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 . This action is made non-final.
Claims 1-18 filed on 06/02/2026 have been reviewed and considered by this office action.
Claims 1, 4, and 6 have been amended.
Claims 16-18 have been newly added.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
Response to Arguments
Applicant’s amended claims, filed 06/02/2026, have overcome the rejections under 35 U.S.C. § 112. Therefore, the rejections have been withdrawn.
Applicant’s arguments regarding the rejections under 35 U.S.C. § 103 have been considered but are moot because a new ground of rejection is made in view of Liu and Lucas.
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.
Claims 1-14, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Woodside et al. (US 2023/0075352 A1), in view of Liu et al. (US 2018/0364731 A1), and in view of Lucas et al. (US 2021/0229216 A1).
Regarding claim 1, Woodside teaches a system, comprising:
a plurality of sensors, each configured to generate a data stream to produce a plurality of data streams ([0003]: “The present disclosure relates to dynamic compensation for errors in the position and orientation of a robot end effector, and more particularly dynamically compensating for errors in the position and orientation of a robot end effector utilizing a kinematic error observer algorithm. Even more specifically, this disclosure relates to using an external high-precision metrology tracking system, such as a laser tracker system, to directly measure robot kinematic errors such that corrections are implemented”),
wherein ([0033]: “the metrology tracking system has two components, the 6 DoF sensor and the laser tracker”);
one or more processors in communication with one or more memory having machine readable instructions stored thereon ([0027]: “The apparatuses/systems and methods described herein can be implemented at least in part by one or more computer program products comprising one or more non-transitory, tangible, computer-readable mediums storing computer programs with instructions that may be performed by one or more processors. The computer programs may include processor executable instructions and/or instructions that may be translated or otherwise interpreted by a processor such that the processor may perform the instructions”) that when executed by the one or more processors are configured to:
integrate the plurality of data streams into a single integrated data stream providing an actual positional data stream for an object under observation ([0033]: “Position and orientation measurements collected by the laser tracker and 6 DoF sensor, respectively, are combined through a proprietary method to create a single measurement of the position and orientation of the 6 DoF sensor, and hence the actual position and orientation of the end effector”);
receive an object positional data stream from the object under observation ([0007]: “The computer is configured to receive the robot measurement signal corresponding to the kinematic position and orientation of the end effector from the robot control system”);
compare the object positional data stream to the single integrated data stream to determine a kinematic offset between an actual position of the object under observation and a programmed position of the object under observation ([0006]: “kinematic error is the difference between the location of the robot's end effector measured by the robot controller referred to as the kinematic location, and the actual location measured by the metrology tracking system. The term 'location', as used in this disclosure, means both position and orientation. The kinematic location is computed from the robot's encoder measurements mapped through the robot's forward kinematic model… When the kinematic location is compared to that of the actual location, provided by the metrology tracking system, these errors can be identified and corrected”);
provide control instructions to the object under observation to account for the kinematic offset and the expected offset and reposition the object under observation from the actual position to an intended position ([0034]: “At runtime, the robot measurement is matched to the tracker measurement, the matched set of measurements are used to compute a kinematic error measurement, a kinematic error estimate is computed from the kinematic error measurement, and a rounded incremental correction of the end effectors position and orientation are computed from the kinematic error estimate. The incremental correction command is then transmitted to the robot controller where it is used to correct the position and orientation of the robot's end effector,” where the feedback loop implementing the incremental correction is shown in Fig. 1).
Woodside does not explicitly teach wherein at least one of the plurality of sensors comprises an inertial measurement unit (IMU). Also, while Woodside teaches determining an expected temporal offset ([0045]: “Find the average relative delay,
E
(
δ
r
)
,
by measuring the average temporal offset from the plot”), Woodside does not explicitly teach “determine expected offsets based on calculation and machine implementation latency wherein determining the expected offsets comprises projecting at least one of the object positional data stream and the single integrated data stream into a future time corresponding to implementation of control instructions.”
Liu teaches wherein at least one of the plurality of sensors comprises an inertial measurement unit (IMU) ([0034]: “the auxiliary sensor can be a multi-axis inertial measurement unit (IMU), which measures and reports the mobile unit's linear acceleration using one or more accelerometers and rotational rate using one or more gyroscopes”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the system of Woodside to incorporate the teachings of Liu so as to include at least one of the plurality of sensors comprising an inertial measurement unit (IMU). Doing so would allow high frequency inertial measurement data to be used with laser tracker measurements with the aim of providing more frequent positional updates while retaining accurate position information ([0057]: “the localization pipeline combines information from IMU 606 which runs at relatively high frequency to provide frequent updates of less accurate information, and camera 602, which run at relatively lower frequency, 30 Hz, to provide more accurate information with less frequency”).
Lucas teaches determine an expected offset based on machine implementation latency wherein determining the expected offset comprises projecting at least one of the object positional data stream and the single integrated data stream into a future time corresponding to implementation of control instructions ([0008]: “Such servo systems have a finite tracking delay between the commanded position and the actual mirror position”; [0021]: “The prediction of the future location of the positioning system using this velocity information assumes that the motion of the positioning system will be approximately straight-line over the time interval of the galvanometer tracking delay”; [0026]: “A position offset is calculated for the galvanometers that is proportional to the galvanometer tracking delay and the currently calculated positioning system velocity and acceleration 34”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the system of Woodside in view of Liu to incorporate the teachings of Lucas so as to include determining an expected offset based on machine implementation latency wherein determining the expected offset comprises projecting at least one of the object positional data stream and the single integrated data stream into a future time corresponding to implementation of control instructions. Doing so would allow for the reduction of positional error due to tracking delay with the aim of improving speed and accuracy (Lucas, [0011]: “There remains a need however, for a more efficient and more economical on-the-fly laser marking system that provides improved laser marking accuracy at higher speeds and accuracy”).
Regarding claim 2, Woodside in view of Liu and Lucas teaches the system of claim 1.
Woodside further teaches synchronize the data streams, prior to integrating the plurality of data streams into the single integrated data stream ([0040]: “As mentioned in [0034] the robot and tracker measurements may be unsynchronized. Lack of synchronicity of the measurements will result in both a relative time delay between the two clock signals and jitter in each clock signal's timing. Each of these issues are addressed independently in the algorithmic procedure discussed below”; The positional data stream may be synchronized by matching “the lagging and interpolated leading measurements for the kth control iteration by,
(
T
r
b
[
k
]
,
T
s
b
[
k
]
,
t
k
[
k
]
)
=
T
r
b
,
T
~
,
t
r
τ
=
1
T
~
,
T
s
b
,
t
s
τ
=
0
,” as supported by [0052]).
While Woodside teaches filtering the kinematic error estimate ([0093]: “To provide a single metric for each increase in the robot's corrected kinematic error, the spatial components of the corrected positional kinematic error were filtered independently using a zero-phase 6th order Butterworth filter with cutoff frequencies ranging between 0.1 Hz and 0.5 Hz”), Woodside does not explicitly teach “filter at least one of the plurality of data streams.” Additionally, while Woodside teaches interpolating the data points ([0051]: “Interpolate a leading measurement,
T
~
, at
t
~
from the leading measurement data,
T
1
and
T
2
, corresponding to the timestamps,
t
1
and
t
2
, by,
T
~
=
f
i
n
t
T
1
,
t
1
,
T
2
,
t
2
,
t
~
where
f
i
n
t
.
.
.
:
☐
4
×
4
→
☐
4
×
4
is the homogenous transformation interpolation function defined in the appendix”), Woodside does not explicitly teach “extrapolate information from at least one of the plurality of data streams.”
Liu further teaches wherein the one or more processors in communication with one or more memory having machine readable instructions stored thereon that when executed by the one or more processors are further configured to: filter at least one of the plurality of data streams, extrapolate information from at least one of the plurality of data streams ([0162]: “Implementations can employ extended Kalman filtering (EKF), shown in a general nonlinear model form by equations (1), to extrapolate an initial pose using inertial data from the multi-axis IMU, to generate a propagated pose”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the system of Woodside in view of Lucas to incorporate the teachings of Liu so as to include filtering at least one of the plurality of data streams and extrapolate information from at least one of the plurality of data streams. Doing so would allow extrapolation of data with the aim of generating positional information quickly while reducing computational cost ([0164]: “IMU data alone is used to perform propagation of the EKF. This enables certain implementations to provide fast results and at relatively low computation costs”).
Regarding claim 3, Woodside in view of Liu and Lucas teaches the system of claim 1.
Woodside further teaches wherein the one or more processors in communication with one or more memory having machine readable instructions stored thereon that when executed by the one or more processors are further configured to: provide a kinematic feedback loop to the object under observation to correct the actual position of the object under observation to provide for positional precision and accuracy of the object under observation in real time ([0034]: “At runtime, the robot measurement is matched to the tracker measurement, the matched set of measurements are used to compute a kinematic error measurement, a kinematic error estimate is computed from the kinematic error measurement, and a rounded incremental correction of the end effectors position and orientation are computed from the kinematic error estimate. The incremental correction command is then transmitted to the robot controller where it is used to correct the position and orientation of the robot's end effector,” where the feedback loop is shown in Fig. 1).
Regarding claim 4, Woodside teaches a method, comprising: providing a plurality of sensors configured to observe an object under observation ([0003]: “The present disclosure relates to dynamic compensation for errors in the position and orientation of a robot end effector, and more particularly dynamically compensating for errors in the position and orientation of a robot end effector utilizing a kinematic error observer algorithm. Even more specifically, this disclosure relates to using an external high-precision metrology tracking system, such as a laser tracker system, to directly measure robot kinematic errors such that corrections are implemented”),
wherein ([0033]: “the metrology tracking system has two components, the 6 DoF sensor and the laser tracker”);
generating a plurality of data streams from each of the plurality of sensors ([0007]: “The tracker generates a tracker measurement signal corresponding to the actual position and orientation of the end effector as the end effector moves toward its desired position and orientation and supplies the tracker measurement signal to a computer”);
integrating the plurality of data streams into a single integrated data stream providing an actual positional data stream for the object under observation ([0033]: “Position and orientation measurements collected by the laser tracker and 6 DoF sensor, respectively, are combined through a proprietary method to create a single measurement of the position and orientation of the 6 DoF sensor, and hence the actual position and orientation of the end effector”);
receiving an object positional data stream from the object under observation ([0007]: “The computer is configured to receive the robot measurement signal corresponding to the kinematic position and orientation of the end effector from the robot control system”);
comparing the object positional data stream to the single integrated data stream to determine a kinematic offset between an actual position of the object under observation and a programmed position of the object under observation ([0006]: “kinematic error is the difference between the location of the robot's end effector measured by the robot controller referred to as the kinematic location, and the actual location measured by the metrology tracking system. The term 'location', as used in this disclosure, means both position and orientation. The kinematic location is computed from the robot's encoder measurements mapped through the robot's forward kinematic model… When the kinematic location is compared to that of the actual location, provided by the metrology tracking system, these errors can be identified and corrected”).
providing control instructions to the object under observation to account for the kinematic offset and the expected offset and reposition the object under observation from the actual position to an intended position ([0034]: “At runtime, the robot measurement is matched to the tracker measurement, the matched set of measurements are used to compute a kinematic error measurement, a kinematic error estimate is computed from the kinematic error measurement, and a rounded incremental correction of the end effectors position and orientation are computed from the kinematic error estimate. The incremental correction command is then transmitted to the robot controller where it is used to correct the position and orientation of the robot's end effector,” where the feedback loop implementing the incremental correction is shown in Fig. 1).
Woodside does not explicitly teach wherein at least one of the plurality of sensors comprises an inertial measurement unit (IMU). Also, while Woodside teaches determining an expected temporal offset ([0045]: “Find the average relative delay,
E
(
δ
r
)
,
by measuring the average temporal offset from the plot”), Woodside does not explicitly teach “determining expected offsets based on calculation and machine implementation latency wherein determining the expected offsets comprises projecting at least one of the object positional data stream and the single integrated data stream into a future time corresponding to implementation of control instructions.”
Liu teaches wherein at least one of the plurality of sensors is an inertial measurement unit (IMU) ([0034]: “the auxiliary sensor can be a multi-axis inertial measurement unit (IMU), which measures and reports the mobile unit's linear acceleration using one or more accelerometers and rotational rate using one or more gyroscopes”).
The reasons to combine Liu with Woodside are the same as articulated in the rejection of claim 1 above.
Lucas further teaches determining an expected offset based on calculation and machine implementation latency wherein determining the expected offsets comprises projecting at least one of the object positional data stream and the single integrated data stream into a future time corresponding to implementation of control instructions ([0008]: “Such servo systems have a finite tracking delay between the commanded position and the actual mirror position”; [0021]: “The prediction of the future location of the positioning system using this velocity information assumes that the motion of the positioning system will be approximately straight-line over the time interval of the galvanometer tracking delay”; [0026]: “A position offset is calculated for the galvanometers that is proportional to the galvanometer tracking delay and the currently calculated positioning system velocity and acceleration 34”).
The reasons to combine Lucas with Woodside in view of Liu are the same as articulated in the rejection of claim 1 above.
Regarding claim 5, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside further teaches further comprising providing a kinematic feedback loop to the object under observation to correct the actual position of the object under observation to be the programmed position of the object under observation to provide for positional precision and accuracy of the object under observation ([0034]: “At runtime, the robot measurement is matched to the tracker measurement, the matched set of measurements are used to compute a kinematic error measurement, a kinematic error estimate is computed from the kinematic error measurement, and a rounded incremental correction of the end effectors position and orientation are computed from the kinematic error estimate. The incremental correction command is then transmitted to the robot controller where it is used to correct the position and orientation of the robot's end effector,” where the feedback loop implementing the incremental correction is shown in Fig. 1).
Regarding claim 6, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside further teaches further comprising synchronizing the object positional data stream to the single integrated data stream before determining the expected offset ([0040]: “As mentioned in [0034] the robot and tracker measurements may be unsynchronized. Lack of synchronicity of the measurements will result in both a relative time delay between the two clock signals and jitter in each clock signal's timing. Each of these issues are addressed independently in the algorithmic procedure discussed below”; The positional data stream may be synchronized by matching “the lagging and interpolated leading measurements for the kth control iteration by,
(
T
r
b
[
k
]
,
T
s
b
[
k
]
,
t
k
[
k
]
)
=
T
r
b
,
T
~
,
t
r
τ
=
1
T
~
,
T
s
b
,
t
s
τ
=
0
,” as supported by [0052]).
While Woodside teaches using the temporal offset to compute a kinematic error estimate, which is then used to implement a control command ([0034]: “The incremental correction command is then transmitted to the robot controller where it is used to correct the position and orientation of the robot's end effector,” where the feedback loop is shown in Fig. 1; FIG. 5 and [0057]: “The KEC algorithm computes a rounded incremental correction (Step 5) from the kinematic error estimate to be applied to the robot during the timestep of the control iteration”), Woodside does not explicitly teach “using the expected offset in a kinematic feedback loop to correct the actual position of the object under observation so the expected offset aligns in time with an implementation of a control command to the object under observation using the expected offset.” Also, while Woodside teaches interpolating the data points ([0051]: “Interpolate a leading measurement,
T
~
, at
t
~
from the leading measurement data,
T
1
and
T
2
, corresponding to the timestamps,
t
1
and
t
2
, by,
T
~
=
f
i
n
t
T
1
,
t
1
,
T
2
,
t
2
,
t
~
where
f
i
n
t
.
.
.
:
☐
4
×
4
→
☐
4
×
4
is the homogenous transformation interpolation function defined in the appendix”), Woodside does not explicitly teach “extrapolating data points within at least one of the plurality of data streams to provide additional data points for synchronization and comparison.” Finally, while Woodside teaches filtering the kinematic error estimate ([0093]: “To provide a single metric for each increase in the robot's corrected kinematic error, the spatial components of the corrected positional kinematic error were filtered independently using a zero-phase 6th order Butterworth filter with cutoff frequencies ranging between 0.1 Hz and 0.5 Hz”), Woodside does not explicitly teach “filtering at least one of the plurality of data streams.”
Lucas further teaches using the expected offset in a kinematic feedback loop to correct the actual position of the object under observation so the expected offset aligns in time with an implementation of a control command to the object under observation using the expected offset ([0026]: “A position offset is calculated for the galvanometers that is proportional to the galvanometer tracking delay and the currently calculated positioning system velocity and acceleration 34. This offset is added to the galvanometer command data 36”).
Liu further teaches extrapolating data points within at least one of the plurality of data streams to provide additional data points for synchronization and comparison, and filtering at least one of the plurality of data streams ([0162]: “Implementations can employ extended Kalman filtering (EKF), shown in a general nonlinear model form by equations (1), to extrapolate an initial pose using inertial data from the multi-axis IMU, to generate a propagated pose”).
Regarding claim 7, Woodside in view of Liu and Lucas teaches the method of claim 6.
Liu further teaches wherein the filtering comprises using Kalman Filtering to produce the single integrated data stream ([0162]: “Implementations can employ extended Kalman filtering (EKF), shown in a general nonlinear model form by equations (1), to extrapolate an initial pose using inertial data from the multi-axis IMU, to generate a propagated pose”).
Regarding claim 8, Woodside in view of Liu and Lucas teaches the method of claim 6.
Woodside further teaches wherein the generated data streams are related to a position of the object under observation ([0010]: “a laser tracking measuring system, having a 6 DoF sensor carried by the end effector is utilized to determine the actual position and orientation of the end effector as it is moved toward its desired position and orientation with this FIG. 2 illustrating the transformational relationships that are used to define kinematic and measured position and orientation of the 6 DoF sensor with respect to the robot's base frame”).
Regarding claim 9, Woodside in view of Liu and Lucas teaches the method of claim 6.
Woodside further teaches wherein another sensor is positioned on at least one of the plurality of sensors configured for observing the object under observation ([0033]: “The 6 DoF sensor houses several orientation sensors and a retro reflector which are used to measure its orientation and position, respectively. More specifically the position of the 6 DoF sensor is measured by the laser tracker and the orientation of the 6 DoF sensor is measured by the sensor itself and transmitted to the tracker”).
Regarding claim 10, Woodside in view of Liu and Lucas teaches the method of claim 6.
Woodside further teaches wherein the receipt, processing, comparison, and feedback loop are provided in real time to provide an updated kinematic correction to the object under observation to update an object position during use ([0003]: “dynamically compensating for errors in the position and orientation of a robot end effector utilizing a kinematic error observer algorithm,” where dynamic compensation corresponds to real time position updates).
Regarding claim 11, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside further teaches wherein at least one of the single integrated data stream and the object positional data stream is ([0040]: “As mentioned in [0034] the robot and tracker measurements may be unsynchronized. Lack of synchronicity of the measurements will result in both a relative time delay between the two clock signals and jitter in each clock signal's timing. Each of these issues are addressed independently in the algorithmic procedure discussed below”; The positional data stream may be synchronized by matching “the lagging and interpolated leading measurements for the kth control iteration by,
(
T
r
b
[
k
]
,
T
s
b
[
k
]
,
t
k
[
k
]
)
=
T
r
b
,
T
~
,
t
r
τ
=
1
T
~
,
T
s
b
,
t
s
τ
=
0
,” as supported by [0052]).
While Woodside teaches interpolating the data points ([0051]: “Interpolate a leading measurement,
T
~
, at
t
~
from the leading measurement data,
T
1
and
T
2
, corresponding to the timestamps,
t
1
and
t
2
, by,
T
~
=
f
i
n
t
T
1
,
t
1
,
T
2
,
t
2
,
t
~
where
f
i
n
t
.
.
.
:
☐
4
×
4
→
☐
4
×
4
is the homogenous transformation interpolation function defined in the appendix”), Woodside does not explicitly teach “wherein at least one of the single integrated data stream and the object positional data stream is extrapolated to provide additional data points for comparison.” Additionally, while Woodside teaches filtering the kinematic error estimate ([0093]: “To provide a single metric for each increase in the robot's corrected kinematic error, the spatial components of the corrected positional kinematic error were filtered independently using a zero-phase 6th order Butterworth filter with cutoff frequencies ranging between 0.1 Hz and 0.5 Hz”), Woodside does not explicitly teach “wherein at least one of the single integrated data stream and the object positional data stream is” filtered.
Liu further teaches wherein at least one of the single integrated data stream and the object positional data stream is extrapolated to provide additional data points for comparison, and filtered ([0162]: “Implementations can employ extended Kalman filtering (EKF), shown in a general nonlinear model form by equations (1), to extrapolate an initial pose using inertial data from the multi-axis IMU, to generate a propagated pose”).
Regarding claim 12, Woodside in view of Liu and Lucas teaches the method of claim 4.
Lucas further teaches wherein data streams are provided directly into one or more high speed programmable logic controller (PLC) processors for processing and comparison with the object positional data stream ([0028]: “the motion controller 60 also communicates directly with the PLC 62 via, for example, standard input/output protocols”).
Regarding claim 13, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside further teaches wherein the plurality of sensors comprises at least one sensor selected from the group consisting of inertial measurement units (IMUs), laser trackers, laser scanners, cameras, distance systems, probing sensors, accelerometers, and robot encoders ([0033]: “the metrology tracking system has two components, the 6 DoF sensor and the laser tracker… The azimuth and elevation of the beam, determined by the laser tracker's encoders, and the distance of the beam are used to determine the 6 DoF sensor's position,” which corresponds to a distance system; [0032]: “The proprietary trajectory controller utilizes the forward kinematic model of the robot to convert the encoder (joint) measurements into a kinematic position and orientation of its tool flange for use in its control algorithm”).
Regarding claim 14, Woodside in view of Liu and Lucas teaches the method of claim 4.
While Woodside teaches a forward kinematic robot model ([0006]: “The kinematic location is computed from the robot's encoder measurements mapped through the robot's forward kinematic model, that latter being an idealized nonlinear set of equations relating the position of the robot's joints to the location of its tool flange in Euclidian space”), Woodside does not explicitly “further comprising using forward path projections based on the comparison to provide control instructions to one or more objects within an environment for positional correction.”
Lucas further teaches further comprising using forward path projections based on the comparison to provide control instructions to one or more objects within an environment for positional correction ([0021]: “The prediction of the future location of the positioning system using this velocity information assumes that the motion of the positioning system will be approximately straight-line over the time interval of the galvanometer tracking delay”; [0022]: “position feed-forward is used to adjust galvanometer trajectory such that the system follows the predicted path of a measured external motion system”; [0023]: “The predictive adjustment of the command data causes the beam to be steered to the actual position of the moving workpiece thus improving overall laser positioning accuracy”).
Regarding claim 16, Woodside in view of Liu and Lucas teaches the system of claim 1.
Woodside does not explicitly teach “the expected offset is a predicted difference between the projected object positional data stream and the projected single integrated data stream at the future time.”
Lucas further teaches wherein the expected offset is a predicted difference between the projected object positional data stream and the projected single integrated data stream at the future time ([0026]: “A position offset is calculated for the galvanometers that is proportional to the galvanometer tracking delay and the currently calculated positioning system velocity and acceleration 34… The offset is the predictive positional bias that causes the laser to be steered to where the workpiece will be after the tracking delay”).
Regarding claim 17, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside does not explicitly teach “the expected offset is a predicted difference between the projected object positional data stream and the projected single integrated data stream at the future time.”
Lucas further teaches wherein the expected offset is a predicted difference between the projected object positional data stream and the projected single integrated data stream at the future time ([0026]: “A position offset is calculated for the galvanometers that is proportional to the galvanometer tracking delay and the currently calculated positioning system velocity and acceleration 34… The offset is the predictive positional bias that causes the laser to be steered to where the workpiece will be after the tracking delay”).
The reasons to combine Lucas with Woodside in view of Liu are the same as articulated in the rejection of claim 1 above.
Claims 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Woodside et al. (US 2023/0075352 A1), in view of Liu et al. (US 2018/0364731 A1), in view of Lucas et al. (US 2021/0229216 A1), and in view of Hoedt (US 2020/0026296 A1).
Regarding claim 15, Woodside in view of Liu and Lucas teaches the method of claim 4.
While Woodside teaches storing measurements in a lookup table ([0048]: “After conversion, the leading measurements, identified by (3) from the steps in [0040] are stored in a lookup table of sufficient size (constructed using a Last in First Out (LIFO) buffer). Now, the effects of the relative time delay, discussed in [0039], are compensated by matching (Step 2.2) the robot measurements to the tracker measurements producing the set of (matched) measurements, (
T
r
b
k
,
T
s
b
k
,
t
k
k
), for the kth) iteration of the Kinematic Error Control System,” where the lookup table corresponds to a database that is used for course correction), Woodside does not explicitly teach “rolling calibration of machines by post processing of the comparison to feed into a database for course correction based on a prior travel path of the object.”
Hoedt further teaches further comprising rolling calibration of machines by post processing of the comparison to feed into a database for course correction based on a prior travel path of the object ([0040]: “a manipulated variable profile 13 of the iteratively learning controller 5 associated with the planned trajectory 7 is adjusted or modified and then updated in the database 12”; [0053]: “After traversing the planned trajectory and recording the control error in the memory, the manipulated variable profile for the iteratively learning controller is adjusted and updated accordingly in the database”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the system of Woodside in view of Liu and Lucas to incorporate the teachings of Hoedt so as to include rolling calibration of machines by post processing of the comparison to feed into a database for course correction based on a prior travel path of the object. Doing so would allow path specific course correction profiles to be updated using prior control error, with the aim of optimizing repeated operations (Hoedt, [0017]: “For each classified trajectory or the corresponding class, the iteratively learning controller or the manipulated variable profile stored in the database for the corresponding class can thus be adjusted with each new pass through an accordingly classified planned trajectory and in this way can be optimized operation-by-operation”).
Regarding claim 18, Woodside in view of Liu and Lucas teaches the method of claim 4.
Woodside, Liu, and Lucas do not explicitly teach “further comprising storing at least one of the object positional data stream and the single integrated data stream with the expected offset into a database for course correction over time, and providing a feedback loop to generate the control instruction to the object under observation based on a comparison of a current travel path to a previous travel path stored in the database and the expected offset associated with the previous travel path.”
Hoedt further teaches further comprising storing at least one of the object positional data stream and the single integrated data stream with the expected offset into a database for course correction over time ([0016]: “Respective manipulated variable profiles for the classes or clusters are stored in a database for the iteratively learning controller. The respective manipulated variable profiles are called up according to the classification of a planned trajectory and are implemented accordingly by the iteratively learning controller when traversing the planned trajectory”), and
providing a feedback loop to generate the control instruction to the object under observation based on a comparison of a current travel path to a previous travel path stored in the database and the expected offset associated with the previous travel path ([0043-0044]: “adjusting or correcting the manipulated variable 10 for future control while taking into account the control error 11 that has occurred in the past… the control error 11 is represented as the difference between the planned trajectory 7 and the travelled trajectory 21… an adjustment of the correction value 18 applicable to the current cycle can be carried out for each new pass through a planned trajectory 7 or the associated stored trajectory 13”).
The reasons to combine Hoedt with Woodside in view of Liu and Lucas are the same as articulated in the rejection of claim 15 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2016/0320476 A1: Multi-sensor camera/radar object tracking with synchronized data streams, predicted object positions, Kalman filtering, and extrapolation/interpolation
US 2015/0148956 A1: Robot control by detecting an actual trajectory, comparing it to a target trajectory, calculating trajectory error, storing improved trajectories, and using correction amounts in later operations
US 2018/0117758 A1: Robot learning control devices using acceleration sensors, gyro sensors, inertial sensors, laser trackers, cameras, or motion capture devices to determine leading end position error and storing trajectory data and learning correction amounts for different robot operations
US 2004/0093119 A1: High-precision industrial robot control comparing a reference path with a measured outcome path from an external measuring system, calculating path deviation, and iteratively adjusting the robot reference path
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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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