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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/08/2024 was filed and has been considered by the examiner.
Drawings
The drawings that were filed on 07/03/2024 have been considered by the examiner.
Response to Preliminary Amendment
The Preliminary Amendment filed on 12/05/2024 has been considered by the examiner. Claims 1-35 were cancelled by this amendment. The remaining Claims 36-62 are addressed in this office action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 36, 38, 40, 42-55, 57, 59-62 are rejected under 35 U.S.C. 103 as being unpatentable over Pinto et al. (US 10073175 B2), and herein after will be referred to as Pinto, in view of Mahmoud et al. (US 20200158862 A1), herein after will be referred to as Mahmoud.
Regarding Claim 36, Pinto teaches a method for enhanced navigation of a marine platform (100) over a seafloor (200) (A sonar system carried by a naval vehicle to determine its speed relative to the sea bed as a navigation method; Col 5 lines 41-48), comprising:
performing micronavigation displacement measurements and associated coordinate frame calculations (Calculating the coordinate frame angles (trim, sight) and then determining speed (displacement); Col 4 lines 25-44)
by using at least one one-sided or two-sided sonar (Two measuring devices port side and starboard side; Col 9 lines 51-56)
comprising at least one transducer (20 a, 20 b) comprising at least one transmitter (21) configured to transmit pings (Sonar system has a means for transmitting acoustic signal, which is a transmitter configured to transmit pings; Col 5 lines 44-48) and
at least two parallel receiver arrays (22 a, 22 b) each arranged roughly parallel to a travel direction of the marine platform (100), the receiver arrays (22 a, 22 b) being configured to register echo of pings reflected from the seafloor (200) (Two receivers that register the reflected echoes and are in parallel; Col 5 lines 49-60, Col 6 lines 35-42),
wherein the micronavigation displacement measurements are performed along the primary axes of two different coordinate systems in the form of a receiver array (22 a, 22 b) frame and a patch frame (Determining the speed derived from displacement measurements along two different axes, sight axis and longitudinal axis. The longitudinal axis is the receiver array frame and the sight u axis is the patch frame; Col 10-11 lines 66-5),
the patch frame being located at an acoustic center of mass for an instance of seafloor illumination (Echo from the sea bed corresponds to the interference from the diffusers where the acoustic signal in the direction of sight u is the acoustic center of mass; Col 6 lines 14-22);
calculating a three-dimensional orientation of the receiver array (22 a, 22 b) frame relative to the patch frame (The interferometric function determines the angles of orientation of the network antenna (receiver array frame) in relation to the bed and the angle of sight (azimuth/bearing) which defines a 3D orientation; Col 11 lines 6-11).
and the output of the estimator observation model is used to correct navigation data for the marine platform (100) (The navigation filter (estimator) produces an output, a corrected speed vector, that is used to eliminate the projection errors; Col 11 lines 6-13).
Pinto does not explicitly teach calculating accuracies for all micronavigation displacement measurements and associated coordinate frame calculations, wherein the micronavigation displacement measurements are processed in an estimator observation model, modelling the relationship between position, orientation, and velocity of navigation states of the marine platform (100) and the micronavigation displacement measurements, coordinate frames and accuracies.
However, Mahmoud discloses a method for providing an integrated navigation solution by using sensor data and nonlinear state estimation technique (Abstract). Specifically, Mahmoud teaches accounting for errors in three categories; environmental factors that affect radar measurements, sensor errors, and dynamic errors of the vehicle relative to the target ([0096]). Mahmoud further teaches that the measurement error may be modeled as a Gaussian distribution with standard deviation ([0097]). This teaching is equivalent to the claimed limitation of calculating accuracies because the standard deviation is a statistical calculation of measurement accuracy or variance. Mahmoud teaches using a nonlinear state estimation technique on motion sensor data with a nonlinear measurement models and map information ([0068]). This teaching is equivalent to the claimed limitation of an estimator observation model” because the nonlinear measurement model is a type of estimator observation model used in filters. Mahmoud teaches the nonlinear measurement model of the radar measurements is aimed to model the probability of measurements given the knowledge of the map and state of the vehicle at time t ([0089]). This teaching is equivalent to modelling the relationship between position, orientation, and velocity of navigation states of the marine platform because the nonlinear measurement model defines the relationship between the navigation state and the measurement by using the calculated accuracy in its model.
Pinto and Mahmoud are considered to be analogous to the claim invention because they are in the same field of navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using a nonlinear estimator that model sensor measurements based on their calculated accuracy as taught by Mahmoud based on the motivation to improve the accuracy of the navigation filter where sensor measurements are not of equal quality. This provides the benefit of a more reliable navigation method by weighting high quality measurements and discounting the low-quality or noisy measurements to reduce a long-term position drift.
Regarding Claim 38, Pinto and Mahmoud remains as applied in claim 36. Pinto further teaches registering transmit and receive times for pings (Speed is calculated between two phase centers over a period between the transmission and the reception of the wave; Col 8 lines 40-46),
calculating patch frame angles for each ping (Calculating the relative trim after the transmission of the acoustic signal and its reception; Col 10 lines 26-42), and
calculating delta positions for each pair of successive pings enabling reduction of velocity error and hence position error (Correlating the pairs of phase centers from the successive pings to determine the speed, which reduces the velocity error; Col 8 lines 55-60).
Regarding Claim 40, Pinto and Mahmoud remains as applied in claim 36. Pinto further teaches using ping data to estimate the scattering distribution over a patch to estimate azimuth direction for line of sight (The echo (ping data) is the interference from all the diffusers M (the scattering distribution) to the direction of sight u (azimuth/line of sight); Col 6 lines 14-22).
Regarding Claim 42, Pinto and Mahmoud remains as applied in claim 36. Pinto further teaches using the estimator observation model to model the relationship between position, orientation, and velocity of navigation states of the marine platform (100) and the measurements or states from additional sensors (50) (The navigation filter (estimator model) integrates the sonar-derived speed with other measurements including inertial unit; Col 9 lines 57-65).
Regarding Claim 43, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach using the estimator observation model to estimate systematic errors in the measurements and calculations.
However, Mahmoud discloses an integrated navigation system where the nonlinear state estimation uses a motion sensor error model ([0073]) and the stochastic gyroscope drift is modeled with the state vector ([0134]). This teaching is equivalent to the claimed limitation because the gyroscope drift is a systematic error and is a state within the state vector of the nonlinear state estimation technique which forces the filter to estimate this systematic error. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using a nonlinear estimator technique that uses a motion sensor error model to account for drift in the state vector as taught by Mahmoud based on the motivation to improve the navigation accuracy of the vehicle by accounting for systematic errors. This provides the benefit of improving the long term accuracy of the navigation method by estimating and removing the sensor drift.
Regarding Claim 44, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach using a Kalman filter or an extended Kalman filter as the estimator observation model.
However, Mahmoud discloses using a filtering method for the sensor-aided inertial navigation that models errors and their accuracies using a Kalman filter for linear models or an Extended Kalman filter to linearize the models prior to running the filter ([0162]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using a Kalman filter or an extended Kalman filter with the system model as taught by Mahmoud based on a reasonable expectation of success and the motivation to select a well-known type of filter for the navigation methods. This provides the benefit of improving the estimation technique by applying a computationally efficient filter for combining sensor data.
Regarding Claim 45, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach predicting estimates of the estimator observation model and their error covariance between micronavigation displacement measurements and the associated coordinate frame measurements, and updating the different estimates and their error covariance every time a new micronavigation displacement measurement and the associated coordinate frame measurements are registered.
However, Mahmoud discloses that the navigation system uses a state estimation technique with a two-step operational cycle of a prediction phase and an update phase ([0067]). In the prediction stage of the estimator, the state (including error covariance) is propagated between measurements ([0164]). These teachings are equivalent to the claimed limitations of predicting estimates of the estimator observation model and their error covariance because the prediction phase of the estimator is propagated (predicted) between new measurements. Mahmoud further teaches the state estimation technique where the measurement models are used in the update phase ([0067]) and that the measurements are used in the measurement models to compute the probability of scan of measurements which is used to weight the importance of each particle ([0165]). These teachings are equivalent to the claimed limitations of updating the different estimates and their error covariance every time a new measurement is registered because the update phase uses new radar measurements and is used (registered) to re-calculate the importance weight of each particle. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of the operational steps of a prediction phase and an update phase as taught by Mahmoud based on a reasonable expectation of success of a recursive estimator and provides the benefit of the navigation filter integrating new measurements over time.
Regarding Claim 46, Pinto and Mahmoud remains as applied in claim 36. Pinto further teaches converting the micronavigation displacement measurements to estimator observation model calculations by converting displacements in combination with transmit and receive times to velocities (The velocity is calculated by dividing the delta movement over the delta period in time and is stated between the transmission and reception; Col 8 lines 40-45).
Regarding Claim 47, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach using displacement accuracies either directly or indirectly by converting displacement accuracies in combination with transmit and receive times to velocity accuracies.
However, Mahmoud discloses a state estimation or filtering technique that includes a prediction and updated phased used in the system model ([0067]) where the state of the device is derived from a function that includes the latitude, longitude, altitude, and directional velocities of the state vector of the vehicle used with Kalman filter ([0115]). This teaching is equivalent to the claimed limitation because the estimator model includes both displacement, as latitude, longitude, and altitude for position, and velocity as states. The prediction phase of the estimator is a fundamental operation that propagates or converts the error covariance (the accuracies) of the displacement state to the velocity state over the time step. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of the estimation technique that models position and velocity and propagates the error covariance based on the motivation to create a navigation filter that models the relationship between position and velocity for the navigation system. This provides the benefit of improving the navigation system that correctly tracks the error propagation between the related navigation states.
Regarding Claim 48, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach performing micronavigation lever arm compensation as part of the estimator observation model calculations.
However, Mahmoud explicitly teachings performing lever arm compensation by calculating the sensor’s position from the platform’s state and its local offset and using the calculation as part of the closed-form measurement model ([0102] [0110]). This teaching is equivalent to the claimed limitation because the measurement model directly calculates the sensor’s true position by applying the lever arm compensation formula using the platform state and local offset as part of its calculation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of calculating the sensor’s position from the platform’s state and local offset as part of the closed-form measurement model as taught by Mahmoud based on a reasonable expectation of success and the motivation to correct the positioning and velocity errors caused by physical separation between the different sensors. This provides the benefit of improving the accuracy of the positioning and velocity data of the navigation system.
Regarding Claim 49, Pinto and Mahmoud remains as applied in claim 48. Pinto further teaches a dynamic part due to varying overlap caused by surge motion of the marine platform (100) (DPCA method determines the speed by finding the maximum correlation between the pairs of phase centers that have moved due to the platforms surge motion, this correlation is the measurement of varying overlap; Col 8 lines 55-61).
Pinto does not explicitly teach calculating the lever arm compensation by a static part from mechanical offsets from a navigation aiding apparatus origin to the transmitter (21) and multiple receiver arrays (22 a, 22 b) of the sonar.
However, Mahmoud explicitly teaches calculating the sensor’s position using a static part of the radar’s position relative to the platform’s local frame, which represents a mechanical offset from the platform’s frame ([0102]). This teaching is equivalent to the claimed limitation because the sensor position is calculated using the relative position from the global frame for a mechanical offset. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of measuring the surge motion with the DPCA method of Pinto to incorporate the teachings of calculating the mechanical offset of the lever arm as taught by Mahmoud based on the motivation to properly correlate the sonar’s measurements to the platform’s navigation frame. This provides the benefit of a more accurate navigation solution by accounting for the sensor’s fixed mounting position and its dynamic motion.
Regarding Claim 50, Pinto and Mahmoud remains as applied in claim 36. Pinto does not explicitly teach accounting for three-dimensional rotation between the receiver array (22 a, 22 b) frame and the patch frame by rotation of the patch frame relative to the receiver array (22 a, 22 b) frame between transmit and receive times for two consecutive pings.
However, Mahmoud teaches an estimator model that explicitly calculates the change in orientation in a three-dimensional rotation by integrating angular rates over the time interval between two consecutive pings ([0151]). This teaching is equivalent to the claimed limitation because the estimator model calculates the three-dimensional rotations of theta x, y, and z by integrating the angular rates “omega” over the time interval of delta t, which is the time between two consecutive pings. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of calculating the change in orientation by angular rates over time intervals as taught by Mahmoud based on the motivation to properly propagate the navigation state orientation from one measurement time to the next time interval. This provides the benefit of improving the accuracy of the navigation state that accounts for the vehicle’s rotational motion between measurements.
Regarding Claim 51, Pinto and Mahmoud remains as applied in claim 50. Pinto does not explicitly teach using the estimator observation model to describe a connection between navigation states of the marine platform (100), errors of the navigation states of the marine platform (100), and accuracies of the micronavigation displacement measurements and rotation of the patch frame relative to the receiver array (22 a, 22 b) frame.
However, Mahmoud teaches the state estimation is composed of navigational states of position, velocity, and orientation of the device ([0115]) where the state of the device can be a contained within the platform of a vessel ([0035]). This teaching is equivalent to the claimed estimator observation to describe a connection between navigational states of the marine platform because the state of the device includes navigational states of position, velocity, and orientation and is contained within the platform of a marine vessel. Mahmoud further teaches an error state estimator model where the state vector is the error of the navigation states ([0131]). This teaching is equivalent to the claimed estimator observation model to describe a connection between errors of the navigation states of the marine platform because the error state system model consists of the errors in the navigation states of the device within the platform of a marine vessel. Furthermore, Mahmoud teaches the estimator model includes accuracies of the displacement measurements by modeling the variance of the measurement model ([0100]) and that the error state consists of errors in the rotation matrix that transforms from the device body frame to the local-level frame ([0131]). These teachings are equivalent to the claimed limitation of accuracies of the micronavigation displacement measurements and rotation of the patch frame relative to the receiver array frame because the variance of the measurement model determines the accuracy of the displacement measurements and determining how spread out the data set is from the mean and the error state accounting for errors of rotation from the device body to the level-level frame. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using the state estimator model to relate the navigational states of position, velocity, and orientation of the device, the errors of the navigational states, and modeling the variance of the measurement model along with errors in the rotation matrix as taught by Mahmoud based on the motivation to account for various sources of errors in the marine platform and improve the accuracy of the navigational method.
Regarding Claim 52, Pinto and Mahmoud remains as applied in claim 51. Pinto does not explicitly teach calibrating apparatus parameters, hereunder scale factor errors and transducer alignment errors, by incorporating additional states in the estimator observation model.
However, Mahmoud explicitly teaches calculating the pitch misalignment angle, transducer alignment error, by incorporating a state estimation technique ([0214]) and using measurements and amending the states of the main filter to include the pitch misalignment state ([0215]). These teachings are equivalent to calibrating apparatus parameters, transducer alignment errors, by incorporating additional states in the estimator observation model because the states are amended to include the pitch misalignment angle to account for the error. Mahmoud further teaches heading misalignments where the accelerometer readings are correct for sensor errors such as biases, scale factors, and non-orthogonalities ([0203]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of calculating the pitch misalignment angel and amend the states of the filter to include the pitch misalignment state and correcting the sensor errors for scale factors as taught by Mahmoud based on the motivation to improve the accuracy of the estimator and account for real world sensor imperfections. This provides the benefit of an accurate navigation solution that accounts for sensor errors through calibration and or scale factor drifts.
Regarding Claim 53, Pinto and Mahmoud remains as applied in claim 47. Pinto does not explicitly teach calculating an observation noise matrix based on the calculated accuracies alone or in combination with configuration parameters by parametrizing an observation equation around a navigation equation solution and patch angles.
However, Mahmoud teaches that the measurement error can be modeled as a Gauassian distribution and standard deviation where accuracy of a sensor measurement is calculated and an Adaptive Measurement Model that models different error sources like environmental or dynamic factors to compute the measurement’s variance ([0096]-[0097], [0100]). This teaching is equivalent to the claimed limitations of calculating an observation noise matrix based on calculated accuracies in combination with configuration parameters because the standard deviation defines the observation matrix through calculation of the variance and is calculated with factors such as environmental or dynamic factors. Mahmoud further teaches that when a non-linear model is used, Extended Kalman Filter, which linearizes the model around the current state vector of position, velocity, and orientation ([0115], [0162]). This teaching is equilvanet to the claimed parametrizing an obersation equation around a navigation equation solution and patch angels because linearizing of the Extended Kalman Filter is parametrizing and is done to the nonlinear model around the state vector which contains patch angles. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using the Adaptive Measurement Model that computes the measurements variance and the Extended Kalman Filter to linearize the non-linear model around the current state vectors as taught by Mahmoud based on the motivation to provide the Extended Kalman Filter the necessary and required parametrizing and observation equation around the navigation solution and provides the benefit of improving the accuracy of the filter.
Regarding Claim 54, Pinto and Mahmoud remains as applied in claim 46. Pinto further teaches converting the micronavigation displacement measurements to a velocity measurement applicable in a preset or desired time interval (Covering the delta movement to speed (velocity) over a period of time; Col 8 lines 40-45).
Regarding Claim 55, Pinot teaches an apparatus (10) for providing enhanced navigation of a marine platform (100) over a seafloor (200) (A sonar system carried by a naval vehicle to determine its speed relative to the sea bed as a navigation method; Col 5 lines 41-48), comprising
at least one one-sided or two-sided sonar with at least one transmitter (21) configured to transmit pings and at least two parallel receiver arrays (22 a, 22 b) each arranged roughly parallel to a travel direction of the marine platform (100), the receiver arrays (22 a, 22 b) being configured to register echo of pings reflected from the seafloor (200) (The sonar system with a transmitter and two parallel receiver arrays that are parallel to the travel direction that receive reflect echoes; Col 9 lines 51-56, Col 5 lines 44-60, Col 6 lines 35-42),
the sonar being configured to perform micronavigation displacement measurements and associated coordinate frame calculations (Calculating the coordinate frame angles (trim, sight) and then determining speed (displacement); Col 4 lines 25-44); and
a navigation processor (40) provided with means or software for calculating three-dimensional orientation of the receiver array (22 a, 22 b) frame relative to the patch frame (The interferometric function determines the angles of orientation of the network antenna (receiver array frame) in relation to the bed and the angle of sight (azimuth/bearing) which defines a 3D orientation; Col 11 lines 6-11);
the output of the estimator observation model is used for correction of navigation data for the marine platform (100) (The navigation filter (estimator) produces an output, a corrected speed vector, that is used to eliminate the projection errors; Col 11 lines 6-13), and
the apparatus (10) is configured to perform the micronavigation displacement measurements along the primary axes of two different coordinate systems in the form of a receiver array (22 a, 22 b) frame and patch frame (Determining the speed derived from displacement measurements along two different axes, sight axis and longitudinal axis. The longitudinal axis is the receiver array frame and the sight u axis is the patch frame; Col 10-11 lines 66-5), the patch frame being located at the acoustic center of mass for an instance of seafloor illumination (Echo from the sea bed corresponds to the interference from the diffusers where the acoustic signal in the direction of sight u is the acoustic center of mass; Col 6 lines 14-22).
Pinto does not explicitly teach calculating accuracies for all micronavigation displacement measurements and associated coordinate frame calculations, the navigation processor (40) further comprising an estimator observation model configured to process the micronavigation displacement measurements, modelling the relationship between position, orientation, and velocity of navigation states of the marine platform (100) and the micronavigation displacement measurements, coordinate frames and accuracies.
However, Mahmoud discloses a method for providing an integrated navigation solution by using sensor data and nonlinear state estimation technique (Abstract). Specifically, Mahmoud teaches accounting for errors in three categories; environmental factors that affect radar measurements, sensor errors, and dynamic errors of the vehicle relative to the target ([0096]). Mahmoud further teaches that the measurement error may be modeled as a Gaussian distribution with standard deviation ([0097]). This teaching is equivalent to the claimed limitation of calculating accuracies because the standard deviation is a statistical calculation of measurement accuracy or variance. Mahmoud teaches using a nonlinear state estimation technique on motion sensor data with a nonlinear measurement models and map information ([0068]). This teaching is equivalent to the claimed limitation of an estimator observation model” because the nonlinear measurement model is a type of estimator observation model used in filters. Mahmoud teaches the nonlinear measurement model of the radar measurements is aimed to model the probability of measurements given the knowledge of the map and state of the vehicle at time t ([0089]). This teaching is equivalent to modelling the relationship between position, orientation, and velocity of navigation states of the marine platform because the nonlinear measurement model defines the relationship between the navigation state and the measurement by using the calculated accuracy in its model.
Pinto and Mahmoud are considered to be analogous to the claim invention because they are in the same field of navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto to incorporate the teachings of using a nonlinear estimator that model sensor measurements based on their calculated accuracy as taught by Mahmoud based on the motivation to improve the accuracy of the navigation filter where sensor measurements are not of equal quality. This provides the benefit of a more reliable navigation method by weighting high quality measurements and discounting the low-quality or noisy measurements to reduce a long-term position drift.
Regarding Claim 57, Pinto and Mahmoud remains as applied in claim 55. Pinto further teaches a sonar processor (30) configured for performing micronavigation displacement measurements between sonar transmissions and a coordinate frame for each such measurement (The data processing device computes the speed of movement by data received by the two reception means; Col 5 lines 65-67).
Regarding Claim 59, Pinto and Mahmoud remains as applied in claim 57. Pinto further teaches correlating signals between overlapping phase centers (The data processing device is configured to find the maximum correlation between pairs of phase centers; Col 8 lines 52-61).
Regarding Claim 60, Pinto and Mahmoud remains as applied in claim 55. Pinto further teaches the estimator observation model is configured to model the relationship between position, orientation, and velocity of navigation states of the marine platform (100) and the measurements or states from additional sensors (50) (The navigation filter (estimator model) integrates the sonar-derived speed with other measurements including inertial unit; Col 9 lines 57-65).
Regarding Claim 61, Pinto and Mahmoud remains as applied in claim 55. Pinto further teaches registering and converting the micronavigation displacement measurements to estimator measurements by converting micronavigation displacements in combination with transmit and receive times to velocities (The velocity is calculated by dividing the delta movement over the delta period in time and is stated between the transmission and reception; Col 8 lines 40-45).
Regarding Claim 62, Pinto and Mahmoud remains as applied in claim 55. Pinto further teaches converting the micronavigation displacement measurements to a velocity measurement applicable in a preset or desired time interval (Covering the delta movement to speed (velocity) over a period of time; Col 8 lines 40-45).
Claim(s) 37, 39, and 56 are rejected under 35 U.S.C. 103 as being unpatentable over Pinto in view of Mahmoud, as applied in claim 36 and 55, and in further view of Rikoski et al. (US 20140328141 A1), herein after will be referred to as Rikoski.
Regarding Claim 37, the prior art combination remains as applied in claim 36. The prior art combination does not explicitly teach the output of the estimator observation model is used as input for a controller or control system controlling motion of the marine platform (100) or a vessel or craft towing the marine platform (100).
However, Rikoski discloses a sonar navigation system for autonomous underwater vehicles that uses navigation data to control the vehicle on the central control unit (CCU) ([0043] [0044]). This teaching is equivalent to the claimed limitation because the CCU uses the navigational parameters for controlling the movement of the vehicle.
Pinto, Mahmoud, and Rikoski are considered to be analogous to the claim invention because they are in the same field of navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto and Mahmoud to incorporate the teachings of using the navigation data as the input for the vehicle motion controller as taught by Rikoski based on the motivation to use the corrected navigation data to improve the accuracy and guidance of vehicle navigation system. This provides the benefit of improving the autonomous navigation system and an accurate path for the vehicle to follow.
Regarding Claim 39, the prior art combination remains as applied in claim 36. The prior art combination does not explicitly teach correlating along-track elements to estimate azimuth direction for line of sight.
However, Rikoski discloses a sonar navigation system that uses beamforming on a receiver array to estimate signal direction where the sonar unit includes a transducer array with receiving elements arranged in a row ([0049]). Rikoski further teaches performing matched filtering in azimuth for direction estimation ([0052]). These teachings are equivalent to the claimed limitations because the receiving elements arranged in a row are along track elements and beamforming along with matched filtering in azimuth are techniques used to perform direction estimation. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Pinto and Mahmoud to incorporate the teachings of the beamforming method using transducer array arranged in a row and matched filtering as taught by Rikoski based on the motivation to improve the angle estimation and the measurement of the patch frame’s orientation.
Regarding Claim 56, the prior art combination remains as applied in claim 55. The prior art combination does not explicitly teach the calculated corrections are provided to a controller or control system controlling motion of the marine platform (100) or a vessel or craft towing the marine platform (100).
However, Rikoski discloses a sonar navigation system for autonomous underwater vehicles that uses navigation data to control the vehicle on the central control unit (CCU) ([0043] [0044]). This teaching is equivalent to the claimed limitation because the CCU uses the navigational parameters for controlling the movement of the vehicle.
Pinto, Mahmoud, and Rikoski are considered to be analogous to the claim invention because they are in the same field of navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto and Mahmoud to incorporate the teachings of using the navigation data as the input for the vehicle motion controller as taught by Rikoski based on the motivation to use the corrected navigation data to improve the accuracy and guidance of vehicle navigation system. This provides the benefit of improving the autonomous navigation system and an accurate path for the vehicle to follow.
Claim 41 is rejected under 35 U.S.C. 103 as being unpatentable over Pinto in view of Mahmoud, as applied in claim 36, and in further view of Jean et al. (US 7933167 B2), herein after will be referred to as Jean.
Regarding Claim 41, Pinto and Mahmoud remains as applied in claim 36. Pinto further teaches
using the effective seafloor (200) slope together with line of sight and a normal vector of the seafloor (200) to determine plane of sight, thereby determining the patch frame defined by Y-axis along the line of sight, with the X-axis pointing along the seafloor (200) and with the Z-axis normal to these in the plane of sight (Determining the angle of sight (line of sight) and relative trim angle derived from the normal vector/seafloor slope to define the orientation for speed calculations; Col 3 lines 13-22, Col 11 lines 1-5).
Pinto and Mahmoud does not explicitly teach using ping data to estimate the seafloor (200) depth at multiple azimuth directions and ranges to estimate the effective seafloor (200) slope.
However, Jean discloses a synthetic aperture sonar system that uses an algorithm for forming a bathymetric image of the seafloor (Col 6 lines 7-10). This teaching is equivalent to the claimed limitation because a bathymetric image is a map of the seafloor at depth at multiple azimuth directions and ranges, which is necessary data to estimate the seafloor slope.
Pinto, Mahmoud, and Jean are considered to be analogous to the claim invention because they are in the same field of sonar-based navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto and Mahmoud to incorporate the teachings of using an algorithm for forming a bathymetric image of the seafloor as taught by Jean based on the motivation to estimate the slope of the seafloor. This provides the benefit of improving the accuracy of the navigation method by accounting for the changing depth of the seafloor.
Claim 58 is rejected under 35 U.S.C. 103 as being unpatentable over Pinto in view of Mahmoud, as applied in claim 55, and in further view of Dickey et al. (US 4244026 A), herein after will be referred to as Dickey.
Regarding Claim 58, Pinto and Mahmoud remains as applied in claim 55. Pinto and Mahmoud does not explicitly teach a trigger control unit (60) configured to control a trigger signal for the at least one transmitter (21) each time the marine platform (100) and navigation aiding apparatus (10) is estimated to have travelled a fixed distance (D) in an earth fixed coordinate system based on velocity estimates from the navigation processor (40).
However, Dickey discloses a sonar system where the time interval between pings is controlled by a microprocessor based on the ship’s estimated speed where the timing circuits of the system include a clock generator which is controlled by the microprocessor (Col 11 lines 50-55). Dickey further teaches that the time interval is selected by the microprocessor using criteria involving the ship’s speed (Col 11 lines 55-58) and that the outputs of the microprocessor provide calculated velocity estimates (Col 14 lines 4-7). The predicted velocity is used to compute an optimum value of the scaling factor T which controls the repetition interval of the transmitter (Col 17 lines 39-48). These teachings are equivalent to the claimed limitations because the microprocessor provides a predicted velocity, that is equivalent to the estimated velocity, to the controller that computes the repetition interval, that is equivalent to a trigger signal, after a fixed distance to achieve the optimal spatial ping separation.
Pinto, Mahmoud, and Dickey are considered to be analogous to the claim invention because they are in the same field of sonar-based navigation systems. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Pinto and Mahmoud to incorporate the teachings of a trigger control unit that bases its ping intervals on the velocity provided by the processor as taught by Dickey based on the motivation to optimize the ping spacing of the sonar system. This provides the benefit of a optimizing the sonar pings and ensuring that the pings are transmitted at an optimal distance.
Prior Art
The prior art made of record and not relied upon is considered pertinent, most relevant, to applicant's disclosure.
Billion (US 6304513 B1)
Mathews (US 10371806 B2)
Conclusion
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/EDWARD ANDREW IZON DIZON/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663