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 Office Action is in response to the application filed on 07/22/2026. Claims 1-20 are presently pending and are presented for examination. Claims 1, 9, and 17 were amended.
Response to Remarks
Applicant’s arguments, see Pages 8-11 of the Applicant's Remarks, 07/22/2026, with respect to the claim rejection(s) of claim(s) 1-20 under 35 U.S.C. § 102/103 have been fully considered and are not persuasive. Applicant argues that Smith does not teach object classification by examining translational and rotational motion of LIDAR point clouds. Examiner respectfully disagrees.
Smith does teach the examination of LIDAR point clouds for object classification and subsequent vehicle control. Smith does note, such as in Figure 3A. (154A, 170), and Fig. 8 (802, 804), that LIDAR point clouds can be collocated and that they reflect different portions of the same object such as a rotating wheel, a leg, and a translational body that then correspond to a bicycle. Smith, as noted in the office action, therefore does take in raw lidar data, notice that LIDAR point clouds can be associated and grouped together and reflect different portions of the same object, a rotating wheel, a leg, and a translational body and hence correspond to a bicycle for object classification. Therefore, the rejection utilizing Smith is maintained.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-7, 9, 13-15, 17, and 19-20 are rejected under 35 U.S.C. § 102(a)(1) as being unpatentable over Smith et al., US-20190317219-A1, hereinafter referred to as Smith.
As per claim 1
Smith discloses [a] system comprising (control subsystems 150 - Smith ¶71):
a sensing system of an autonomous vehicle (AV), the sensing system to (Perception subsystem 154 is principally responsible for detecting, tracking and/or identifying elements within the environment surrounding vehicle 100 - Smith ¶71):
obtain a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system (determining at least one instantaneous velocity of the object based on at least one of the corresponding velocities of the subgroup…Adapting autonomous control of the vehicle, , LIDAR data can indicate, for each of a plurality of points in an environment, at least a range for the point and a velocity for the point, with each point in the 3D point cloud defining a corresponding range (via its 3D coordinate in the 3D point cloud) - Smith ¶5 & ¶52 & ¶57);
a perception system of the AV, the perception system to: identify a first cluster of the plurality of return points, the first cluster associated with a first translational velocity (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶117);
identify a second cluster of the plurality of return points, the second cluster associated with a combination of the first translational velocity and a second rotational velocity (when the point is moving…which can be used to determine radial velocity of the point, Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶56 & ¶117);
classify, using a motion pattern defined, at least in part, by the first translational velocity in combination with the second rotational velocity, an object corresponding to a combination of the first cluster and the second cluster (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle. The object detection and classification module 154 A can determine the bounding shape 584 based on processing the FMCW LIDAR data over a classification model 170 , to generate output that indicates neighboring spatial regions that each have a classification probability for “bicyclist” that satisfies a threshold - Smith ¶117);
output a control instruction that causes one or more AV control systems to change a driving path of the AV in view of the classified object (Poses, classifications, and/or velocities of environmental objects determined according to techniques described herein can be utilized in control of an ego vehicle… autonomously controlled, a candidate trajectory (if any) can be determined for each of a plurality of objects 791 , 792 , and 793 based on classifications of those objects and/or instantaneous velocities for those objects, and how autonomous control of the vehicle 100 can be adapted based on the candidate trajectories - Smith ¶121-¶122).
As per claim 5
Smith further discloses wherein to classify the object, the perception system of the AV is to: determine, responsive to identifying that the motion pattern corresponds to (i) a first motion of a torso occurring with the first translational velocity and (ii) a second motion of an arm or a leg occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a pedestrian (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117 – Examiner reasons that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the rotation of the pedestrian’s leg can be used to determine the relationship between the different point clusters and can be used to classify the points as representative of a person).
As per claim 6
Smith further discloses wherein the perception system of the AV is further to: determine, based on the motion pattern, that the pedestrian is running (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117 – Examiner reasons that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the rotation of the pedestrian’s leg can be used to determine the relationship between the different point clusters and can be used to classify the points as representative of a person running).
As per claim 7
Smith further discloses wherein to classify the object, the perception system of the AV is to: determine, responsive to identifying that the motion pattern corresponds to (i) a first motion of the object occurring with the first translational velocity and (ii) a second motion of a wheel occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a wheeled object (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117).
As per claim 9
Smith discloses [a] method comprising (control subsystems 150 - Smith ¶71):
obtaining, by a sensing system of an autonomous vehicle (AV), a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system (determining at least one instantaneous velocity of the object based on at least one of the corresponding velocities of the subgroup…Adapting autonomous control of the vehicle, , LIDAR data can indicate, for each of a plurality of points in an environment, at least a range for the point and a velocity for the point, with each point in the 3D point cloud defining a corresponding range (via its 3D coordinate in the 3D point cloud) - Smith ¶5 & ¶52 & ¶57);
identifying a first cluster of the plurality of return points, the first cluster associated with a first translational velocity (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶117);
identifying a second cluster of the plurality of return points, the second cluster associated with a combination of the first translational velocity and a second rotational velocity (when the point is moving…which can be used to determine radial velocity of the point, Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶56 & ¶117);
classifying, using a motion pattern defined, at least in part, by the first translational velocity in combination with the second rotational velocity, an object corresponding to a combination of the first cluster and the second cluster (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle. The object detection and classification module 154 A can determine the bounding shape 584 based on processing the FMCW LIDAR data over a classification model 170 , to generate output that indicates neighboring spatial regions that each have a classification probability for “bicyclist” that satisfies a threshold - Smith ¶117);
outputting a control instruction that causes one or more AV control systems to change a driving path of the AV in view of the classified object (Poses, classifications, and/or velocities of environmental objects determined according to techniques described herein can be utilized in control of an ego vehicle… autonomously controlled, a candidate trajectory (if any) can be determined for each of a plurality of objects 791 , 792 , and 793 based on classifications of those objects and/or instantaneous velocities for those objects, and how autonomous control of the vehicle 100 can be adapted based on the candidate trajectories - Smith ¶121-¶122).
As per claim 13
Smith further discloses wherein classifying the object comprises: determining, responsive to identifying that the motion pattern corresponds to (i) a first motion of a torso occurring with the first translational velocity and (ii) a second motion of an arm or a leg occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a pedestrian (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117 – Examiner reasons that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the rotation of the pedestrian’s leg can be used to determine the relationship between the different point clusters and can be used to classify the points as representative of a person).
As per claim 14
Smith further discloses further comprises: determining, based on the motion pattern, that the pedestrian is running (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117 – Examiner reasons that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the rotation of the pedestrian’s leg can be used to determine the relationship between the different point clusters and can be used to classify the points as representative of a person running).
As per claim 15
Smith further discloses wherein classifying the object comprises: determining, responsive to identifying that the motion pattern corresponds to (i) a first motion of the object occurring with the first translational velocity and (ii) a second motion of a wheel occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a wheeled object (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117).
As per claim 17
Smith discloses [a]n autonomous vehicle (AV) comprising (Adapting autonomous control of the vehicle, control subsystems 150 - Smith ¶5 & ¶71):
a sensing system to (Perception subsystem 154 is principally responsible for detecting, tracking and/or identifying elements within the environment surrounding vehicle 100 - Smith ¶71):
obtain a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system (determining at least one instantaneous velocity of the object based on at least one of the corresponding velocities of the subgroup…Adapting autonomous control of the vehicle, , LIDAR data can indicate, for each of a plurality of points in an environment, at least a range for the point and a velocity for the point, with each point in the 3D point cloud defining a corresponding range (via its 3D coordinate in the 3D point cloud) - Smith ¶5 & ¶52 & ¶57);
a perception system to: identify a first cluster of the plurality of return points, the first cluster associated with a first translational velocity (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶117);
identify a second cluster of the plurality of return points, the second cluster associated with a combination of the first translational velocity and a second rotational velocity (when the point is moving…which can be used to determine radial velocity of the point, Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith ¶56 & ¶117);
classify, using a motion pattern defined, at least in part, by the first translational velocity in combination with the second rotational velocity, an object corresponding to a combination of the first cluster and the second cluster (Some of those are indicated with a plus sign (+), (which means they have a positive corresponding velocity value), while others are indicated with a minus sign (−). The differing velocity directions on the wheels of the bicycle of the bicyclist 594 can be due to the wheels rotation of the wheels. The differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle. The object detection and classification module 154 A can determine the bounding shape 584 based on processing the FMCW LIDAR data over a classification model 170 , to generate output that indicates neighboring spatial regions that each have a classification probability for “bicyclist” that satisfies a threshold - Smith ¶117);
a control system to: change a driving path of the AV in view of the classified object (Poses, classifications, and/or velocities of environmental objects determined according to techniques described herein can be utilized in control of an ego vehicle… autonomously controlled, a candidate trajectory (if any) can be determined for each of a plurality of objects 791 , 792 , and 793 based on classifications of those objects and/or instantaneous velocities for those objects, and how autonomous control of the vehicle 100 can be adapted based on the candidate trajectories - Smith ¶121-¶122).
As per claim 19
Smith further discloses wherein to classify the object, the perception system is to: determine, responsive to identifying that the motion pattern corresponds to (i) a first motion of a torso occurring with the first translational velocity and (ii) a second motion of an arm or a leg occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a pedestrian (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117 – Examiner reasons that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the rotation of the pedestrian’s leg can be used to determine the relationship between the different point clusters and can be used to classify the points as representative of a person).
As per claim 20
Smith further discloses wherein to classify the object, the perception system is to: determine, responsive to identifying that the motion pattern corresponds to (i) a first motion of the object occurring with the first translational velocity and (ii) a second motion of a wheel occurring with the combination of the first translational velocity and the second rotational velocity, that the object comprises a wheeled object (FMCW LIDAR data points that correspond to a pedestrian 593…differing velocity directions on the pedestrian 593 can be due to the pedestrian walking during the sensing cycle, differing velocity directions between the body of the bicyclist 594 and one of the legs of the bicyclist 594 can be due to the one leg moving in an opposite direction due to pedaling of the bicyclist during the sensing cycle - Smith Fig. 5 (583) + ¶116-¶117).
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 2-3, 10-11, and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Smith, as per claims 1, 2, 9, 10, and 17, respectively, and further in view of Bongio Karrman et al., US-20210255307-A1, hereinafter referred to as Bongio Karrman.
As per claim 2
Smith does not specifically disclose wherein to identify the first cluster, the perception system of the AV is to: fit the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity.
However, Bongio Karrman teaches wherein to identify the first cluster, the perception system of the AV is to: fit the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity (determining whether a holonomic solution exists for a rigid body estimation based on the subset of radar data., The visualization 314 illustrates that some of the points appear to be close in location, and as such, may be associated with a single object. For example, the points 316(2)-316(6) are closely situated, e.g., within a threshold distance, and in some instances those points may be estimated to be indicative of a single object, such as an object 318… a point cluster may include a plurality of points that have some likelihood, e.g., a level and/or degree of similarity, to identify a single object or grouping of objects that should be considered together, e.g., by a planning system of an autonomous vehicle, a RANSAC operation may be based at least in part on an assumption about motion of the detected object, such as a rigid body model, although this may be assumption may be altered based at least in part on a classification of the object detected (e.g., based at least in part on other perception data, such as a classification determined based at least in part on image and/or lidar data)., each of the first detected point d0 , the second detected point d1 , and the third detected point d2 has an associated observed velocity, θi , e.g., a Doppler velocity. Such velocities are illustrated in the example as gdv0 , gdv1 , and gdv2 , respectively - Bongio Karrman Fig 3 + ¶10 & ¶57 & ¶59 & ¶62 – Examiner reasons that the points 316(3) correspond to motion of a first part of a vehicle object).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Bongio Karrman teaches a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points with a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor, as taught by Bongio Karrman, with a reasonable expectation of success to improve the safety of a vehicle by improving the vehicle's ability to predict movement and/or behavior of objects in the vehicle's surroundings, see Bongio Karrman ¶17 for details.
As per claim 3
Smith does not specifically disclose wherein to identify the second cluster, the perception system of the AV: fit the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity.
However, Bongio Karrman teaches wherein to identify the second cluster, the perception system of the AV: fit the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity (velocity determined by the techniques may comprise a two or three-dimensional velocity and/or a rotational velocity…a linear velocity and a rotational velocity, determining whether a holonomic solution exists for a rigid body estimation based on the subset of radar data., The visualization 314 illustrates that some of the points appear to be close in location, and as such, may be associated with a single object. For example, the points 316(2)-316(6) are closely situated, e.g., within a threshold distance, and in some instances those points may be estimated to be indicative of a single object, such as an object 318… a point cluster may include a plurality of points that have some likelihood, e.g., a level and/or degree of similarity, to identify a single object or grouping of objects that should be considered together, e.g., by a planning system of an autonomous vehicle, a RANSAC operation may be based at least in part on an assumption about motion of the detected object, such as a rigid body model, although this may be assumption may be altered based at least in part on a classification of the object detected (e.g., based at least in part on other perception data, such as a classification determined based at least in part on image and/or lidar data)., each of the first detected point d0 , the second detected point d1 , and the third detected point d2 has an associated observed velocity, θi , e.g., a Doppler velocity. Such velocities are illustrated in the example as gdv0 , gdv1 , and gdv2 , respectively - Bongio Karrman Fig 3 + ¶9 & ¶10 & ¶57 & ¶59 & ¶62).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Bongio Karrman teaches a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points with a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor, as taught by Bongio Karrman, with a reasonable expectation of success to improve the safety of a vehicle by improving the vehicle's ability to predict movement and/or behavior of objects in the vehicle's surroundings, see Bongio Karrman ¶17 for details.
As per claim 10
Smith does not specifically disclose wherein identifying the first cluster comprises: fitting the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity.
However, Bongio Karrman teaches wherein identifying the first cluster comprises: fitting the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity (determining whether a holonomic solution exists for a rigid body estimation based on the subset of radar data., The visualization 314 illustrates that some of the points appear to be close in location, and as such, may be associated with a single object. For example, the points 316(2)-316(6) are closely situated, e.g., within a threshold distance, and in some instances those points may be estimated to be indicative of a single object, such as an object 318… a point cluster may include a plurality of points that have some likelihood, e.g., a level and/or degree of similarity, to identify a single object or grouping of objects that should be considered together, e.g., by a planning system of an autonomous vehicle, a RANSAC operation may be based at least in part on an assumption about motion of the detected object, such as a rigid body model, although this may be assumption may be altered based at least in part on a classification of the object detected (e.g., based at least in part on other perception data, such as a classification determined based at least in part on image and/or lidar data)., each of the first detected point d0 , the second detected point d1 , and the third detected point d2 has an associated observed velocity, θi , e.g., a Doppler velocity. Such velocities are illustrated in the example as gdv0 , gdv1 , and gdv2 , respectively - Bongio Karrman Fig 3 + ¶10 & ¶57 & ¶59 & ¶62 – Examiner reasons that the points 316(3) correspond to motion of a first part of a vehicle object).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Bongio Karrman teaches a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points with a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor, as taught by Bongio Karrman, with a reasonable expectation of success to improve the safety of a vehicle by improving the vehicle's ability to predict movement and/or behavior of objects in the vehicle's surroundings, see Bongio Karrman ¶17 for details.
As per claim 11
Smith does not specifically disclose wherein identifying the second cluster comprises: fitting the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity.
However, Bongio Karrman teaches wherein identifying the second cluster comprises: fitting the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity (velocity determined by the techniques may comprise a two or three-dimensional velocity and/or a rotational velocity…a linear velocity and a rotational velocity, determining whether a holonomic solution exists for a rigid body estimation based on the subset of radar data., The visualization 314 illustrates that some of the points appear to be close in location, and as such, may be associated with a single object. For example, the points 316(2)-316(6) are closely situated, e.g., within a threshold distance, and in some instances those points may be estimated to be indicative of a single object, such as an object 318… a point cluster may include a plurality of points that have some likelihood, e.g., a level and/or degree of similarity, to identify a single object or grouping of objects that should be considered together, e.g., by a planning system of an autonomous vehicle, a RANSAC operation may be based at least in part on an assumption about motion of the detected object, such as a rigid body model, although this may be assumption may be altered based at least in part on a classification of the object detected (e.g., based at least in part on other perception data, such as a classification determined based at least in part on image and/or lidar data)., each of the first detected point d0 , the second detected point d1 , and the third detected point d2 has an associated observed velocity, θi , e.g., a Doppler velocity. Such velocities are illustrated in the example as gdv0 , gdv1 , and gdv2 , respectively - Bongio Karrman Fig 3 + ¶9 & ¶10 & ¶57 & ¶59 & ¶62).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Bongio Karrman teaches a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points with a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor, as taught by Bongio Karrman, with a reasonable expectation of success to improve the safety of a vehicle by improving the vehicle's ability to predict movement and/or behavior of objects in the vehicle's surroundings, see Bongio Karrman ¶17 for details.
As per claim 18
Smith does not specifically disclose wherein to identify the first cluster and the second cluster, the perception system is to: fit the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity; and fit the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity.
However, Bongio Karrman teaches wherein to identify the first cluster and the second cluster, the perception system is to: fit the one or more velocity values and the one or more coordinates of return points of the first cluster to a first rigid-body motion associated with the first translational velocity; and fit the one or more velocity values and the one or more coordinates of return points of the second cluster to a second rigid-body motion associated with the first translational velocity and the second rotational velocity (velocity determined by the techniques may comprise a two or three-dimensional velocity and/or a rotational velocity…a linear velocity and a rotational velocity, determining whether a holonomic solution exists for a rigid body estimation based on the subset of radar data., The visualization 314 illustrates that some of the points appear to be close in location, and as such, may be associated with a single object. For example, the points 316(2)-316(6) are closely situated, e.g., within a threshold distance, and in some instances those points may be estimated to be indicative of a single object, such as an object 318… a point cluster may include a plurality of points that have some likelihood, e.g., a level and/or degree of similarity, to identify a single object or grouping of objects that should be considered together, e.g., by a planning system of an autonomous vehicle, a RANSAC operation may be based at least in part on an assumption about motion of the detected object, such as a rigid body model, although this may be assumption may be altered based at least in part on a classification of the object detected (e.g., based at least in part on other perception data, such as a classification determined based at least in part on image and/or lidar data)., each of the first detected point d0 , the second detected point d1 , and the third detected point d2 has an associated observed velocity, θi , e.g., a Doppler velocity. Such velocities are illustrated in the example as gdv0 , gdv1 , and gdv2 , respectively - Bongio Karrman Fig 3 + ¶9 & ¶10 & ¶57 & ¶59 & ¶62 – Examiner reasons that the points 316(3) correspond to motion of a first part of a vehicle object).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Bongio Karrman teaches a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points with a system that utilizes radar sensors to provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor, as taught by Bongio Karrman, with a reasonable expectation of success to improve the safety of a vehicle by improving the vehicle's ability to predict movement and/or behavior of objects in the vehicle's surroundings, see Bongio Karrman ¶17 for details.
Claims 4, and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Smith, as per claims 2, and 9, respectively, and further in view of Han et al., US-20220080974-A1, hereinafter referred to as Han.
As per claim 4
Smith does not specifically disclose wherein to identify the first cluster, the perception system of the AV is further to: determine the first translational velocity based on distances, in a coordinate-velocity space, between the return points of the first cluster and a reference point of the first cluster.
However, Han teaches wherein to identify the first cluster, the perception system of the AV is further to: determine the first translational velocity based on distances, in a coordinate-velocity space, between the return points of the first cluster and a reference point of the first cluster (providing a vehicle configured for setting a reference point based on a combination of objects recognized by a surrounding vehicle, and determining a speed of the surrounding vehicle using the reference point, determine actual distance coordinates of the reference point, and to determine longitudinal distance coordinates and lateral distance coordinates between the surrounding vehicle and the vehicle using the determined actual distance coordinates of the reference point, determine a longitudinal speed and a lateral speed of the surrounding vehicle using the actual distance coordinates of the reference point, the longitudinal distance, and the lateral distance - Han ¶8 & ¶11 & ¶78).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Han teaches a vehicle and a method of controlling the vehicle that sets a reference point based on a combination of objects recognized.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points, with a vehicle and a method of controlling the vehicle that sets a reference point based on a combination of objects recognized, as taught by Han, with a reasonable expectation of success to prevent the accuracy from deteriorating due to external influences other than a movement of the surrounding vehicle, see Han ¶70 for details.
As per claim 12
Smith does not specifically disclose wherein identifying the first cluster further comprises: determining the first translational velocity based on distances, in a coordinate- velocity space, between the return points of the first cluster and a reference point of the first cluster.
However, Han teaches wherein identifying the first cluster further comprises: determining the first translational velocity based on distances, in a coordinate- velocity space, between the return points of the first cluster and a reference point of the first cluster (providing a vehicle configured for setting a reference point based on a combination of objects recognized by a surrounding vehicle, and determining a speed of the surrounding vehicle using the reference point, determine actual distance coordinates of the reference point, and to determine longitudinal distance coordinates and lateral distance coordinates between the surrounding vehicle and the vehicle using the determined actual distance coordinates of the reference point, determine a longitudinal speed and a lateral speed of the surrounding vehicle using the actual distance coordinates of the reference point, the longitudinal distance, and the lateral distance - Han ¶8 & ¶11 & ¶78).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Han teaches a vehicle and a method of controlling the vehicle that sets a reference point based on a combination of objects recognized.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points, with a vehicle and a method of controlling the vehicle that sets a reference point based on a combination of objects recognized, as taught by Han, with a reasonable expectation of success to prevent the accuracy from deteriorating due to external influences other than a movement of the surrounding vehicle, see Han ¶70 for details.
Claims 8, and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Smith, as per claims 7, and 15, respectively, and further in view of Fink et al., US-3927914-A, hereinafter referred to as Fink.
As per claim 8
Smith further discloses wherein to classify the object, the perception system of the AV is further to (Perception subsystem 154 is principally responsible for detecting, tracking and/or identifying elements within the environment surrounding vehicle 100 - Smith ¶71).
Smith does not specifically disclose determine, based on a difference between the first translational velocity and the second rotational velocity, that the wheel of the wheeled object is slipping against a roadway.
Smith discloses using a LIDAR to measure the velocity and direction of return points associated with an object. However, Fink teaches determine, based on a difference between the first translational velocity and the second rotational velocity, that the wheel of the wheeled object is slipping against a roadway (difference between the translational speed of the vehicle and the rotational speed of the wheel, or rather the slip of the wheel – Fink Column 1 Lines 28-31).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Fink teaches a device for antiskid control for motor vehicles that explains how determining the difference between translational and rotational velocities for a wheel is the data that is needed to determine wheel slip.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points, with a device for antiskid control for motor vehicles that explains how determining the difference between translational and rotational velocities for a wheel is the data that is needed to determine wheel slip, as taught by Fink, with a reasonable expectation of success to prevent a jerky control of the brake pressure and, thus, an uncomfortable ride, see Fink Abstract for details.
As per claim 16
Smith further discloses wherein classifying the object further comprises (Perception subsystem 154 is principally responsible for detecting, tracking and/or identifying elements within the environment surrounding vehicle 100 - Smith ¶71).
Smith does not specifically disclose determining, based on a difference between the first translational velocity and the second rotational velocity, that the wheel of the wheeled object is slipping against a roadway.
Smith discloses using a LIDAR to measure the velocity and direction of return points associated with an object. However, Fink teaches determining, based on a difference between the first translational velocity and the second rotational velocity, that the wheel of the wheeled object is slipping against a roadway (difference between the translational speed of the vehicle and the rotational speed of the wheel, or rather the slip of the wheel – Fink Column 1 Lines 28-31).
Smith discloses a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points. Fink teaches a device for antiskid control for motor vehicles that explains how determining the difference between translational and rotational velocities for a wheel is the data that is needed to determine wheel slip.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Smith, a vehicle-based object detection system that utilizes a LIDAR to determine the velocity and classification of different objects comprising detection points, with a device for antiskid control for motor vehicles that explains how determining the difference between translational and rotational velocities for a wheel is the data that is needed to determine wheel slip, as taught by Fink, with a reasonable expectation of success to prevent a jerky control of the brake pressure and, thus, an uncomfortable ride, see Fink Abstract for details.
Conclusion
The prior art reference considered relevant to the Applicant’s invention, but not used as a basis for a rejection of the claims include:
Chen et al., US-20220128995-A1, which discloses a classification module that utilizes features of the tracked clusters, such as positions, velocity (both rotational and translational), see Chen ¶40 for details.
Saini et al., US-20210207961-A1, which discloses a system that classifies vehicle motion using translational motion data and rotational motion data, see Saini ¶155 for details.
It is respectfully requested that the Applicant review these references along with the prior art of record in deciding on what if any amendments will be made to the claims.
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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/F.A.S./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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