 21(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the initial scan frame of reference includes an initial scan frame of reference for each sensor based on a pose of the at least one sensor(Dhayalkar discloses the transformation from one coordinate system with one origin to another based on the pose of the sensor, the change coordinate system indicates the existence of initial scan frame of reference. Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206…the sensor transform 216 comprises a spatial transformation of coordinates from an origin 214 of the base link frame 212 to an origin 218 of a sensor 206 based on a position of the origin 218 with respect to the origin 214 of the base link frame 212. This transformation 216 is dependent on a pose of the sensor 206, and thereby a pose of the sensor origin 218 with respect to the origin 214 of the base link frame 212), and wherein the instructions cause the one or more processors to determine the new scan frame of reference by determining a new scan frame of reference for each sensor(As discussed above, Dhayalkar discloses the transformation from one coordinate system with one origin to another based on the pose of the sensor, indicating a coordinate system(new frame of reference) based on the changed origin and sensor pose. Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206…the sensor transform 216 comprises a spatial transformation of coordinates from an origin 214 of the base link frame 212 to an origin 218 of a sensor 206 based on a position of the origin 218 with respect to the origin 214 of the base link frame 212. This transformation 216 is dependent on a pose of the sensor 206, and thereby a pose of the sensor origin 218 with respect to the origin 214 of the base link frame 212).
Regarding Claim 26, the combination of Close and Dhayalkar teaches the system of claim 21(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the at least one sensor comprises an inertial measurement unit (IMU)( Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”)) and wherein the instructions cause the one or more processors to calculate the new point of origin based on the IMU's position within the environment(Dhayalkar, paragraph 34, robots may include autonomous and/or semi-autonomous cars, floor cleaners Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206. Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”)).
Regarding Claim 27, the combination of Close and Dhayalkar teaches the system of claim 26(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the instructions cause the one or more processors to calculate the new point of origin while traversing the environment(Dhayalkar discloses its navigation system continuously localizes an origin indicating the origin being determined while its traversing the environment. Dhayalkar, paragraph 71, a reference frame with an origin defined about a designated origin point 218 of the sensor 206. Dhayalkar, paragraph 57, sensor units 114 may be configured to determine the odometry of robot 102. For example, sensor units 114 may include proprioceptive sensors, which may comprise sensors such as accelerometers, inertial measurement units (“IMU”). Dhayalkar, paragraph 92, navigation units 106 may continuously localize an origin 214 of the robot 102 within its environment with respect to an origin 208 of a world frame 202 as the robot 102 utilizes the test sensor and second sensor to collect the first and second scans).
Claims 3,15,23, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Close (US 20060074525 A1) (hereinafter Close) in view of Dhayalkar (US 20210294328 A1) in further view of Pillmann (US 20230237697 A1).
Regarding claim 3, the combination of Close and Dhayalkar teaches the method of claim 2(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose reference adjustment further comprising calculating a correction factor and wherein determining the new scan frame of reference for each sensor includes applying the correction factor.
However, Pillmann, which is in the same analogous art and that teaches about object tracking and location perdition, discloses a method further comprising calculating a correction factor(Pillmann, paragraph 111, calibration factor may be determined by mapping a position of the targeting sensor or implement on a surface to pan and tilt actuator positions. Pillmann, paragraph 43, a change in position, or an offset may be expressed in a one frame of reference, it should be understood that the position, change in position, or offset may be expressed in any frame of reference or may be readily converted between frames of reference.) and wherein determining the new scan frame of reference for each sensor includes applying the correction factor(Pillmann, paragraph 43, a position of an object or a position of a sensor, may be expressed relative to a frame of reference. Exemplary frames of reference include a surface frame of reference, a vehicle frame of reference, a sensor frame of reference, or an actuator frame of reference. Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Pillmann to determine the frame of reference for plurality of sensors by applying conversion factor/ offset value, which corresponds to a correction factor. By identifying new frame of reference of each sensor by including conversion factor, it is possible to improve targeting accuracy.( Pillmann, paragraph 111, calibration factor may be determined by mapping a position of the targeting sensor or implement on a surface to pan and tilt actuator positions. Correcting for targeting distortions may improve targeting accuracy).
Regarding Claim 15, the combination of Close and Dhayalkar teaches the method of claim 1(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin) wherein determining the new scan frame of reference (Pillmann, paragraph 43, a position of an object or a position of a sensor, may be expressed relative to a frame of reference. Exemplary frames of reference include a surface frame of reference, a vehicle frame of reference, a sensor frame of reference, or an actuator frame of reference. Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model)includes adjustment based on a size difference caused by the modification of the space-constrained environment(As discussed above, space-constrained environment can be interpreted as a confined space, indoor space, or a pipeline. Similarly, Pillmann discloses its robot adapting to different size environment such as cluttered rooms that are smaller in size to perform tasks. Pillmann, paragraph 2, tasks performed in unpredictable environments, such as driving on city streets or vacuuming a cluttered room, depend on dynamic feedback and adaptation to perform the task. Pillmann, paragraph 43, Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model. While a position, a change in position, or an offset may be expressed in a one frame of reference, it should be understood that the position, change in position, or offset may be expressed in any frame of reference or may be readily converted between frames of reference ).
Regarding Claim 23, the combination of Close and Dhayalkar teaches the system of claim 22(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose reference adjustment wherein the instructions cause the one or more processors to calculate a correction factor and wherein the instructions cause the one or more processors to determine the new scan frame of reference for each sensor by applying the correction factor.
However, Pillmann, which is in the same analogous art and that teaches about object tracking and location perdition, discloses a system wherein the instructions cause the one or more processors to calculate a correction factor(Pillmann, paragraph 111, calibration factor may be determined by mapping a position of the targeting sensor or implement on a surface to pan and tilt actuator positions. Pillmann, paragraph 43, a change in position, or an offset may be expressed in a one frame of reference, it should be understood that the position, change in position, or offset may be expressed in any frame of reference or may be readily converted between frames of reference) and wherein the instructions cause the one or more processors to determine the new scan frame of reference for each sensor by applying the correction factor(Pillmann, paragraph 43, a position of an object or a position of a sensor, may be expressed relative to a frame of reference. Exemplary frames of reference include a surface frame of reference, a vehicle frame of reference, a sensor frame of reference, or an actuator frame of reference. Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Pillmann to determine the frame of reference for plurality of sensors by applying conversion factor/ offset value, which corresponds to a correction factor. By identifying new frame of reference of each sensor by including conversion factor, it is possible to improve targeting accuracy.( Pillmann, paragraph 111, calibration factor may be determined by mapping a position of the targeting sensor or implement on a surface to pan and tilt actuator positions. Correcting for targeting distortions may improve targeting accuracy).
Regarding Claim 29, the combination of Close and Dhayalkar teaches the system of claim 21(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin), wherein the instructions cause the one or more processors to determine the new scan frame of reference by adjustment based on a size difference caused by the modification of the environment(The size difference has been assumed to be a different space with different size. Pillmann discloses its robot adapting to different environment such as cluttered rooms that are smaller in size to perform tasks. Pillmann, paragraph 2, tasks performed in unpredictable environments, such as driving on city streets or vacuuming a cluttered room, depend on dynamic feedback and adaptation to perform the task. Pillmann, paragraph 43, Positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model. While a position, a change in position, or an offset may be expressed in a one frame of reference, it should be understood that the position, change in position, or offset may be expressed in any frame of reference or may be readily converted between frames of reference).
Claims 7,8,24, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Close (US 20060074525 A1) (hereinafter Close) in view of Dhayalkar (US 20210294328 A1) in further view of Pillmann (US 20230237697 A1) in further view of Xie (CN 113607154 A).
Regarding Claim 7, the combination of Close, Dhayalkar, and Pillmann teaches the method of claim 3(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin; Pillmann, paragraph 43, positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model),
The combination of Close, Dhayalkar, and Pillmann specifically fails to disclose a method wherein the correction factor is calculated based on a difference between the initial point of origin and the new point of origin.
However, Xie, which is in the same analogous art and that teaches about an intelligent robot discloses wherein the correction factor is calculated based on a difference between the initial point of origin and the new point of origin(Xie discloses determining offset value based on target parking position and precise parking position that is similar to initial point of origin and the new point of origin. The offset value determination can be implemented to determine the offset value between initial origin and new origin, since origin can be any arbitrary reference point. Xie, paragraph 158, calculating the offset between the target parking position and the precise parking position; according to the offset control robot to move to the target parking position).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close, Dhayalkar, and Pillmann with Xie to determine the offset/correction factor between two different positions. By determining correction factor/offset value, it is possible to reduce measurement error of sensor device.
Regarding Claim 8, the combination of Close, Dhayalkar, Pillmann, and Xie teaches the method of claim 7(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin; Pillmann, paragraph 43, positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model; Xie, paragraph 158, calculating the offset between the target parking position and the precise parking position; according to the offset control robot to move to the target parking position), wherein a robot is moving within the space-constrained environment(Under broadest reasonable interpretation space-constrained environment can be interpreted as a small space in an environment. Xie generally teaches how technological development has allowed the operation of robots in a smaller space. Xie, paragraph 2, the robot is normally operated in the room with small space.) and the method further comprises calculating a distance that would have been traveled in the space-constrained environment using the correction factor(Xie discloses determining the offset value that corresponds to the correction factor, which allows the determination of start and stop position of a robot. This implies the distance can be calculated based on the difference based on the offset value. Xie, paragraph 158, controlling the robot to move to the target stop position according to the offset. Xie, paragraph 167, if the precise stopping position is not the line precise stopping position, then according to the difference between the robot starting position and the closest precise stopping position to control the robot to move to the clamping precise stopping position…calculating the offset between the robot target stop position and the closest accurate stop position; controlling the robot to move to the target stop position according to the offset.).
Regarding Claim 24 the combination of Close, Dhayalkar, and Pillmann teaches the system of claim 23(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin; Pillmann, paragraph 43, positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model),
The combination of Close, Dhayalkar, and Pillmann specifically fails to disclose a system wherein the instructions further cause the one or more processors to calculate the correction factor based on a difference between the initial point of origin and the new point of origin.
However, Xie, which is in the same analogous art and that teaches about an intelligent robot discloses a system wherein the instructions further cause the one or more processors to calculate the correction factor based on a difference between the initial point of origin and the new point of origin(Xie discloses determining offset value based on target parking position and precise parking position that is similar to initial point of origin and the new point of origin. The offset value determination can be implemented to determine the offset value between initial origin and new origin, since origin can be any arbitrary reference point. Xie, paragraph 158, calculating the offset between the target parking position and the precise parking position; according to the offset control robot to move to the target parking position).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close, Dhayalkar, and Pillmann with Xie to determine the offset/correction factor between two different positions. By determining correction factor/offset value, it is possible to reduce measurement error of sensor device
Regarding Claim 25, the combination of Close, Dhayalkar, and Pillmann teaches the system of claim 24(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin; Pillmann, paragraph 43, positions may be readily converted between frames of reference, for example by using a conversion factor or a calibration model; Xie, paragraph 158, calculating the offset between the target parking position and the precise parking position; according to the offset control robot to move to the target parking position ), wherein a robot is moving within the environment and the instructions cause the one or more processors to calculate a distance that would have been traveled in the environment using the correction factor(Xie discloses determining the offset value that corresponds to the correction factor, which allows the determination of start and stop position of a robot. This implies the distance can be calculated based on the difference based on the offset value. Xie, paragraph 158, controlling the robot to move to the target stop position according to the offset. Xie, paragraph 167, if the precise stopping position is not the line precise stopping position, then according to the difference between the robot starting position and the closest precise stopping position to control the robot to move to the clamping precise stopping position…calculating the offset between the robot target stop position and the closest accurate stop position; controlling the robot to move to the target stop position according to the offset.).
Claims 13 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Close (US 20060074525 A1) (hereinafter Close) in view of Dhayalkar (US 20210294328 A1) in further view of Liu(CN 110174136 A).
Regarding Claim 13 the combination of Close and Dhayalkar teaches the method of claim 1(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose a method further comprising: assimilating, by the one or more processors, data from the at least one sensor; and determining, by the one or more processors and based on the assimilation of data, the new scan frame of reference.
However, Liu, which is in the same analogous art and that teaches about underground pipeline intelligent detecting robot, discloses a method further comprising: assimilating, by the one or more processors, data from the at least one sensor(Liu discloses fusing(assimilating) multiple sensor data.
Liu, paragraph 16, calculating the sensor data space position; and registering and fusing different sensor data to obtain the fusion pipeline space multi-source space data ); and determining, by the one or more processors and based on the assimilation of data, the new scan frame of reference(Liu discloses determining space position reference for a robot, which is similar to determining the frame of reference. Liu, paragraph 10, the plurality of sensors are respectively used for autonomous positioning when the mobile robot main body moves in the underground pipeline space, for collecting high precision fusion mapping data, and providing geographic space position reference for the data; the high precision fusion mapping data comprises collecting the image in the pipeline space, three-dimensional laser point cloud, depth image ).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Liu to assimilate (fuse) sensor data and to determine space position reference. By fusing sensor data, it is possible to improve the richness of the data that ensures the accuracy of the data. (Liu, paragraph 22, collecting and processing ensures the accuracy and accuracy of the data; the multi-sensor fusion method improves the richness of the data, The diversity is capable of collecting the multidimensional data at the same time ).
Regarding Claim 28, the combination of Close and Dhayalkar teaches the system of claim 21(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose a system
wherein the instructions further cause the one or more processors to:
assimilate data from the at least one sensor; and
determine, based on the assimilation of data, the new scan frame of reference.
However, Liu, which is in the same analogous art and that teaches about underground pipeline intelligent detecting robot, discloses a system wherein the instructions further cause the one or more processors to:
assimilate data from the at least one sensor(Liu discloses fusing(assimilating) multiple sensor data. Liu, paragraph 16, calculating the sensor data space position; and registering and fusing different sensor data to obtain the fusion pipeline space multi-source space data); and
determine, based on the assimilation of data, the new scan frame of reference(Liu discloses determining space position reference for a robot, which is similar to determining the frame of reference. Liu, paragraph 10, the plurality of sensors are respectively used for autonomous positioning when the mobile robot main body moves in the underground pipeline space, for collecting high precision fusion mapping data, and providing geographic space position reference for the data; the high precision fusion mapping data comprises collecting the image in the pipeline space, three-dimensional laser point cloud, depth image).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Liu to assimilate (fuse) sensor data and to determine space position reference. By fusing sensor data, it is possible to improve the richness of the data that ensures the accuracy of the data. (Liu, paragraph 22, collecting and processing ensures the accuracy and accuracy of the data; the multi-sensor fusion method improves the richness of the data, The diversity is capable of collecting the multidimensional data at the same time ).
Claims 20 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Close (US 20060074525 A1) (hereinafter Close) in view of Dhayalkar (US 20210294328 A1) in further view of Lin(CN 110736456 A).
Regarding Claim 20, the combination of Close and Dhayalkar teaches the method of claim 1(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose a method wherein the feature is associated with a confidence score and wherein calculating the new point of origin occurs when the confidence score satisfies a predetermined threshold.
However, Lin, which is in the same analogous art and that teaches about real-time positioning of a robot discloses a method wherein the feature is associated with a confidence score and wherein calculating the new point of origin occurs when the confidence score satisfies a predetermined threshold(Lin disclose determining a new pose of robot (which has an origin) based on features with high confidence scores leaving out features with low confidence score. A person of ordinary skill in the art would be able to modify Lin’s teaching to calculate the new origin(pose) when an extracted feature’s confidence score is higher than a predetermined threshold instead of selecting the feature with highest confidence score to calculate new pose(origin). Lin, paragraph 12, the weight of point distribution in the different sets are not completely consistent, calculating each of the possible candidate pose of the confidence degree, selecting the estimation confidence pose the highest degree value as the optimal pose of the robot. Lin, paragraph 132, the confidence σ for each candidate pose and confidence degree weight ω the product as the current pose of the confidence score, the formula is as follows, Lin, paragraph 133 score= σ * ω selecting the confidence pose degree value with the highest score as the optimal pose estimation.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Lin to select the pose(origin) with the highest confidence score while improving accuracy of robot positioning in sparse environment. (Lin, paragraph 54, using two-dimensional laser real-time positioning method of sparse environment of the invention based on the feature extraction, the cloud data of different feature types giving different confidence degree weight value, can improve barrier confidence of characteristic mark in the positioning process, so as to improve the positioning accuracy of the robot).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
Regarding Claim 30, the combination of Close and Dhayalkar teaches the system of claim 21(Close, paragraph 19, the robot is also communicatively connected to a remote compute; Dhayalkar, paragraph 37, a transformation matrix may correspond to a mathematical representation of a change of coordinates from a first coordinate system defined about a first origin to a second coordinate system defined about a second origin),
The combination of Close and Dhayalkar specifically fails to disclose a system wherein the feature is associated with a confidence score and wherein the instructions cause the one or more processors to calculate the new point of origin when the confidence score satisfies a predetermined threshold(Lin disclose determining a new pose of robot (which has an origin) based on features with high confidence scores leaving out features with low confidence score. A person of ordinary skill in the art would be able to modify Lin’s teaching to calculate the new origin(pose) when an extracted feature’s confidence score is higher than a predetermined threshold instead of selecting the feature with highest confidence score to calculate new pose(origin). Lin, paragraph 12, the weight of point distribution in the different sets are not completely consistent, calculating each of the possible candidate pose of the confidence degree, selecting the estimation confidence pose the highest degree value as the optimal pose of the robot. Lin, paragraph 132, the confidence σ for each candidate pose and confidence degree weight ω the product as the current pose of the confidence score, the formula is as follows, Lin, paragraph 133 score= σ * ω selecting the confidence pose degree value with the highest score as the optimal pose estimation).
However, Lin, which is in the same analogous art and that teaches about real-time positioning of a robot discloses a system wherein the feature is associated with a confidence score and wherein the instructions cause the one or more processors to calculate the new point of origin when the confidence score satisfies a predetermined threshold.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Close and Dhayalkar with Lin to select the pose(origin) with the highest confidence score while improving accuracy of robot positioning in sparse environment. (Lin, paragraph 54, using two-dimensional laser real-time positioning method of sparse environment of the invention based on the feature extraction, the cloud data of different feature types giving different confidence degree weight value, can improve barrier confidence of characteristic mark in the positioning process, so as to improve the positioning accuracy of the robot).
This conclusion of obviousness corresponds to KSR rationale “A”: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined prior art elements according to known methods to yield predictable results. See MPEP § 2141, subsection III.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BESUFEKAD LEMMA TESSEMA/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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