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 is a Final Action on the Merits. Claims 1-2 and 4-20 are currently pending and are addressed below.
Response to Amendments
The amendment filed on December 9th, 2025 has been considered and entered. Accordingly, claims 1-2, 13, 18, and 20 have been amended. Claim 3 has been cancelled.
Response to Arguments
The previous rejection of claims 1-2 and 4-20 under 35 USC 101 have been overcome due to the applicant’s amendments.
The applicant’s arguments with respect to claims 1-2 and 4-20 have been considered but are moot in view of the newly formulated grounds of rejection necessitated by the applicant’s amendments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6-11, 13, 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Brannstrom (US 20160377437 A1) (Brannstrom) in view of Natroshvili (US 20130035855 A1) (“Natroshvili”).
With respect to claim 1, Brannstrom teaches a computer-implemented method comprising:
determining, by a processor, a pair of position measurements of a point feature, wherein each of the position measurements is computed from a global position measurement made using a first sensor (See at least Brannstrom Paragraphs 56-57 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system. Thus the external sensors are typically sensors providing visual measurements of the surroundings, such as of road signs, guard rails, position of landmarks, etc. The satellite navigation system may be the global positioning system (GPS) and/or any other satellite navigation system.”) and a relative position measurement made using a second sensor (See at least Brannstrom Paragraph 11 “The position estimate computed by the first computation unit is performed using data from at least an inertial measurement unit, an IMU, ”), and wherein the position measurements of the pair are made within a time threshold or a distance threshold (See at least Brannstrom FIG. 4 and Paragraphs 93-98 “FIG. 4 illustrates a flow chart of a method for improving positioning accuracy of an autonomous vehicle (203) driving on a road (305), where the method is performed in a unit (100). The method comprises: computing (S1) a first position of the vehicle on the road at a time T1 in a first computation unit, where the computation is performed using data from at least an inertial measurement unit (IMU); computing (S2) a second position of the vehicle on the road at the time T1 in a second computation unit, where the computation is performed using data from at least one external sensor and a map”);
determining, by the processor, a difference between the first position measurement and the second position measurement; providing, by the processor, an error as an output to indicate a measurement error of the second sensor used to determine the relative position measurement (See at least Brannstrom FIG. 4 and Paragraph 93-98 “correcting (S4) an error parameter of at least the IMU in a correction unit, where the error parameter is used for correcting a third position of the vehicle computed by the first computation unit at a time T2 with the computed position difference at time T1, if the second computation unit is unable to compute a fourth position of the vehicle at the time T2; and deciding (S5) the position of the vehicle on the road in a positioning unit”).
Brannstrom, however, fails to explicitly disclose computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix.
Natroshvili teaches computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix (See at least Natroshvili Paragraphs 32-33 “The Kalman filter as used in the navigation system 20 of FIG. 1 and the method of FIG. 2 includes a first filter, a second filter, and a third filter. The first filter receives the satellite positioning data and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix. The second filter receives the vehicle sensor data and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix. The third filter receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generates a combined state vector estimate and a corresponding combined state error covariance matrix therefrom. The third filter comprises a prediction processor implemented on the basis of a state transition model. The prediction processor generates a predicted state vector estimate on the basis of the combined state vector estimate. Further, the prediction processor generates a predicted state error covariance matrix on the basis of the combined state error covariance matrix. The predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter. Accordingly, in this Kalman filter the first filter may determine the first state vector estimate by updating the fed back predicted state vector estimate with the received satellite positioning data. Further, the first filter may determine the first state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the satellite positing data. Similarly, the second filter may determine the second state vector estimate by updating the fed back predicted state vector estimate with the received vehicle sensor data. Further, the second filter may determine the second state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the vehicle sensor data. The third filter may determine the combined state error covariance matrix on the basis of the fed back predicted state error covariance matrix. Further, the third filter may determine the combined state vector estimate on the basis of the fed back predicted state vector estimate and the fed back predicted state error covariance matrix. The state transition model used by the prediction processor may be based on a linear state transition matrix.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom to include computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix, as taught by Natroshvili as disclosed above, such that the error is provided as an output to indicate a measurement error of the second sensor used to determine the relative position measurement, in order to ensure an accurate vehicle position (Natroshvili Paragraph 6 “Accordingly, there is a need for techniques which allow for efficiently improving accuracy of position estimation using satellite positioning data.”).
With respect to claim 6, Brannstrom in view of Natroshvili teaches that the first sensor used to make the global position measurement is associated with a vehicle or a device, and wherein the second sensor is mounted to the vehicle or the device (See at least Brannstrom Paragraphs 56-57 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system. Thus the external sensors are typically sensors providing visual measurements of the surroundings, such as of road signs, guard rails, position of landmarks, etc. The satellite navigation system may be the global positioning system (GPS) and/or any other satellite navigation system.” | Paragraph 11 “The position estimate computed by the first computation unit is performed using data from at least an inertial measurement unit, an IMU,”).
With respect to claim 7, Brannstrom in view of Natroshvili teaches determining a pose of the vehicle or the device, wherein the error of the relative position measurement is further based on the pose of the vehicle or the device (See at least Brannstrom FIG. 4 and Paragraph 91 “FIG. 3 shows a schematic example of the ego vehicle position. The first computation unit is configured to compute a first position 304. The second computation unit is configured to compute a second position 310. Depending on the accuracy of the computations, the first 304 and the second 310 position may be the same position or different positions. The ego vehicle positions 304, 310 of the vehicle 203 on the road 305 may be expressed in a curved coordinate system (xt; yt) as is shown in FIG. 3. Here, xt is the longitudinal position along the road 305 at time t, and yt is the lateral position in the lane. Additionally an ego vehicle positioning filter, or positioning filter, may estimate the heading angle at relative to the lane and a discrete parameter Lat which corresponds to in which lane the ego vehicle 203 is positioned. Thus the heading angle at is a direction or orientation relative to the lane and the heading angle at may correspond to an orientation, a direction, etc. Lat=1 corresponds to a one or a first lane, and Lat=2 corresponds to another or a second lane.”).
With respect to claim 8, Brannstrom in view of Natroshvili teaches that the pose is determined based on an orientation of a road on which the vehicle or the device is traveling (See at least Brannstrom FIG. 4 and Paragraph 91 “FIG. 3 shows a schematic example of the ego vehicle position. The first computation unit is configured to compute a first position 304. The second computation unit is configured to compute a second position 310. Depending on the accuracy of the computations, the first 304 and the second 310 position may be the same position or different positions. The ego vehicle positions 304, 310 of the vehicle 203 on the road 305 may be expressed in a curved coordinate system (xt; yt) as is shown in FIG. 3. Here, xt is the longitudinal position along the road 305 at time t, and yt is the lateral position in the lane. Additionally an ego vehicle positioning filter, or positioning filter, may estimate the heading angle at relative to the lane and a discrete parameter Lat which corresponds to in which lane the ego vehicle 203 is positioned. Thus the heading angle at is a direction or orientation relative to the lane and the heading angle at may correspond to an orientation, a direction, etc. Lat=1 corresponds to a one or a first lane, and Lat=2 corresponds to another or a second lane.”).
With respect to claim 9, Brannstrom in view of Natroshvili teaches that the relative position measurement is made from the vehicle or the device to the point feature (See at least Brannstrom FIG. 4 and Paragraph 91 “FIG. 3 shows a schematic example of the ego vehicle position. The first computation unit is configured to compute a first position 304. The second computation unit is configured to compute a second position 310. Depending on the accuracy of the computations, the first 304 and the second 310 position may be the same position or different positions. The ego vehicle positions 304, 310 of the vehicle 203 on the road 305 may be expressed in a curved coordinate system (xt; yt) as is shown in FIG. 3. Here, xt is the longitudinal position along the road 305 at time t, and yt is the lateral position in the lane. Additionally an ego vehicle positioning filter, or positioning filter, may estimate the heading angle at relative to the lane and a discrete parameter Lat which corresponds to in which lane the ego vehicle 203 is positioned. Thus the heading angle at is a direction or orientation relative to the lane and the heading angle at may correspond to an orientation, a direction, etc. Lat=1 corresponds to a one or a first lane, and Lat=2 corresponds to another or a second lane.”).
With respect to claim 10, Brannstrom in view of Natroshvili teaches that the first sensor is a satellite-based positioning sensor (See at least Brannstrom Paragraph 56 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system”)
With respect to claim 11, Brannstrom in view of Natroshvili teaches determining to include a data source associated with the pair of the position measurements based on the measurement error (See at least Brannstrom FIG. 4 and Paragraphs 93-98 “correcting (S4) an error parameter of at least the IMU in a correction unit, where the error parameter is used for correcting a third position of the vehicle computed by the first computation unit at a time T2 with the computed position difference at time T1, if the second computation unit is unable to compute a fourth position of the vehicle at the time T2;”).
With respect to claim 13, Brannstrom teaches an apparatus comprising:
at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,
determine a pair of position measurements, wherein each of the position measurements is computed from a global position measurement of a vehicle or a device (See at least Brannstrom Paragraphs 56-57 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system. Thus the external sensors are typically sensors providing visual measurements of the surroundings, such as of road signs, guard rails, position of landmarks, etc. The satellite navigation system may be the global positioning system (GPS) and/or any other satellite navigation system.”) and a relative position measurement made using a second sensor (See at least Brannstrom Paragraph 11 “The position estimate computed by the first computation unit is performed using data from at least an inertial measurement unit, an IMU, ”), and wherein the position measurements of the pair are made within a time threshold or a distance threshold (See at least Brannstrom FIG. 4 and Paragraphs 93-98 “FIG. 4 illustrates a flow chart of a method for improving positioning accuracy of an autonomous vehicle (203) driving on a road (305), where the method is performed in a unit (100). The method comprises: computing (S1) a first position of the vehicle on the road at a time T1 in a first computation unit, where the computation is performed using data from at least an inertial measurement unit (IMU); computing (S2) a second position of the vehicle on the road at the time T1 in a second computation unit, where the computation is performed using data from at least one external sensor and a map”);
determining, by the processor, a difference between the first position measurement and the second position measurement; providing, by the processor, an error as an output to indicate a measurement error of the second sensor used to determine the relative position measurement (See at least Brannstrom FIG. 4 and Paragraph 93-98 “correcting (S4) an error parameter of at least the IMU in a correction unit, where the error parameter is used for correcting a third position of the vehicle computed by the first computation unit at a time T2 with the computed position difference at time T1, if the second computation unit is unable to compute a fourth position of the vehicle at the time T2; and deciding (S5) the position of the vehicle on the road in a positioning unit”).
Brannstrom, however, fails to explicitly disclose computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix.
Natroshvili teaches computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix (See at least Natroshvili Paragraphs 32-33 “The Kalman filter as used in the navigation system 20 of FIG. 1 and the method of FIG. 2 includes a first filter, a second filter, and a third filter. The first filter receives the satellite positioning data and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix. The second filter receives the vehicle sensor data and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix. The third filter receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generates a combined state vector estimate and a corresponding combined state error covariance matrix therefrom. The third filter comprises a prediction processor implemented on the basis of a state transition model. The prediction processor generates a predicted state vector estimate on the basis of the combined state vector estimate. Further, the prediction processor generates a predicted state error covariance matrix on the basis of the combined state error covariance matrix. The predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter. Accordingly, in this Kalman filter the first filter may determine the first state vector estimate by updating the fed back predicted state vector estimate with the received satellite positioning data. Further, the first filter may determine the first state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the satellite positing data. Similarly, the second filter may determine the second state vector estimate by updating the fed back predicted state vector estimate with the received vehicle sensor data. Further, the second filter may determine the second state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the vehicle sensor data. The third filter may determine the combined state error covariance matrix on the basis of the fed back predicted state error covariance matrix. Further, the third filter may determine the combined state vector estimate on the basis of the fed back predicted state vector estimate and the fed back predicted state error covariance matrix. The state transition model used by the prediction processor may be based on a linear state transition matrix.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom to include computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix, as taught by Natroshvili as disclosed above, such that the error is provided as an output to indicate a measurement error of the second sensor used to determine the relative position measurement, in order to ensure an accurate vehicle position (Natroshvili Paragraph 6 “Accordingly, there is a need for techniques which allow for efficiently improving accuracy of position estimation using satellite positioning data.”).
With respect to claim 16, Brannstrom in view of Natroshvili teaches that the position measurements and the global position measurement is made with respect to a global frame of reference, and wherein the relative position measurement is made with respect to a local frame of reference of the vehicle or the device (See at least Brannstrom Paragraph 11 “The position estimate computed by the first computation unit is performed using data from at least an inertial measurement unit, an IMU, ” | Paragraphs 56-57 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system. Thus the external sensors are typically sensors providing visual measurements of the surroundings, such as of road signs, guard rails, position of landmarks, etc. The satellite navigation system may be the global positioning system (GPS) and/or any other satellite navigation system.”).
With respect to claim 17, Brannstrom in view of Natroshvili teaches the error is further based on an orientation of the vehicle or the device (See at least Brannstrom FIG. 4 and Paragraph 91 “FIG. 3 shows a schematic example of the ego vehicle position. The first computation unit is configured to compute a first position 304. The second computation unit is configured to compute a second position 310. Depending on the accuracy of the computations, the first 304 and the second 310 position may be the same position or different positions. The ego vehicle positions 304, 310 of the vehicle 203 on the road 305 may be expressed in a curved coordinate system (xt; yt) as is shown in FIG. 3. Here, xt is the longitudinal position along the road 305 at time t, and yt is the lateral position in the lane. Additionally an ego vehicle positioning filter, or positioning filter, may estimate the heading angle at relative to the lane and a discrete parameter Lat which corresponds to in which lane the ego vehicle 203 is positioned. Thus the heading angle at is a direction or orientation relative to the lane and the heading angle at may correspond to an orientation, a direction, etc. Lat=1 corresponds to a one or a first lane, and Lat=2 corresponds to another or a second lane.”).
With respect to claim 18, Brannstrom teaches a non-transitory computer-readable storage medium, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:
determining a pair of position measurements of a point feature, wherein each of the position measurements is computed from a global position measurement made using a first sensor (See at least Brannstrom Paragraphs 56-57 “In some embodiments the at least one external sensor providing data used in the second computation unit is one or more of: a camera, a radar, a lidar, and/or a satellite navigation system. Thus the external sensors are typically sensors providing visual measurements of the surroundings, such as of road signs, guard rails, position of landmarks, etc. The satellite navigation system may be the global positioning system (GPS) and/or any other satellite navigation system.”) and a relative position measurement made using a second sensor (See at least Brannstrom Paragraph 11 “The position estimate computed by the first computation unit is performed using data from at least an inertial measurement unit, an IMU, ”), and wherein the position measurements of the pair are made within a time threshold or a distance threshold (See at least Brannstrom FIG. 4 and Paragraphs 93-98 “FIG. 4 illustrates a flow chart of a method for improving positioning accuracy of an autonomous vehicle (203) driving on a road (305), where the method is performed in a unit (100). The method comprises: computing (S1) a first position of the vehicle on the road at a time T1 in a first computation unit, where the computation is performed using data from at least an inertial measurement unit (IMU); computing (S2) a second position of the vehicle on the road at the time T1 in a second computation unit, where the computation is performed using data from at least one external sensor and a map”);
determining, by the processor, a difference between the first position measurement and the second position measurement; providing, by the processor, an error as an output to indicate a measurement error of the second sensor used to determine the relative position measurement (See at least Brannstrom FIG. 4 and Paragraph 93-98 “correcting (S4) an error parameter of at least the IMU in a correction unit, where the error parameter is used for correcting a third position of the vehicle computed by the first computation unit at a time T2 with the computed position difference at time T1, if the second computation unit is unable to compute a fourth position of the vehicle at the time T2; and deciding (S5) the position of the vehicle on the road in a positioning unit”).
Brannstrom, however, fails to explicitly disclose computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix.
Natroshvili teaches computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix (See at least Natroshvili Paragraphs 32-33 “The Kalman filter as used in the navigation system 20 of FIG. 1 and the method of FIG. 2 includes a first filter, a second filter, and a third filter. The first filter receives the satellite positioning data and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix. The second filter receives the vehicle sensor data and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix. The third filter receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and generates a combined state vector estimate and a corresponding combined state error covariance matrix therefrom. The third filter comprises a prediction processor implemented on the basis of a state transition model. The prediction processor generates a predicted state vector estimate on the basis of the combined state vector estimate. Further, the prediction processor generates a predicted state error covariance matrix on the basis of the combined state error covariance matrix. The predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter. Accordingly, in this Kalman filter the first filter may determine the first state vector estimate by updating the fed back predicted state vector estimate with the received satellite positioning data. Further, the first filter may determine the first state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the satellite positing data. Similarly, the second filter may determine the second state vector estimate by updating the fed back predicted state vector estimate with the received vehicle sensor data. Further, the second filter may determine the second state error covariance matrix on the basis of the fed back predicted state error covariance matrix and a measurement error covariance matrix of the vehicle sensor data. The third filter may determine the combined state error covariance matrix on the basis of the fed back predicted state error covariance matrix. Further, the third filter may determine the combined state vector estimate on the basis of the fed back predicted state vector estimate and the fed back predicted state error covariance matrix. The state transition model used by the prediction processor may be based on a linear state transition matrix.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom to include computing, by the processor, a first covariance matrix over the difference; estimating, by the processor, a second covariance matrix for the relative position measurement based on the first covariance matrix; determining, by the processor, an error of the relative position measurement based on the second covariance matrix, as taught by Natroshvili as disclosed above, such that the error is provided as an output to indicate a measurement error of the second sensor used to determine the relative position measurement, in order to ensure an accurate vehicle position (Natroshvili Paragraph 6 “Accordingly, there is a need for techniques which allow for efficiently improving accuracy of position estimation using satellite positioning data.”).
Claims 2, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brannstrom (US 20160377437 A1) (Brannstrom) in view of Natroshvili (US 20130035855 A1) (“Natroshvili”) further in view of Rife (US 20140232595 A1) (“Rife”).
With respect to claim 2, and similarly claim 20, Brannstrom in view of Natroshvili fails to explicitly disclose that the error is based on a least-squares estimate.
Rife teaches that the error is based on a least-squares estimate (See at least Rife Paragraphs 90-93 “Data from collaborators thus can be purposefully excluded in order to minimize the risk of faulty measurement sets corrupting the common-residual estimate. To this end, the threshold Tl for each external measurement set is computed from an exclusion risk probability a, that is set to a relatively large value, for example, between 0.05 and 0.2 (thereby excluding 5% to 20% of all external measurement sets) … The all-in-view common residual ĉav is computed using a weighted least squares estimate … A+ is a mapping matrix (defined below in equation (34)) and p is a concatenated parity-space residual vector, which combines the data from all measurement sets. For simplicity, the measurement sets may be re-indexed such that those excluded based on equation (29) are no longer given an index l or counted in the collaborator total L … The parity space residual vectors pl are obtained from equations (6) and (7) after computing the position solution for each receiver separately. The mapping matrix A+ is a weighted pseudo-inverse of the following form … and from a weighting matrix that is the inverse of Qs, the covariance of the specific noise associated with p. This covariance matrix is block diagonal, with each block element Qs[i, j] having the following form, where Qs,l is defined by equation (24).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom in view of Natroshvili to include that the error is based on a least-squares estimate, as taught by Rife as disclosed above, in order to ensure an accurate determination of the error measurement (Rife Paragraph 9 “Accordingly, systems and methods are disclosed herein for verifying the quality of global navigation satellite system (GNSS) measurements.”).
With respect to claim 12, Brannstrom in view of Natroshvili fails to explicitly disclose determining an operational status of the second sensor based on the measurement error.
Rife teaches determining an operational status of the second sensor based on the measurement error (See at least Rife Paragraph 40 “The fault detection processor 216 receives the pseudorange measurements from the GNSS receiver 211 and the other CERIM Units 2 and 3. The fault detection processor 216 performs calculations, described in relation to FIG. 3B, with the pseudorange measurements to determine whether any of the GNSS satellites have a fault. If one of the satellites has a fault, the fault detection processor 216 or a different processor can issue an alert to a system operator, a driver, the other CERIM units, and/or any other interested party. The fault detection processor 216 can also be configured to perform further analysis to determine which satellite is at fault. An exemplary method for identifying a faulty GNSS satellite is described in relation to FIG. 4. Furthermore, the fault detection processor 216 can be configured to detect whether any GNSS signals it is receiving are affected by multipath or other receiver-specific errors. The fault detection processor 216 can be a general-purpose processor. The GNSS processor 214 and the fault detection processor 216 may be a single processor.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom in view of Natroshvili to include determining an operational status of the second sensor based on the measurement error, as taught by Rife as disclosed above, in order to ensure an accurate determination of the error measurement (Rife Paragraph 9 “Accordingly, systems and methods are disclosed herein for verifying the quality of global navigation satellite system (GNSS) measurements.”).
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Brannstrom (US 20160377437 A1) (Brannstrom) in view of Natroshvili (US 20130035855 A1) (“Natroshvili”) further in view of Alles (US 20090177382 A1) (“Alles”).
With respect to claim 4, and similarly claim 19, Brannstrom in view of Natroshvili fails to explicitly disclose that the pair is part of plurality of pairs of the location measurements, and wherein the measurement error of the second sensor is determined based on the plurality of pairs of the location measurements.
Alles teaches that the pair is part of plurality of pairs of the location measurements, and wherein the measurement error of the second sensor is determined based on the plurality of pairs of the location measurements (See at least Alles Paragraphs 56-57 “The location estimator unit 230 may also determine angle probabilities as represented by block 540 using the inputs of continuous location blocks 512 and candidate roads 532 as inputs. The road estimator unit may determine the probabilities of a point being on a road based upon the direction of the road and the direction of travel. FIG. 9 is an illustration of the determination of an angle between a road and selected points according to an embodiment of the present subject matter. With reference to FIG. 9, to determine the direction of the road and the direction of travel, the x-axis may be selected as a reference; however, in additional embodiments, other references may also be selected. To determine the direction of a road for a given point, any number of algorithms may be utilized and the following discussion should not be construed as limiting the scope of the claims appended herewith … Generally, a minimum value for NUM_NODES may be two, and a maximum value generally depends upon the number of nodes a road has and the curvature thereof. For a given point, the closest NUM_NODES nodes 902, 904, 906 may then be determined. A best-fit line 910 that models these nodes 902, 904, 906 in a least-squares analysis may then be determined, and the angle between the best-fit line 910 and the reference, in this case x-axis, may be determined. The angle may also be adjusted as a function of terrain, topographical constrains, permissible direction of travel, etc. For example, if the road provides only one-way traffic, then the angle may be adjusted as necessary by adding an appropriate angle. This determined angle, road_angle, is representative of the direction of a road … This predetermined number or threshold may be a function of terrain, topographical constrains, permissible direction of travel, etc. A similar rule may also be applied when the number of points after the current point is less than the required number. After determining the set of location points, a best-fit line 930 that models this set of points in a least-squares analysis may then be determined, and the angle, dir_angle, between this best-fit line 930 and the reference, in this case x-axis, may be determined. The difference between road_angle and dir_angle is given by the following relationship … where ANGLE_DEV is an estimated standard deviation of the angle between a road and the direction of travel and where diff_angle is generally equivalent to angle. ANGLE_DEV is configurable and may be a function of terrain, topographical constrains, permissible direction of travel, etc”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom in view of Natroshvili to include that the pair is part of plurality of pairs of the location measurements, and wherein the measurement error of the second sensor is determined based on the plurality of pairs of the location measurements, as taught by Alles as disclosed above, in order to ensure an accurate determination of error measurements (Alles Paragraph 13 “Therefore, an embodiment of the present subject matter provides a method for correcting position error in a navigation system.”).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Brannstrom (US 20160377437 A1) (Brannstrom) in view of Natroshvili (US 20130035855 A1) (“Natroshvili”) further in view of Reimer (US 20220196852 A1) (“Reimer”).
With respect to claim 5, and similarly claim 14, Brannstrom in view of Natroshvili fails to explicitly disclose that the distance threshold is based on a distance over which another measurement error associated with the first sensor remains constant within a threshold range.
Reimer teaches that the distance threshold is based on a distance over which another measurement error associated with the first sensor remains constant within a threshold range (See at least Reimer Paragraph 51 “Correcting the sensor data S400 functions to apply a correction of errors (e.g., systematic errors, sensor errors, bias, scale factors, etc.) to the sensor data to remove or reduce the contribution of errors to the kinematic parameter solutions determined from the sensor data. S400 is preferably performed before S500, but can be performed during (for instance, when the mechanization model applies the sensor error correction) and/or after S500. S400 is preferably performed by a computing system (e.g., an error compensator, an error estimator, an IMU error estimator, a mechanization module, etc.) and/or any component. The error correction can include an offset (e.g., constant) in the sensor data, higher order error corrections (e.g., linear bias, quadratic bias, nonlinear bias, scale factor nonlinearity, cross-axis sensitivity, etc.), other error terms (e.g., scale factor, scale factor error, orthogonality errors, misalignment, g-sensitivity, g2-sensitivity, etc.), and/or any suitable error in the sensor data. In a specific example, the error correction can include only the offset (e.g., only a constant) for an IMU sensor (e.g., 1 offset for accelerometer, 1 offset for the gyroscope, etc.). For instance, a error correction associated with an IMU sensor can account for six states (e.g., 1 per x/y/z direction for each of the accelerometer and the gyroscope). However, fewer than six states and/or more states can be accounted for in the error correction.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom in view of Natroshvili to include that the distance threshold is based on a distance over which another measurement error associated with the first sensor remains constant within a threshold range, as taught by Reimer as disclosed above, in order to ensure accurate measurements based on sensor data and sensor data error (Reimer Paragraph 16 “First, variants of the technology can increase the availability of a positioning solution transmitted to a system by fusing positioning solutions that are low-rate and long-term time stable (but can have high short-term errors; such as from satellite observations) with positioning solutions that are high-rate with good short-term stability (but can have high long-term errors; such as from IMU data). By fusing these solutions, the technology can improve (e.g., better accuracy, better integrity, better availability, etc.) the positioning solutions as compared to using the solutions independently”).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Brannstrom (US 20160377437 A1) (Brannstrom) in view of Natroshvili (US 20130035855 A1) (“Natroshvili”) further in view of Laine (US 20220161782 A1) (“Laine”).
With respect to claim 15, Brannstrom in view of Natroshvili fails to explicitly disclose that the relative position measurement is made using a second sensor with a normally distributed measurement error.
Laine teaches that the relative position measurement is made using a second sensor with a normally distributed measurement error (See at least Laine Paragraph 66 “The prediction of potentially swept area is based on an initial vehicle state, a geometry of the articulated vehicle 1, and on error characteristics associated with one or more sensor input signals used for positioning the vehicle 1 … The error characteristics associated with the one or more sensor input signals also affect the potentially swept area. In general, uncertain sensor input signal data, i.e., sensor input signal data associated with large errors, give more uncertainty and therefore also a larger potentially swept area. More accurate sensor input signals on the other hand often result in a smaller potentially swept area.” | Paragraphs 73-74 “The initial vehicle state S(0) at time t=0 and its estimated value Ŝ(0) which will be associated with an error is also likely to influence the size and shape of the potentially swept area 33. For instance, an uncertain starting point is more likely to generate a large potentially swept area compared to a more certain starting point for the articulated vehicle 1. The initial vehicle state may comprise any number of state variables, such as position in two or three dimensions, velocity vector, acceleration vector, vehicle heading, and vehicle turn rate. Each of the state variables may be associated with an error or an uncertainty having error characteristics described by some statistical distribution. An example with normally distributed errors, also sometimes referred to as Gaussian errors, is illustrated in FIG. 5b , which shows an example scenario 50 where vehicle state error in terms of at least position is assumed to be unbiased with respect to the planned track 23 and to follow a Gaussian distribution centered around the track. The articulated vehicle 1 is at location (A) associated with true position ps1, true velocity vector vS1, and true heading vector hS1. An estimated state vector S=[pA, vA, hA] has associated Gaussian or normally distributed errors [np, nv, nh]. The uncertainty in the estimated state vector is described by a covariance matrix associated with the error variables.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Brannstrom in view of Natroshvili to include that the relative position measurement is made using a second sensor with a normally distributed measurement error, as taught by Laine as disclosed above, in order to ensure accurate measurements based on sensor data and sensor data error (Laine Paragraphs 9-10 “The method also comprises determining the allowable vehicle state space such that the predicted potentially swept area during the maneuver does not extend beyond the drivable area ahead of the vehicle. This way it is ensured that the maneuver can be executed safely given current driving scenario”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM ABDOALATIF ALSOMAIRY whose telephone number is (571)272-5653. The examiner can normally be reached M-F 7:30-5:30.
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/IBRAHIM ABDOALATIF ALSOMAIRY/Examiner, Art Unit 3667
/KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667