Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 37 and 39-40 are objected to because of the following informalities:
Claim 37 should read,
“The method as recited in claim 22, wherein:
i) the regression analysis is carried out using an error-in-variables regression method, and/or
ii) the azimuth angle is optimized, and/or
iii) the regression analysis is carried out using an iteratively reweighted least squares method.”
Claim 39 lines 1-4 should read,
“An arithmetic logic unit configured to obtain a multiplicity of measurement data sets and configured to determine a movement state of a rigid body relative to an environment using the multiplicity of measurement data sets that relate to objects in the environment around the body, […]”.
Claim 40 lines 1-9 should read,
“A vehicle and/or a robot, comprising:
one or more sensors that are attached to a body of the vehicle and/or robot, and that capture an environment around the body, the one or more sensors being configured to take measurements of objects in the environment and to send measurement data sets captured to an arithmetic logic unit; and
the arithmetic logic unit configured to obtain a multiplicity of the measurement data sets and configured to determine a movement state of a rigid body relative to an environment using the multiplicity of measurement data sets what relate to objects in the environment around the body, […]”.
Claim 41 should read,
“The vehicle and/or robot as recited in claim 40, wherein the one or more sensors are radar sensors and/or LiDAR sensors.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 32-35 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 32 recites the limitation “the body sensor velocity vector”. There is insufficient antecedent basis for this limitation in the claim, thus it is indefinite. Claims 33-35 are rejected due to their dependence on claim 32.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 22, 24-27, 37, and 39-42 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Monaco et al. (RADARODO: Ego-Motion Estimation From Doppler and Spatial Data in RADAR Images [2020]), hereinafter Monaco.
Regarding claims 22, 39-40, and 42, Monaco teaches a method and non-transitory machine-readable storage medium on which is stored a computer program for determining a movement state of a rigid body relative to an environment using a multiplicity of measurement data sets relating to objects in the environment around the body, an arithmetic logic unit configured to obtain a multiplicity of measurement data sets and configured to determine a movement state of a rigid body relative to an environment using the multiplicity of measurement data sets what relate to objects in the environment around the body (Fig. 1, system algorithm involves Doppler and spatial measurement data sets used to determine translational and rotational velocity estimates), and vehicle and/or a robot, comprising:
one or more sensors that are attached to a body of the vehicle and/or robot, and that capture an environment around the body, the one or more sensors being configured to take measurements of objects in the environment and to send measurement data sets captured to the arithmetic logic unit (Fig. 1, system algorithm involves Doppler and spatial measurement data sets used to determine translational and rotational velocity estimates; pages 480-481, “It is noteworthy that, up until this point, RADARODO has only been used to estimate the sensor’s velocities. However the true goal is to estimate the ego-vehicle’s motion.”),
wherein each of the measurement data sets includes a measurement time, a Doppler velocity, and an azimuth angle in relation to a sensor reference system of a sensor (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”), the method comprising the following steps:
determining the movement state of the body relative to the environment as a velocity vector and an angular velocity vector in a body reference system, wherein each sensor reference system can be translated into the body reference system by a non-singular transformation (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”; pages 480-481, “It is noteworthy that, up until this point, RADARODO has only been used to estimate the sensor’s velocities. However, the true goal is to estimate the ego-vehicle’s motion. The ego-vehicle’s motion can be estimated by transforming the estimated sensor motion if the rigid transformation between the two are known. For simplicity, their axes are assumed to be aligned to yield the planar transformation in Equation 17 […]”), the determining of the movement state including:
creating at least one set of conditions that includes a plurality of the measurement data sets, and minimizing in a regression analysis for the at least one set of conditions a functional that is dependent on Doppler velocity deviations between estimated Doppler velocities and the Doppler velocities of the measurement data sets included in the at least one set of conditions, wherein the estimated Doppler velocities are represented as dependent variables in the regression analysis, wherein one or more components of the velocity vector and/or of the angular velocity vector are determined by the regression analysis (page 478, “RADARODO uses Doppler data to estimate translational ego-motion by solving Equation 3 via Least Squares Regression. This linear system is represented as a matrix in Equation 7:
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“; regression analysis implicitly involves conditions to be met).
Regarding claim 24, Monaco teaches the method as recited in claim 22,
wherein the measurement data sets are captured using one or more sensors that are attached to the body and capture the environment around the body (page 478 right-hand column, “For this paper’s implementation, the aforementioned error metrics were used within the g2o non-linear optimization framework [34] to estimate the sensor’s translational velocities. The Least Squares Solution served as the initial estimate. Furthermore, this framework calculated its estimates’ expected uncertainties. This environment-dependent information is critical for later RADARODO subprocesses and integration into overarching sensor fusion frameworks.”).
Regarding claim 25, Monaco teaches the method as recited in claim 22,
wherein the measurement data sets include at least two measurement data sets from a sensor having different measurement times (page 480, “Yet, ego-motion estimation depends on consecutive measurements that are relatively consistent. Thus, a high SNR region [i.e. target] from the previous frame is expected to correspond with a similarly-high SNR region in the current frame.”).
Regarding claim 26, Monaco teaches the method as recited in claim 22,
wherein the measurement times of the measurement data sets from at least two different sensors are different from one another (page 483 left-hand column, “Although it does require a complementary sensor, e.g. an inertial measurement unit [IMU], many suitable sensors are guaranteed on today’s vehicles. Thus, the very accurate Doppler-based measurements can be a powerful and complementary asset to conventional sensor suites. This implementation alone can meet the estimation needs for a large majority of vehicles.”).
Regarding claim 27, Monaco teaches the method as recited in claim 22,
wherein the movement state for one determination time is determined at least at one calculation time, and wherein the at least one set of conditions is created from the measurement data sets whose measurement times are within a determination period (page 478, “RADARODO uses Doppler data to estimate translational ego-motion by solving Equation 3 via Least Squares Regression.”; the conditions for least squares regressions are assessed from input data; page 483 right-hand column, “Notably, a measurement of a map landmark may determine the most likely combination of yaw or side-slip that caused the RADAR’s lateral motion. More impressively, even sporadic landmark measurements can retroactively correct previous pose changes.”).
Regarding claim 37, Monaco teaches the method as recited in claim 22, wherein:
i) the regression analysis is carried out using an error-in-variables regression method, and/or
ii) the azimuth angle is optimized (page 478 right-hand column, “While the Least Squares Solution works very well, it incorrectly assumes perfect measurements of the target’s azimuthal angles. Thus, a non-linear optimization framework that takes this uncertainty into account could yield even more accurate estimates. Specifically, this framework attempts to converge to the translational velocities that would cumulatively minimize all targets’ range rate and azimuthal angle errors.”), and/or
iii) the regression analysis is carried out using an iteratively reweighted least squares method.
Regarding claim 41, Monaco teaches the vehicle and/or robot as recited in claim 40,
wherein the one or more sensors are radar sensors and/or LiDAR sensors (page 475 left-hand column, “This paper presents the novel real-time RADAR-based ego-motion estimation algorithm, RADARODO [RADAR Odometry].”).
Claim Rejections - 35 USC § 103
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.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Monaco in view of Hong et al. (US 20230305141 A1), hereinafter Hong.
Regarding claim 23, Monaco teaches the method as recited in claim 22,
wherein at least some of the one or more measurement data sets are determined from measurement results that include only a measurement time, the Doppler velocity, and the azimuth angle (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”), but fails to teach
wherein at least one of the measurement data sets includes an elevation angle, and wherein the elevation angle is set to be the same as a predetermined value which is exactly zero.
However, Hong teaches
wherein at least one of the measurement data sets includes an elevation angle, and wherein the elevation angle is set to be the same as a predetermined value which is exactly zero (para. 97, “Additionally or alternatively, angle information can be determined based on Doppler information. For example, if the egospeed is known [e.g., based on auxiliary sensor information], then the composite angle a can be determined based on the egospeed and the Doppler information, following from the equation S.sub.ego=V.sub.D/cos α. In some embodiments, one of the polar angle measurements [e.g., azimuthal angle ϕ or elevation angle θ=0] may already be known [e.g., from independent information such as auxiliary sensor information], and so the composite angle a can be used [along with this information] to determine the unknown polar angle. For example, for points substantially in a horizontal plane [e.g., ground plane, horizontal plane intersecting the radar sensor, elevation angle θ=0, etc.], a can be equivalent [or substantially equivalent] to the azimuthal angle ϕ [e.g., in situations in which the egovelocity is parallel to the horizontal plane and oriented at ϕ=0, such as wherein the egovelocity is forward with respect to the radar sensor and/or vehicle orientation]. This information can then be used to determine the total egorotation [e.g., as described above], such as determining all three orthogonal components of the egorotation.”).
Monaco and Hong are considered to be analogous to the claimed invention because they are in the same field of vehicle motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Hong with the motivation of simplifying motion estimation.
Claims 29-30 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Monaco in view of Zhang (US 11015936 B2).
Regarding claim 29, Monaco teaches the method as recited in claim 27,
wherein the movement state for a plurality of successive determination times is determined (page 483 right-hand column, “Notably, a measurement of a map landmark may determine the most likely combination of yaw or side-slip that caused the RADAR’s lateral motion. More impressively, even sporadic landmark measurements can retroactively correct previous pose changes.”), but fails to teach
wherein each determination period of the plurality of successive determination times has a lower time limit and an upper time limit, wherein the lower time limit is identical to a previous determination time of the plurality of successive determination times and the upper time limit is identical to a subsequent determination time of the plurality of successive determination times or to the calculation time.
However, Zhang teaches
wherein each determination period of the plurality of successive determination times has a lower time limit and an upper time limit, wherein the lower time limit is identical to a previous determination time of the plurality of successive determination times and the upper time limit is identical to a subsequent determination time of the plurality of successive determination times or to the calculation time (col. 16 line 64 – col. 17 line 5, “That is, the method of the embodiment shown in FIG. 5 and FIG. 6 may be used to obtain moving tracks of the second device that are presented within different preset duration. The different preset duration may be consecutive duration. That is, a moving track of the second device within each preset duration is calculated, and then global fitting is performed, according to a time sequence, on all the moving tracks of the second device that are presented within all the preset durations.”).
Monaco and Zhang are considered to be analogous to the claimed invention because they are in the same field of vehicle motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Zhang with the motivation of increasing processing efficiency.
Regarding claim 30, Monaco teaches the method as recited in claim 27, but fails to teach
wherein a first determination of the movement state for the determination time is carried out based on a first determination period, and a second determination of the movement state for the determination time is carried out based on a second determination period, wherein the second determination period is different from the first determination period and includes at least one measurement time that is not included in the first determination period, and wherein the first determination is carried out at a first calculation time and the second determination is carried out at a second, later calculation time, wherein an upper time limit of the second determination period comes after the first calculation time.
However, Zhang teaches
wherein a first determination of the movement state for the determination time is carried out based on a first determination period, and a second determination of the movement state for the determination time is carried out based on a second determination period, wherein the second determination period is different from the first determination period and includes at least one measurement time that is not included in the first determination period, and wherein the first determination is carried out at a first calculation time and the second determination is carried out at a second, later calculation time, wherein an upper time limit of the second determination period comes after the first calculation time (col. 16 line 64 – col. 17 line 5, “That is, the method of the embodiment shown in FIG. 5 and FIG. 6 may be used to obtain moving tracks of the second device that are presented within different preset duration. The different preset duration may be consecutive duration. That is, a moving track of the second device within each preset duration is calculated, and then global fitting is performed, according to a time sequence, on all the moving tracks of the second device that are presented within all the preset durations.”).
Monaco and Zhang are considered to be analogous to the claimed invention because they are in the same field of vehicle motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Zhang with the motivation of increasing processing efficiency.
Regarding claim 38, Monaco teaches a method for determining a relative position and/or a relative orientation of a rigid body, comprising the following steps:
determining a movement state relative to an environment using a multiplicity of measurement data sets relating to objects in the environment around the body, wherein each of the measurement data sets includes a measurement time, a Doppler velocity, and an azimuth angle in relation to a sensor reference system of a sensor (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”), the movement state being determined by:
determining the movement state of the body relative to the environment as a velocity vector and an angular velocity vector in a body reference system, wherein each sensor reference system can be translated into the body reference system by a non-singular transformation (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”; pages 480-481, “It is noteworthy that, up until this point, RADARODO has only been used to estimate the sensor’s velocities. However, the true goal is to estimate the ego-vehicle’s motion. The ego-vehicle’s motion can be estimated by transforming the estimated sensor motion if the rigid transformation between the two are known. For simplicity, their axes are assumed to be aligned to yield the planar transformation in Equation 17 […]”), the determining of the movement state including:
creating at least one set of conditions that includes a plurality of the measurement data sets, and minimizing in a regression analysis for the at least one set of conditions a functional that is dependent on Doppler velocity deviations between estimated Doppler velocities and the Doppler velocities of the measurement data sets included in the at least one set of conditions, wherein the estimated Doppler velocities are represented as dependent variables in the regression analysis, wherein one or more components of the velocity vector and/or of the angular velocity vector are determined by the regression analysis (page 478, “RADARODO uses Doppler data to estimate translational ego-motion by solving Equation 3 via Least Squares Regression. This linear system is represented as a matrix in Equation 7:
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“; regression analysis implicitly involves conditions to be met), but fails to teach
determining a plurality of movement states of the body for a plurality of successive determination times, each of the movement states being determined relative to an environment using a multiplicity of measurement data sets relating to objects in the environment around the body,
integrating the movement states over time between a starting determination time of the plurality of determination times and an end determination time of the plurality of determination times to obtain the relative position and/or the relative orientation as results of the integration.
However, Zhang teaches
determining a plurality of movement states of the body for a plurality of successive determination times, each of the movement states being determined relative to an environment using a multiplicity of measurement data sets relating to objects in the environment around the body, integrating the movement states over time between a starting determination time of the plurality of determination times and an end determination time of the plurality of determination times to obtain the relative position and/or the relative orientation as results of the integration (col. 16 line 64 – col. 17 line 5, “That is, the method of the embodiment shown in FIG. 5 and FIG. 6 may be used to obtain moving tracks of the second device that are presented within different preset duration. The different preset duration may be consecutive duration. That is, a moving track of the second device within each preset duration is calculated, and then global fitting is performed, according to a time sequence, on all the moving tracks of the second device that are presented within all the preset durations.”).
Monaco and Zhang are considered to be analogous to the claimed invention because they are in the same field of device motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Zhang with the motivation of increasing motion estimation accuracy.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Monaco in view of Lessmann et al. (US 12248053 B2), hereinafter Lessmann.
Regarding claim 31, Monaco teaches the method as recited in claim 22,
wherein the estimated Doppler velocities are also represented in the regression analysis as being dependent on the velocity vector and the angular velocity vector by using the transformation between the respective sensor reference system and the body reference system, and the velocity vector is determined such that the functional is minimized (page 476 right-hand column, “One of the most significant capabilities of RADAR for motion estimation is its ability to instantaneously measure relative radial velocities via Doppler shift. This phenomenon can be analytically described for planar motion by taking the time derivative of range, as seen in Equation 1: […] where r is the range of a target from the RADAR sensor in the xy-plane, x is the position of the target along the sensor’s longitudinal x-axis, y is the position of the target along the sensor’s lateral y-axis, r˙ is the relative velocity of the target with respect to the RADAR along the radial axis [i.e. range rate], and x˙, y˙ are the time derivatives of the target‘s position in the sensor’s Cartesian coordinate system. Assuming that the target is static, the relative position‘s time derivative can be related to the sensor’s planar ego-motion via Equation 2: […] where ωz is the sensor’s rotational velocity about its vertical z-axis [i.e. yaw rate] and vx, vy are the sensor’s translational velocities along its longitudinal and lateral axes, respectively.”), but fails to teach
wherein the angular velocity vector is determined such that the functional is minimized.
However, Lessman teaches
wherein the angular velocity vector is determined such that the functional is minimized (col. 3 lines 39-58, “In some embodiments, the equation of motion is given by: […] [ω] is indicative of a yaw rate of the vehicle, vx is an X-component of a velocity of the vehicle. In this embodiment, the ego-motion information comprises values ω and vx.”; col. 26 lines 11-26, “Furthermore, as previously explained, minimization of the weighted least square function using the method of lagrange multipliers can then be performed to derive a normal equation system of the form shown in equation (13), which can be given as follows:
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[…]”).
Monaco and Lessmann are considered to be analogous to the claimed invention because they are in the same field of device motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Lessmann with the motivation of increasing motion estimation accuracy.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Monaco in view of Stachnik et al. (US 11448746 B2), hereinafter Stachnik.
Regarding claim 36, Monaco teaches the method as recited in claim 22, but fails to teach wherein: the measurement data sets each include at least one additional parameter selected from:
a distance from a captured object, and/or a variance in the distance, and/or a variance in the azimuth angle, and/or a variance in the elevation angle, and/or a variance in the Doppler velocity, and/or a signal strength of a received signal, and/or a cross section, and/or a radar cross section or LiDAR cross section, in each case based on the captured object of the measurement data set, and/or a type of sensor, and/or an arrangement of the sensor on the body, and
wherein the Doppler velocity deviations are multiplied by an additional weight that is a function of the additional parameter, and/or a measurement data set is rejected when the at least one additional parameter is outside at least one predetermined range.
However, Stachnik teaches wherein: the measurement data sets each include at least one additional parameter selected from:
a distance from a captured object (col. 2 lines 33-44, “The present invention relates to a method of estimating a velocity magnitude of a moving target in a horizontal plane using radar signals received by a radar detection system, the radar detection system being configured to resolve multiple dominant points of reflection, i.e. to receive a plurality of radar signals from the moving target in a single measurement instance of a single, wherein each of the resolved points of reflection is described by data relating to a range, an azimuth angle and a raw range rate of the points of reflection in said single radar measurement instance. The invention further relates to a radar detection system.”), and/or a variance in the distance, and/or a variance in the azimuth angle, and/or a variance in the elevation angle, and/or a variance in the Doppler velocity (col. 4 lines 19-25, “This is a fast and reliable way of estimating the velocity magnitude of the target. Preferably the weights are selected as an inverse of an estimated variance for a corresponding estimate during the step of estimating the weighted mean of estimates. This further reduces the error in the calculation making the method more reliable.”), and/or a signal strength of a received signal, and/or a cross section, and/or a radar cross section or LiDAR cross section, in each case based on the captured object of the measurement data set, and/or a type of sensor, and/or an arrangement of the sensor on the body, and
wherein the Doppler velocity deviations are multiplied by an additional weight that is a function of the additional parameter (col. 2 lines 57-65, “The method comprises the steps of: assuming that a heading angle calculated for each of the plurality of received points of reflection is equal to an orientation angle of said moving target; and calculating the velocity magnitude of said moving target as a weighted mean of multiple estimates that are estimated from each range rate and azimuth from the points of reflection and heading angle, with the heading angle being an assumed value, in the single radar measurement instance.”), and/or a measurement data set is rejected when the at least one additional parameter is outside at least one predetermined range.
Monaco and Stachnik are considered to be analogous to the claimed invention because they are in the same field of vehicle motion state estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Monaco with the teachings of Stachnik with the motivation of increasing motion estimation accuracy.
Allowable Subject Matter
Claim 28 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC K HODAC whose telephone number is (571) 270-0123. The examiner can normally be reached M-Th 8-6.
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/ERIC K HODAC/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648