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 .
Status of Claims
This action is in reply to the response filed on November 25, 2025.
Claims 1-3 and 5-16 are currently pending and have been examined.
This action is made FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Response to Arguments
Applicant's arguments filed November 25, 2025 have been fully considered but they are not persuasive. Regarding the claim 1, the applicant amends the limitation of claim 1 to include “determining, via the at least one radar sensor that is mounted on the vehicle, at least one velocity vector of the at least one radar sensor during cornering of the vehicle with respect to an object detected by the at least one radar sensor, the at least one velocity vector having a longitudinal velocity component and a lateral velocity component of the at least one radar sensor”, and argues that Cieslar does not teach the limitation because “the target vehicle 9 of Cieslar is the object that is being detected” and “Cieslar is not for determining a velocity vector of a radar sensor of a vehicle, to which the radar sensor is mounted, when that vehicle is cornering, i.e., driving in a curve” (page 7) and therefore the radar sensor, mounted on the host vehicle, is determining the velocity vector of the target, and not the velocity vector of the host vehicle. However, the examiner respectfully disagrees, as while the radar sensor is used for determining a velocity vector of a target vehicle, the velocity vector of the radar sensor mounted to the host vehicle is also being determined. Cieslar states (“The sensor(s) Over the Ground (OTG) velocities are assumed known (determined from host vehicle motion and sensor mounting positions). Sensor velocity vector is defined as
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity. At each radar measurement instance, the radar unit/sensor captures m raw detections from the target. Each raw detection is described by the following parameters expressed in the sensor coordinate system:
r
i
—range (or radial distance),
θ
i
—azimuth angle, and
r
˙
i
—raw range rate (or radial velocity) i=1, . . . , m.” [0044]) and FIG. 3 that shows the sensor origin point where the sensor is mounted on the host vehicle, which shows that the velocities of the sensor of the host vehicle are determined using the host vehicle motion and sensor mounting positions in which the sensor is mounted on the host vehicle. The sensor also determines a velocity vector of a target vehicle, so therefore the target vehicle, or the object, velocity vector is determined with respect to the sensor velocity vector that is calculated from the host vehicle information. This also shows that the sensor velocity vector includes
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity where the vector has a longitudinal velocity component and a lateral velocity component.
The applicant also argues that Cao ‘811 does not teach the limitation “the longitudinal velocity and the installation orientation of the at least one radar sensor that have been estimated being used to classify the object detected by the at least one radar sensor” because the radar installation angle calculator of Cao ‘811 does not use at least one velocity vector of the at least one radar sensor during cornering of the vehicle where the at least one velocity vector of the at least one radar sensor includes a longitudinal and lateral velocity component of the radar sensor itself (page 8). However, the examiner respectfully disagrees, as Cao ‘811 teaches where the object is classified by being detected using the radar installation angle output, (“The object tracker 150 performs object tracking based on the vehicle-moving state data output from the vehicle state sensor 120, the radar installation angle output from the radar-installation-angle calculator 130, and the object data output from the object detector 140.” [0042]) and the radar installation angle calculation uses the host vehicle velocity and direction, (“The radar-installation-angle calculating device includes: radar data acquisition circuitry that generates a first data group and a second data group for each frame of a plurality of frames by using information regarding a velocity of the vehicle and a moving direction of the vehicle,” [0007]). Therefore, in Cao ‘811, (“The vehicle state sensor 120 is constituted by a plurality of sensors that detect the moving state of the vehicle 200. Parameters indicating the moving state of the vehicle 200 which is detected by the vehicle state sensor 120 include at least a vehicle velocity and a yaw rate or a vehicle velocity and a steering angle. For example, the vehicle velocity is detected by a vehicle-velocity sensor, the steering angle is detected by a steering-angle sensor provided for a steering wheel, and the yaw rate is detected by a yaw sensor. The vehicle state sensor 120 outputs the detected parameters (hereinafter referred to as “vehicle-moving state data”) to the radar-installation-angle calculator 130, the object tracker 150, and the surrounding-situation determiner 170… The radar-installation-angle calculator 130 calculates the radar installation angle by using the radar data output from the radar sensor 110 and the vehicle-moving state data output from the vehicle state sensor 120 and outputs a calculation result to the object tracker 150 and the surrounding-situation determiner 170.” [0039-0040]), it shows that the radar information, such as the velocity and orientation of the radar, which can be found as it is a sensor mounted on a host vehicle, is used to classify the detected object. Accordingly, the 35 U.S.C. 103 rejection is maintained. See detailed rejection below.
Applicant's arguments filed November 25, 2025 have been fully considered but they are not persuasive. Regarding claim 3, the applicant argues that “none of the cited references teach to estimate an installation orientation of a radar sensor based on a velocity vector of the radar sensor when the vehicle is driving in a curve” (page 9). However, the applicant disagrees, because Cieslar states (“The invention determines instantaneous values of lateral velocity, longitudinal velocity and yaw rate of any point of a rigid body (such as another vehicle) in the radar field-of-view (FOV). Generally, a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters.” [0035]). “The sensor(s) Over the Ground (OTG) velocities are assumed known (determined from host vehicle motion and sensor mounting positions). Sensor velocity vector is defined as
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity” [0044]) and FIG. 3 that shows the sensor origin point where the sensor is mounted on the host vehicle, which shows that the velocities of the sensor of the host vehicle are determined using the host vehicle motion and sensor mounting positions in which the sensor is mounted on the host vehicle. The sensor also determines a velocity vector of a target vehicle, so therefore the target vehicle, or the object, velocity vector is determined with respect to the sensor velocity vector that is calculated from the host vehicle information. This also shows that the sensor velocity vector includes
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity where the vector has a longitudinal velocity component and a lateral velocity component. Cao ‘811 also states (“The radar-installation-angle calculator 130 calculates the radar installation angle by using the radar data output from the radar sensor 110 and the vehicle-moving state data output from the vehicle state sensor 120 and outputs a calculation result to the object tracker 150 and the surrounding-situation determiner 170.” See [0040]), where the radar installation orientation is calculated based off of the vehicle data. Accordingly, the 35 U.S.C. 103 rejection is maintained. See detailed rejection below.
Applicant's arguments filed November 25, 2025 have been fully considered but they are not persuasive. Regarding claim 9, the applicant argues that Cao '143 does not teach the limitation "wherein the installation orientation of the at least one radar sensor is determined by estimating a difference between a parameterized installation angle and a true installation angle", because Cao ‘143 teaches the installation orientation of the radar unit is stored in advance. However, the examiner respectfully disagrees, as Cao ‘143 teaches (“The vehicle movement estimation unit 450 estimates the movement velocity (hereinafter referred to as “vehicle movement velocity”) and movement direction (hereinafter referred to as “vehicle movement direction”) of the vehicle reference point of the vehicle based on the output radar movement information and rotational angular velocity information, and the spatial relationship between the radar unit 410 and the vehicle reference point. The vehicle movement estimation unit 450 then outputs vehicle movement information that indicates estimation results to a drive control system or the like of the vehicle. The vehicle movement estimation unit 450 acquires the spatial relationship between the radar unit 410 and the vehicle reference point based on the arrangement information stored in the information storage unit 430, for example.” [0081]), where radar position is in correlation to the vehicle velocity. Cao states (“In ST3007, the determination unit 203 extracts the state candidate with the maximum evaluation value (the total value of the reflection intensities) from the plural state candidates. The determination unit 203 then determines the radar movement velocity candidate Vs and radar movement direction candidate θs that correspond to the extracted state candidate as the true present radar movement velocity Vsd and radar movement direction θsd (the true present movement state of the radar device 411).” See Cao [0156]), which shows the correlation of the velocity vector of the radar, the radar movement angle, and the true angle of the radar. Thus, it would have been obvious to a person of ordinary skill in the art that the installation angle can be found using the stored radar position and the radar movement direction in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where the installation angle can be found using the stored radar position and the radar movement direction. Accordingly, the 35 U.S.C. 103 rejection is maintained. See detailed rejection below.
Dependent claims are rejected for the same reasons as listed above due to dependency. See detailed rejection below.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 8-13, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cieslar et al. (US 20180356517 A1) in view of Cao et al. (US 20160291143 A1) and Cao et al. (US 20180045811 A1).
Regarding claims 1-5, 8-13, and 15:
With respect to claim 1, Cieslar teaches:
determining, via the at least one radar sensor that is mounted on the vehicle, at least one velocity vector of the at least one radar sensor during cornering of the vehicle with respect to an object detected by the at least one radar sensor, the at least one velocity vector having a longitudinal velocity component and a lateral velocity component of the at least one radar sensor; (“The sensor(s) Over the Ground (OTG) velocities are assumed known (determined from host vehicle motion and sensor mounting positions). Sensor velocity vector is defined as
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity. At each radar measurement instance, the radar unit/sensor captures m raw detections from the target. Each raw detection is described by the following parameters expressed in the sensor coordinate system:
r
i
—range (or radial distance),
θ
i
—azimuth angle, and
r
˙
i
—raw range rate (or radial velocity) i=1, . . . , m.” [0044], and FIG. 3 that shows the sensor origin point where the sensor is mounted on the host vehicle, which shows that the velocities of the sensor of the host vehicle are determined using the host vehicle motion and sensor mounting positions in which the sensor is mounted on the host vehicle. The sensor also determines a velocity vector of a target vehicle, so therefore the target vehicle, or the object, velocity vector is determined with respect to the sensor velocity vector that is calculated from the host vehicle information. This also shows that the sensor velocity vector includes
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity where the vector has a longitudinal velocity component and a lateral velocity component.
In addition, Cao teaches (“The radar velocity candidate calculation unit 201 calculates a radar movement velocity candidate Vs for each radar movement direction candidate θs of the radar device 411 by using a radar viewing angle direction θ and a Doppler velocity V of each of the reflected waves.” [0071]), which also shows that the radar velocity can be calculated.
transmitting the at least one velocity vector to a module to estimate the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor; (“Described herein is a radar system configured to estimate the yaw-rate and OTG velocity of extended targets (largely, for vehicle tracking) in real-time based on raw radar detections (i.e., range, range-rate, and azimuth).” [0034], “The invention determines instantaneous values of lateral velocity, longitudinal velocity and yaw rate of any point of a rigid body (such as another vehicle) in the radar field-of-view (FOV).” [0035], “Radar detections received by the host vehicle form the target provide raw data provide data with respect to the position of the radar transmit/receive element/unit e.g. the Cartesian position of the detections or the Polar coordinates (azimuth angle, range).” [0037]), which shows that the velocity vector of the radar sensor can be transmitted in order to estimate the velocity of a target vehicle.
In addition, Cao teaches (“The information storage unit 430 in FIG. 1 in advance stores arrangement information that indicates the relative positions of the radar unit 410 which includes the transmit antenna 15 and the receive antenna 16 with respect to the vehicle reference point. More specifically, the arrangement information includes installation position information that indicates an installation position of the radar device 411 with respect to the vehicle reference point as a reference and installation orientation information that indicates an installation orientation of the radar device 411 with respect to the vehicle front as a reference.” [0076]), where the collected information can be used to determine a position of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s determining radar sensor information because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar information to determine the velocity of a vehicle.
estimating, via the module, the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor at least on the basis of the at least one velocity vector transmitted to the module and via the module, the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor that have been estimated being used to classify the object detected by the at least one radar sensor; (“FIG. 4 illustrates how to calculate velocity vectors at the locations of three raw detections (depicted by reference numeral 6) captured from the same rigid body target and the yaw rate of that target.” [0047], “The method may include additionally determining estimates of longitudinal velocity
u
^
t
,
i
, lateral velocity
v
^
t
,
i
of certain target point from the value of yaw rate and the co-ordinates of the center of rotation of the target
(
y
t
,
C
O
R
,
w
c
s
x
t
,
C
O
R
,
w
c
s
)
” [0014]), which shows that longitudinal velocity of the vehicle can be estimated.
In addition, Cao et al. (US 20180045811 A1), teaches (“When the vehicle 200 is traveling in a vehicle moving direction θv relative to the forward direction (the straight-ahead direction), the radar direction α in which the Doppler velocity is 0 changes by an amount corresponding to the vehicle moving direction θv relative to the width direction of the vehicle 200, as illustrated in FIG. 2. FIG. 2 is a diagram illustrating a case in which the vehicle 200 is traveling in the vehicle moving direction θv relative to the forward direction (the straight-ahead direction)” [0032], “The vehicle state sensor 120 is constituted by a plurality of sensors that detect the moving state of the vehicle 200. Parameters indicating the moving state of the vehicle 200 which is detected by the vehicle state sensor 120 include at least a vehicle velocity and a yaw rate or a vehicle velocity and a steering angle. For example, the vehicle velocity is detected by a vehicle-velocity sensor, the steering angle is detected by a steering-angle sensor provided for a steering wheel, and the yaw rate is detected by a yaw sensor. The vehicle state sensor 120 outputs the detected parameters (hereinafter referred to as “vehicle-moving state data”) to the radar-installation-angle calculator 130, the object tracker 150, and the surrounding-situation determiner 170… The radar-installation-angle calculator 130 calculates the radar installation angle by using the radar data output from the radar sensor 110 and the vehicle-moving state data output from the vehicle state sensor 120 and outputs a calculation result to the object tracker 150 and the surrounding-situation determiner 170.” See [0039-0040]), where the radar position is estimated based on the determined velocity of the vehicle and the radar. This also shows that the radar information, such as the velocity and orientation of the radar, which can be found as it is a sensor mounted on a host vehicle, is used to classify the detected object.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao et al. (US 20180045811 A1)’s determining radar sensor information because (“The radar-installation-angle calculator 130 calculates a radar installation angle in order to check whether or not the radar axis of the radar apparatus 100 is displaced, that is, whether or not the radar-transmission-wave radiation direction of the array antenna of the radar sensor 110 is displaced from a predetermined installation angle.” [0040]).
Cieslar further teaches:
wherein the module is a computer program stored on a non-transitory computer readable medium in the at least one radar sensor or a central processing unit of the vehicle to be executed by the at least one radar sensor or the central processing unit; (“a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters” [0035]), this shows that the host vehicle is equipped with a system or module in order to process data.
With respect to claim 2, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
wherein at least one velocity vector of each of at least two radar sensors is determined during cornering, the velocity vectors of the at least two radar sensors are transmitted to the module, and the estimation of the longitudinal velocity of the vehicle and each installation orientation of the at least two radar sensors is performed on the basis of the transmitted velocity vectors of the at least two radar sensors; (“In D. Kellner, M. Barjenbruch, J. Klappstein, jurgen Dickmann, and K. Dietmayer, “Instantaneous full-motion estimation of arbitrary objects using dual doppler radar,” in Proceedings of Intelligent Vehicles Symposium (IV), Dearborn, Mich., USA, 2014, the same problem was considered and the solution to the Conclusion 1 was to take measurements from two sensors.” [0063]), which shows that while Cieslar does not use two radars, it does show that using information of two radar sensors to determine target velocity information is a known concept.
In addition, Cao teaches (“The radar unit 410 may have one radar device or may have two radar devices that are installed in different positions in the vehicle (not illustrated). In a case of installing two radar devices, the radar unit 410 outputs the reflected wave information for each of the radar devices. Further, the two radar devices are installed in different positions at least in the road surface parallel plane.” [0045], “In a case where the radar unit 410 has two radar devices, the radar movement estimation unit 420 may estimate the movement velocities and movement directions with respect to the radar front directions (hereinafter referred to as “radar movement direction”) of the respective radar devices and may output the radar movement information that indicates the movement velocities of the two radar devices to the angular velocity estimation unit 440.” [0047], “Further, in a case where the radar movement information of two radar devices is output from the radar movement estimation unit 420, the angular velocity estimation unit 440 may estimate the rotational angular velocity of the vehicle based on two radar movement velocities, two radar movement directions, the distance between the two radar devices (hereinafter referred to as “radar-to-radar distance”). The angular velocity estimation unit 440 acquires the radar-to-radar distance based on the arrangement information stored in the information storage unit 430, for example.” [0080]), which shows that at least two radar sensors can be used to calculate velocity information.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s determining radar sensor information because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar information to determine the velocity of a vehicle.
With respect to claim 3, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
wherein the estimation of the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor is performed substantially simultaneously; (“The invention determines instantaneous values of lateral velocity, longitudinal velocity and yaw rate of any point of a rigid body (such as another vehicle) in the radar field-of-view (FOV). Generally, a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters.” [0035], “The sensor(s) Over the Ground (OTG) velocities are assumed known (determined from host vehicle motion and sensor mounting positions). Sensor velocity vector is defined as
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity” [0044]), and FIG. 3 that shows the sensor origin point where the sensor is mounted on the host vehicle, which shows that the velocities of the sensor of the host vehicle are determined using the host vehicle motion and sensor mounting positions in which the sensor is mounted on the host vehicle. The sensor also determines a velocity vector of a target vehicle, so therefore the target vehicle, or the object, velocity vector is determined with respect to the sensor velocity vector that is calculated from the host vehicle information. This also shows that the sensor velocity vector includes
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity where the vector has a longitudinal velocity component and a lateral velocity component.
In addition, Cao et al. (US 20180045811 A1), teaches (“When the vehicle 200 is traveling in a vehicle moving direction θv relative to the forward direction (the straight-ahead direction), the radar direction α in which the Doppler velocity is 0 changes by an amount corresponding to the vehicle moving direction θv relative to the width direction of the vehicle 200, as illustrated in FIG. 2. FIG. 2 is a diagram illustrating a case in which the vehicle 200 is traveling in the vehicle moving direction θv relative to the forward direction (the straight-ahead direction)” [0032], “The vehicle state sensor 120 is constituted by a plurality of sensors that detect the moving state of the vehicle 200. Parameters indicating the moving state of the vehicle 200 which is detected by the vehicle state sensor 120 include at least a vehicle velocity and a yaw rate or a vehicle velocity and a steering angle. For example, the vehicle velocity is detected by a vehicle-velocity sensor, the steering angle is detected by a steering-angle sensor provided for a steering wheel, and the yaw rate is detected by a yaw sensor. The vehicle state sensor 120 outputs the detected parameters (hereinafter referred to as “vehicle-moving state data”) to the radar-installation-angle calculator 130, the object tracker 150, and the surrounding-situation determiner 170… The radar-installation-angle calculator 130 calculates the radar installation angle by using the radar data output from the radar sensor 110 and the vehicle-moving state data output from the vehicle state sensor 120 and outputs a calculation result to the object tracker 150 and the surrounding-situation determiner 170.” See [0039-0040]), where the radar position is estimated based on the determined velocity of the vehicle and the radar.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao et al. (US 20180045811 A1)’s determining radar sensor information because (“The radar-installation-angle calculator 130 calculates a radar installation angle in order to check whether or not the radar axis of the radar apparatus 100 is displaced, that is, whether or not the radar-transmission-wave radiation direction of the array antenna of the radar sensor 110 is displaced from a predetermined installation angle.” [0040]).
With respect to claim 5, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
wherein the at least one velocity vector of the at least one radar sensor is determined based on a yaw rate of the at least one radar sensor, a sideslip angle of the vehicle, a horizontal incidence angle of the at least one radar sensor, and/or a vertical incidence angle of the at least one radar sensor; “In aspects of the invention and with prior art techniques, the velocity and the yaw rate of the host vehicle is assumed known. The host over the ground (OTG) velocity vector is defined as:
V
h
=
[
u
h
v
h
]
T
where
u
h
—host longitudinal velocity and
u
h
—host lateral velocity.” [0042], “The sensor(s) Over the Ground (OTG) velocities are assumed known (determined from host vehicle motion and sensor mounting positions). Sensor velocity vector is defined as
V
s
=
u
s
v
s
T
with
u
s
—sensor longitudinal velocity and
v
s
—sensor lateral velocity. At each radar measurement instance, the radar unit/sensor captures m raw detections from the target. Each raw detection is described by the following parameters expressed in the sensor coordinate system:
r
i
—range (or radial distance),
θ
i
—azimuth angle, and
r
˙
i
—raw range rate (or radial velocity) i=1, . . . , m.” [0044], which shows that the yaw rate is known from the host vehicle information, which can be used to determine the yaw rate of the radar and the velocity of the radar can be estimated from parameters of the host vehicle.
With respect to claim 8, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
wherein a yaw rate of the vehicle is estimated substantially simultaneously with the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor; (The invention determines instantaneous values of lateral velocity, longitudinal velocity and yaw rate of any point of a rigid body (such as another vehicle) in the radar field-of-view (FOV). Generally, a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters.” [0035]).
With respect to claim 9, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar does not teach, but Cao teaches:
wherein the installation orientation of the at least one radar sensor is determined by estimating a difference between a parameterized installation angle and a true installation angle; (“the arrangement information includes installation position information that indicates an installation position of the radar device 411 with respect to the vehicle reference point as a reference and installation orientation information that indicates an installation orientation of the radar device 411 with respect to the vehicle front as a reference.” [0076], “FIG. 7B is a diagram that illustrates the relationship among the radar viewing angle direction of the radar device 411 installed in the vehicle 511, which is turning left, the moving direction θv of the vehicle 511, and the direction of the center of turn of the vehicle.” [0097]). Cao also teaches (“The vehicle movement estimation unit 450 estimates the movement velocity (hereinafter referred to as “vehicle movement velocity”) and movement direction (hereinafter referred to as “vehicle movement direction”) of the vehicle reference point of the vehicle based on the output radar movement information and rotational angular velocity information, and the spatial relationship between the radar unit 410 and the vehicle reference point. The vehicle movement estimation unit 450 then outputs vehicle movement information that indicates estimation results to a drive control system or the like of the vehicle. The vehicle movement estimation unit 450 acquires the spatial relationship between the radar unit 410 and the vehicle reference point based on the arrangement information stored in the information storage unit 430, for example.” [0081]), where radar position is in correlation to the vehicle velocity. Cao states (“In ST3007, the determination unit 203 extracts the state candidate with the maximum evaluation value (the total value of the reflection intensities) from the plural state candidates. The determination unit 203 then determines the radar movement velocity candidate Vs and radar movement direction candidate θs that correspond to the extracted state candidate as the true present radar movement velocity Vsd and radar movement direction θsd (the true present movement state of the radar device 411).” See Cao [0156]), which shows the correlation of the velocity vector of the radar, the radar movement angle, and the true angle of the radar. Thus, it would have been obvious to a person of ordinary skill in the art that the installation angle can be found using the stored radar position and the radar movement direction in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where the installation angle can be found using the stored radar position and the radar movement direction.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s determining radar orientation information because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar information to determine the velocity of a vehicle.
With respect to claim 10, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
wherein the module is a Kalman filter; (“An Extended Kalman Filter has been formulated based on a constant turn motion model and measurement model derived from a cloud algorithm. The algorithm is called YCA (Yaw Cloud Algorithm).” [0065])
With respect to claim 11, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar does not teach, but Cao teaches:
wherein a measurement vector with the longitudinal velocity component and the lateral velocity component of the at least one radar sensor is combined with a state vector to be estimated with the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor in the module to form a state-to-measurement equation; (“Specifically, the radar velocity candidate calculation unit 201 sets the radar movement direction candidates θs for the vote in accordance with state candidates of the ballot box that is prepared by the voting unit 202. The ballot box is a state space like a matrix, in which plural radar movement direction candidates θs and plural radar movement velocity candidates Vs in the range of possible values of the velocity of the radar device 411 are in advance set.” [0140], “In ST3003 in FIG. 11, the radar velocity candidate calculation unit 201 calculates the radar movement velocity candidates Vs in accordance with the equation (2) by using the Doppler velocities V and directions θ for the samples and the radar movement direction candidates θs for the vote that are set in ST3002. The radar velocity candidate calculation unit 201 sets the calculated radar movement velocity candidates Vs as the radar movement velocity candidates Vs for the vote and outputs those with the radar movement direction candidates θs for the vote to the voting unit 202”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s state information because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar and state information to determine the velocity of a vehicle.
With respect to claim 12, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 11. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 11. Cieslar further teaches:
wherein, in the module, a state-to-measurement matrix is formed from the state-to-measurement equation, and the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor are estimated by the module by the state-to-measurement matrix; (“Specifically, the radar velocity candidate calculation unit 201 sets the radar movement direction candidates θs for the vote in accordance with state candidates of the ballot box that is prepared by the voting unit 202. The ballot box is a state space like a matrix, in which plural radar movement direction candidates θs and plural radar movement velocity candidates Vs in the range of possible values of the velocity of the radar device 411 are in advance set. FIG. 12 is a diagram that illustrates one example of a concept of the ballot box that is prepared by the voting unit 202. In FIG. 12, the horizontal axis represents the radar movement direction candidate θs, and the vertical axis represents the radar movement velocity candidate Vs.” [0140-141]), and FIG. 12, which shows a matric using the collected radar and state information can be used to estimate the radar direction.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s matrix because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar and state information to determine the velocity of a vehicle.
With respect to claim 13, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar further teaches:
a central processing unit; (“a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters” [0035]), this shows that the host vehicle is equipped with a system unit in order to process data.
at least one radar sensor; (“Described herein is a radar system configured to estimate the yaw-rate and OTG velocity of extended targets (largely, for vehicle tracking) in real-time based on raw radar detections (i.e., range, range-rate, and azimuth).” [0034])
at least one module, the at least one module being a computer program stored on a non-transitory computer readable medium in the at least one radar sensor or the central processing unit that is to be executed by the at least one radar sensor or the central processing unit, (“determining the yaw rate
(
ω
^
t
)
of a target vehicle in a horizontal plane by a host vehicle equipped with a radar system, said radar system including a radar sensor unit adapted to receive signals emitted from said host vehicle by said target” [0005]), where a unit is comparable to a module.
wherein the radar system is set up to perform the method according to claim 1; (“emitting a radar signal at a single time-point instance and determining from a plurality (m) of point radar detections measurements captured from said target vehicle by said radar sensor unit in said single radar measurement instance, the values for each point detection of range, azimuth and range rate;
[
r
i
,
θ
i
,
r
˙
i
]
; determining the values of the longitudinal and lateral components of the range rate equation of the target
(
c
t
,
s
t
)
from the results
r
˙
i
,
θ
i
; of step a)” [0005]), which shows that the radar system of the host vehicle
With respect to claim 15, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 13. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 13. Cieslar further teaches:
A vehicle comprising the radar system according to claim 13; (“a host vehicle is equipped with a radar system where reflected radar signals (detection) from another vehicle in the field of view are processed to provide data in order to ascertain these parameters.” [0035])
With respect to claim 16, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar does not teach, but Cao further teaches:
wherein the at least one velocity vector of the at least one radar sensor is determined based on a sideslip angle of the vehicle; (“In a case where the vehicle 511 is slipping, the rotational center of rotary motion of the vehicle 511 is not necessarily present on the extension line that connects the left and right rear wheels, and it is highly possible that the hypothesis that the movement direction of the two rear wheels is the front direction does not hold true. Accordingly, it is difficult for the vehicle movement estimation unit 450 to estimate the vehicle movement velocity V′ and vehicle movement direction α′ from one radar movement velocity Vsd, one radar movement direction θsd, and the arrangement information. Thus, the vehicle movement estimation unit 450 further estimates a rotational angular velocity w of the vehicle 511 and uses the estimated rotational angular velocity ω. Details of estimation of the vehicle movement velocity V′ and vehicle movement direction α′ by using the rotational angular velocity ω of the vehicle 511 will be described later.” [0109]), where the velocity can be calculated from the sideslip measurements when a vehicle is slipping, and FIG. 14B shows an example of the circumstance of movement of the vehicle that is slipping.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Cao’s determining radar sensor information because (“the movement velocity and movement direction of the vehicle may be estimated more robustly and highly accurately.” See Cao [0009]), by collecting radar information and using radar information to determine the velocity of a vehicle.
Claim(s) 6-7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cieslar et al. (US 20180356517 A1) in view of Cao et al. (US 20160291143 A1), Cao et al. (US 20180045811 A1), and Gustafsson et al. (US 20200400814 A1).
Regarding claim 6-7 and 14:
With respect to claim 6, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar does not teach, but Gustafsson teaches:
wherein at least one inaccurate longitudinal velocity of the vehicle is determined by an odometry sensor of the vehicle and is transmitted to the module, wherein at least one scaling factor for the transmitted inaccurate longitudinal velocity of the vehicle is estimated substantially simultaneously with the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor, and wherein the at least one scaling factor is a correction factor; (“Furthermore, as the Kalman filter 6 also can provide the longitudinal velocity, lateral velocity and yaw-rate of the ego-vehicle 1, it is e.g. possible to use the longitudinal velocity provided by the Kalman filter 6 to estimate a scale error 8 on a longitudinal velocity provided from vehicle dynamics 9 to provide an improved longitudinal velocity signal 11.” [0060]), where an inaccurate longitudinal velocity of the vehicle and scaling factor can be determined.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Gustafsson’s error determination because (“it is e.g. possible to use the longitudinal velocity provided by the Kalman filter 6 to estimate a scale error 8 on a longitudinal velocity provided from vehicle dynamics 9 to provide an improved longitudinal velocity signal 11.” See Gustafsson [0060]), in which detecting inaccurate measurements is necessary to improve system performance.
With respect to claim 7, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 1. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 1. Cieslar does not teach, but Gustafsson teaches:
wherein at least one yaw rate sensor of the vehicle determines at least one inaccurate yaw rate of the vehicle, which is transmitted to the module, and wherein at least one scaling factor for the inaccurate yaw rate of the vehicle is estimated substantially simultaneously with the longitudinal velocity of the vehicle and the installation orientation of the at least one radar sensor, and wherein the at least one scaling factor is a correction factor; (“From the determined detections from stationary targets is derived a linearized signal processing model involving alignment angles, longitudinal and lateral velocity and yaw-rate of the road vehicle.” [0037], “Furthermore, as the Kalman filter 6 also can provide the longitudinal velocity, lateral velocity and yaw-rate of the ego-vehicle 1, it is e.g. possible to use the longitudinal velocity provided by the Kalman filter 6 to estimate a scale error 8 on a longitudinal velocity provided from vehicle dynamics 9 to provide an improved longitudinal velocity signal 11.” [0060]), which shows that an inaccurate longitudinal velocity can be found, however, the longitudinal velocity, lateral velocity and yaw-rate are all determined. Thus, it would have been obvious to a person of ordinary skill in the art to determine an inaccurate yaw rate in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where an inaccurate yaw rate is determined.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Gustafsson’s error determination because (“it is e.g. possible to use the longitudinal velocity provided by the Kalman filter 6 to estimate a scale error 8 on a longitudinal velocity provided from vehicle dynamics 9 to provide an improved longitudinal velocity signal 11.” See Gustafsson [0060]), in which detecting inaccurate measurements is necessary to improve system performance.
With respect to claim 14, Cieslar in combination with Cao and Cao et al. (US 20180045811 A1), as shown in the rejection above, discloses the limitations of claim 13. The combination of Cieslar, Cao, and Cao et al. (US 20180045811 A1) teaches determining a longitudinal velocity of a vehicle and an installation orientation of a radar sensor during cornering of claim 13. Cieslar does not teach, but Gustafsson teaches:
wherein the at least one radar sensor is connected to the module by a proprietary or open data channel, or a CAN bus; (“Streaming of data between the road vehicle 1 and the processing circuit 14 located at such a remote server 15 (cloud) and back to a road vehicle system 2 comprising the controller 3 for radar auto-alignment of the road vehicle 1 may further include a communication network, e.g. as illustrated by arrow 20, connected to the remote server 15” [0067])
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Cieslar’s determining velocity components of a target vehicle with Gustafsson’s communication network because (“Such a communication network 20 represents one or more mechanisms by which a road vehicle 1 may communicate with the remote server 15. Accordingly, the communication network 20 may be one or more of various wireless communication mechanisms, including any desired combination of wireless, e.g., radio frequency, cellular, satellite, and microwave communication mechanisms and any desired network topology.” See Gustafsson [0067]), in which the system of Gustafsson can provide information through various communication network means.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662