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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-7 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reimer et al. (US 2022/0196852 A1), hereinafter “Reimer”.
As per claim 1 and 20, Reimer teaches method for estimating the position and attitude of a payload mounted to an unmanned vehicle (UV) with a mount, comprising: collecting positioning data from sensors on the UV ([see at least Fig. 5, 0012-0014, 0022, 0023 and 0080], Reimer teaches an unmanned aerial vehicle includes GNSS receiver 100 (i.e., “sensors”) for receiving positioning data); synchronizing the collected positioning data with an onboard processor of the payload as synchronized sensor data ([see at least Fig. 2, 0034 and 0046-0050], Reimer teaches the computing system (“onboard processors”) can include time synchronization to account for latencies between data sources. The satellite observations can be measured by a GNSS receiver, and are associated with observation timestamp. Synchronizing the sensor data and GNSS observations functions to align the sensor data and GNSS observation is performed by a computing system); uploading a dynamic model for a specific type of payload mounted to the UV ([see at least 0029-0031 and 0080], Reimer teaches the relative pose (e.g., a ‘leverarm’) between each GNSS receiver antenna, each sensor, and/or each GNSS receiver/sensor pair can be accounted for in a measurement covariance (e.g., within a measurement model that is processed as part of a filter). When multiple leverarms are present (e.g., three or more total antenna and/or sensors), each leverarm can be accounted for in a measurement covariance and an antenna/antenna leverarm can be accounted for as a state of a filter. However, the leverarm can otherwise be accounted for, modeled, and/or determined. Reimer also teaches at [0080] the sensor engine includes a mechanization model 222 (e.g., built on a physical dynamic model that gets discretized, a set of equations or relationships to determine kinematic parameters from sensor data) The sensor fusion system can be mounted to an external system (e.g., robot, vehicle, agriculture equipment)); processing the synchronized sensor data and measurements received from a dedicated IMU of the payload and system parameters defined by the dynamic model using an Extended Kalman Filter (EKF) to estimate the position and attitude of the payload ([see at least Fig.2, 0026-0028, 0034-0035, 0064-0068, 0071 and 0080], Reimer teaches the sensor data can include inertial data. Applying an error estimator to merge the kinetic parameters. Kinematic parameters can be fused using an extended Kalman filter. Inputs to the error estimator can include: kinematic parameters determined from sensor data (i.e., “measurement received from a dedicated inertial measurement unit”) and GNSS observation (i.e., “synchronized sensor data”.) The state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload); outputting the position and attitude of the payload based on the processed data as a predicted payload position and attitude ([see at least 0065 and 0071], Reimer teaches the state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload. The sensor corrections output by the error estimator are used to correct and/or update the sensor data (e.g., provide a historic correction to previous data, update the systematic sensor error corrections to correct current or future sensor readings)); and correcting raw target data from the payload using the estimated position and attitude to produce precise data ([see at least 0065 and 0071], Reimer teaches the state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload. The sensor corrections output by the error estimator are used to correct and/or update the sensor data (e.g., provide a historic correction to previous data, update the systematic sensor error corrections to correct current or future sensor readings)).
As per claim 2, Reimer teaches method, wherein the collected positioning data includes data from a Global Navigation Satellite System (GNSS) receiver ([see at least Fig. 5, 0012-0014, 0022, 0023 and 0080], Reimer teaches an unmanned aerial vehicle includes GNSS receiver 100 (i.e., “sensors”) for receiving positioning data).
As per claim 3, Reimer teaches method, wherein the collected positioning data includes data from an Inertial Measurement Unit (IMU) ([see at least Fig.2, 0026-0028, 0034-0035, 0064-0068, 0071 and 0080], Reimer teaches the sensor data can include inertial data. Applying an error estimator to merge the kinetic parameters. Kinematic parameters can be fused using an extended Kalman filter. Inputs to the error estimator can include: kinematic parameters determined from sensor data (i.e., “measurement received from a dedicated inertial measurement unit”) and GNSS observation (i.e., “synchronized sensor data”.) The state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload).
As per claim 5, Reimer teaches method, wherein the synchronizing the collected positioning data includes aligning timestamps of the sensors on the UV with timestamps of the onboard processor of the payload ([see at least Fig. 2, 0034 and 0046-0050], Reimer teaches the computing system (“onboard processors”) can include time synchronization to account for latencies between data sources. The satellite observations can be measured by a GNSS receiver, and are associated with observation timestamp. Synchronizing the sensor data and GNSS observations functions to align the sensor data and GNSS observation is performed by a computing system).
As per claim 6, Reimer teaches method, wherein the payload is at least one of a camera or LIDAR ([see at least 0028 and 0044]).
As per claim 7, Reimer teaches method, further comprising: continuously updating the predicted payload position and attitude with new sensor data obtained during the operation of the UV ([see at least 0034, 0035 and 0064-0068]).
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 8-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Reimer in view of Johannesson et al. (US 2021/0053679 A1), hereinafter “Johannesson”.
As per claim 8, Reimer teaches system for estimating the position and attitude of a payload mounted to an unmanned vehicle (UV), the system comprising: an unmanned vehicle (UV) ([see at least Fig. 5, 0012-0014, 0022, 0023 and 0080], Reimer teaches an unmanned aerial vehicle includes GNSS receiver 100 (i.e., “sensors”) for receiving positioning data), comprising: a sensor configured to collect positioning data ([see at least 0080-0081], Reimer teaches a sensor fusion system can include a sensor 300. The sensor fusion system can be mounted to an external system (e.g., robot, vehicle, agricultural equipment) to determine position, velocity attitude of a moving body); a payload communicatively coupled to the UV ([see at least 0080-0081], Reimer teaches a sensor fusion system can include a sensor 300. The sensor fusion system can be mounted to an external system (e.g., robot, vehicle, agricultural equipment) to determine position, velocity attitude of a moving body), comprising: a processor configured to obtain positioning data from the UV in a synchronized manner as synchronized positioning data ([see at least Fig. 2, 0034 and 0046-0050], Reimer teaches the computing system (“onboard processors”) can include time synchronization to account for latencies between data sources. The satellite observations can be measured by a GNSS receiver, and are associated with observation timestamp. Synchronizing the sensor data and GNSS observations functions to align the sensor data and GNSS observation is performed by a computing system), a dedicated Inertial Measurement Unit (IMU) configured to capture motion-related data ([see at least 0028], Reimer teaches the sensors can include inertial measurement unit (IMU)) to capture motion-related data), and an Extended Kalman Filter (EKF) module configured to process the synchronized positioning data, motion-related data and system parameters defined by a dynamic model to estimate the position and attitude of the payload ([see at least Fig.2, 0026-0028, 0034-0035, 0064-0068, 0071 and 0080], Reimer teaches the sensor data can include inertial data. Applying an error estimator to merge the kinetic parameters. Kinematic parameters can be fused using an extended Kalman filter. Inputs to the error estimator can include: kinematic parameters determined from sensor data (i.e., “measurement received from a dedicated inertial measurement unit”) and GNSS observation (i.e., “synchronized sensor data”.) The state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload); and the dynamic model uploaded to the system corresponding to the type of the payload, defining the system parameters ([see at least 0029-0031and 0080], Reimer teaches the relative pose (e.g., a ‘leverarm’) between each GNSS receiver antenna, each sensor, and/or each GNSS receiver/sensor pair can be accounted for in a measurement covariance (e.g., within a measurement model that is processed as part of a filter). When multiple leverarms are present (e.g., three or more total antenna and/or sensors), each leverarm can be accounted for in a measurement covariance and an antenna/antenna leverarm can be accounted for as a state of a filter. However, the leverarm can otherwise be accounted for, modeled, and/or determined. Reimer also teaches at [0080] the sensor engine includes a mechanization model 222 (e.g., built on a physical dynamic model that gets discretized, a set of equations or relationships to determine kinematic parameters from sensor data) The sensor fusion system can be mounted to an external system (e.g., robot, vehicle, agriculture equipment)).
Reimer does not explicitly teach “a mount connecting the payload to the unmanned vehicle, configured to allow specific degrees of freedom and having damping properties” as claimed. However, Johannesson teaches an unmanned vehicle which includes a mount for mounting a payload, and a damper to inhibit transmission of vibrations a t[0005]-[0006]. Thus, it would have been obvious to one of ordinary skill in the art to combine Johannesson with Reimer’s teaching by mounting a sensor on a mount having specific degrees of freedom and damping properties to inhibit transmission of vibrations because “a sensor in a high-vibration environment may provide erroneous measurement”, as suggested by Johannesson at [0004]-[0005].
As per claim 9, Reimer teaches system, further comprising an autopilot of the UV configured to process the collected sensor data and to determine the position of the UV ([see at least 0071-0079]).
As per claim 10, Reimer teaches system, wherein the processor is further configured to obtain the determined UV position as positioning data ([see at least 0034, 0035 and 0064-0068]).
As per claim 11, Reimer teaches system, wherein the autopilot system of the UV is further configured to receive feedback from the EKF to adjust flight control for optimized target data acquisition ([see at least 0069]).
As per claim 12, Reimer teaches system, wherein the synchronized positioning data includes an alignment of timestamps ([see at least Fig. 2, 0034 and 0046-0050], Reimer teaches the computing system (“onboard processors”) can include time synchronization to account for latencies between data sources. The satellite observations can be measured by a GNSS receiver, and are associated with observation timestamp. Synchronizing the sensor data and GNSS observations functions to align the sensor data and GNSS observation is performed by a computing system).
As per claim 13, Reimer teaches system, further comprising a target data sensor configured to collect raw target data ([see at least 0060, 0065, 0067 and 0070]).
As per claim 14, Reimer teaches system, further comprising a data corrector configured to correct raw target data using the estimated position and attitude of the payload to produce precise data ([see at least 0060, 0065, 0067 and 0070]).
As per claim 15, Reimer teaches system, wherein the sensor is Global Navigation Satellite System (GNSS) receiver([see at least Fig. 5, 0012-0014, 0022, 0023 and 0080], Reimer teaches an unmanned aerial vehicle includes GNSS receiver 100 (i.e., “sensors”) for receiving positioning data).
As per claim 16, Reimer teaches system, wherein the sensor is Inertial Measurement Unit (IMU) ([see at least Fig.2, 0026-0028, 0034-0035, 0064-0068, 0071 and 0080], Reimer teaches the sensor data can include inertial data. Applying an error estimator to merge the kinetic parameters. Kinematic parameters can be fused using an extended Kalman filter. Inputs to the error estimator can include: kinematic parameters determined from sensor data (i.e., “measurement received from a dedicated inertial measurement unit”) and GNSS observation (i.e., “synchronized sensor data”.) The state output from the error estimator can include sensor error corrections (e.g., systematic sensor error corrections, sensor bias, accelerometer bias, gyroscope bias), kinematic parameters (e.g., position; velocity, attitude) and covariances, which characterizes the level of uncertainty in a position and attitude estimates for the payload).
As per claim 17, Reimer teaches system, wherein the sensor is a compass ([see at least 0028, 0042, 0046 and 0080]).
As per claim 19, Reimer teaches system, wherein the payload is at least one of a camera or LIDAR ([see at least 0028 and 0044]).
Allowable Subject Matter
Claims 4 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The closest reference on the record do not disclose the independent claims to include wherein the dynamic model is configured to mechanical constraints of the mount, including degrees of freedom and damping properties.
Conclusion
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/YAZAN A SOOFI/Primary Examiner, Art Unit 3668