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 July 29, 2025. Claims 1-20 are pending. Claims 1, 10 and 15 are independent.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/29/2025 and 08/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 7 and 12-17 of U.S. Patent No. 12399494 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations in the above indicated claims of the instant application are anticipated by the respective claimed limitations in the above indicated claims of U.S. Patent No. 12399494 B2. See the claim anticipation mapping below with all matching elements of the claim limitations appear in bold while non-matching elements of the claim limitations are not bolded.
Instant Application 19/283,836
Claims 1-20
U.S. Patent No. US 12399494 B2
Claims 1, 3, 7 and 12-17
Independent Claims:
1. A method, comprising:
determining, by one or more computing devices of a vehicle configured to operate in an autonomous driving mode, correspondence between a first cluster of sensor data at a first point in time and a second cluster of sensor data at a second point in time later than the first point in time, based on a transformation using an initial estimated motion characteristic for a detected object in an external environment of the vehicle;
7. A method for tracking objects by an autonomous vehicle, comprising: receiving, by one or more computing devices of the autonomous vehicle, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object; determining, by the one or more computing devices using a surface matching algorithm, multiple sets of correspondences between the first and second sets of clusters based on a transformation using an initial estimated motion characteristic for the detected object; optimizing, by the one or more computing devices, the transformation between the first set of clusters and the second set of clusters based on the multiple sets of correspondences;
determining, by the one or more computing devices, one or more motion characteristics of the detected object based on the correspondence; and
determining, by the one or more computing devices, one or more motion characteristics based on the optimized transformation; and
controlling, by the one or more computing devices, the vehicle based on the one or more motion characteristics determined for the detected object.
controlling, by the one or more computing devices, the autonomous vehicle based on the one or more motion characteristics determined for the detected object.
10. A method for tracking objects by a vehicle operating in an autonomous driving mode, the method comprising:
receiving, by one or more computing devices of the vehicle, sensor data collected during a plurality of spins by a sensor of the vehicle including a first spin and a second spin, the sensor data including one or more clusters corresponding to one or more objects detected in an environment around the vehicle;
1. A method for tracking objects by an autonomous vehicle, comprising:
receiving, by one or more computing devices of the autonomous vehicle, sensor data collected during a plurality of spins by a sensor of the autonomous vehicle including a first spin and a second spin, the sensor data including one or more clusters corresponding to one or more objects detected in an environment around the autonomous vehicle;
setting, by the one or more computing devices, a current estimated motion characteristic for a given detected object of the one or more detected objects to an initial estimated value;
setting, by the one or more computing devices, a current estimated motion characteristic for the given detected object to an initial estimated value;
determining, by the one or more computing devices, an adjusted motion characteristic for the given detected object;
determining, by the one or more computing devices using a surface matching algorithm, an adjusted motion characteristic based on the adjusted clusters;
comparing, by the one or more computing devices, the current estimated motion characteristic with the adjusted motion characteristic;
comparing, by the one or more computing devices, the current estimated motion characteristic with the adjusted motion characteristic;
determining, by the one or more computing devices, whether the current estimated motion characteristic is within a predetermined tolerance with the adjusted motion characteristic; and
determining, by the one or more computing devices, that the current estimated motion characteristic is within a predetermined tolerance with the adjusted motion characteristic; and
upon determining that the current estimated motion characteristic is within the predetermined tolerance with the adjusted motion characteristic, controlling, by the one or more computing devices, the vehicle in the autonomous driving mode.
determining, by the one or more computing devices based on determining that the current estimated motion characteristic is within the predetermined tolerance, that clusters having one or more points adjusted points and a current estimated velocity are accurate, wherein controlling the vehicle is further based on the determination that the adjusted clusters and the current estimated velocity are accurate.
15. A system for operating a vehicle in an autonomous driving mode, the system comprising:
a driving system configured to cause the vehicle to perform driving actions while in the autonomous driving mode;
17. A system for operating a vehicle in an autonomous driving mode, the system comprising:
a driving system configured to cause the vehicle to perform driving actions while in the autonomous driving mode;
a perception system configured to detect objects in an environment around the vehicle; and
a perception system configured to detect objects in an environment around the vehicle; and
a computing system having one or more processors and memory, the computing system being operatively coupled to the driving system and the perception system, the computing system being configured to:
determine, correspondence between a first cluster of sensor data at a first point in time and a second cluster of sensor data at a second point in time later than the first point in time, based on a transformation using an initial estimated motion characteristic for a detected object in an external environment of the vehicle;
a computing system having one or more processors and memory, the computing system being operatively coupled to the driving system and the perception system, the computing system being configured to:
receive, from a sensor of the perception system, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object;
determine, using a surface matching algorithm, multiple sets of correspondences between the first and second sets of clusters based on a transformation using an initial estimated motion characteristic for the detected object;
determine one or more motion characteristics of the detected object based on the correspondence; and
optimize the transformation between the first set of clusters and the second set of clusters based on the multiple sets of correspondences;
determine one or more motion characteristics based on the optimized transformation; and
control the vehicle in the autonomous driving mode based on the one or more motion characteristics determined for the detected object.
control the vehicle via actuation of the driving system based on the one or more motion characteristics determined for the detected object.
Dependent Claims:
2. The method of claim 1, wherein determining the one or more motion characteristics is performed using a surface matching algorithm.
7. A method for tracking objects by an autonomous vehicle, comprising:
…
determining, by the one or more computing devices using a surface matching algorithm, multiple sets of correspondences between the first and second sets of clusters based on a transformation using an initial estimated motion characteristic for the detected object;
3. The method of claim 1, further comprising determining, by the one or more computing devices, a yaw rate for the detected object.
12. The method of claim 7, further comprising:
…
determining, by the one or more computing devices, a yaw rate for the detected object based on the asymmetry meeting the set of predetermined rules.
4. The method of claim 3, wherein the yaw rate is based on the detected object having a horizontal cross section with an asymmetry meeting a set of rules.
12. The method of claim 7, further comprising:
determining, by the one or more computing devices, that the detected object has a horizontal cross section with asymmetry meeting a set of predetermined rules; and
determining, by the one or more computing devices, a yaw rate for the detected object based on the asymmetry meeting the set of predetermined rules;
5. The method of claim 1, further comprising accumulating, by the one or more computing devices, multiple clusters of sensor data from different points in time into a merged cluster; and classifying, by the one or more computing devices, the detected object based on the merged cluster.
13. The method of claim 7, further comprising: accumulating, by the one or more computing devices, clusters from multiple spins associated as corresponding to the detected object into a merged cluster; and
classifying, by the one or more computing devices, the detected object based on the merged cluster;
6. The method of claim 5, wherein controlling the vehicle is further based on the classification.
13. The method of claim 7, further comprising:
…
wherein controlling the vehicle is further based on the classification.
7. The method of claim 1, wherein the first cluster is from a set of point cloud data collected at the first point in time and the second cluster is from a set of point cloud data collected at the second point in time.
7. A method for tracking objects by an autonomous vehicle, comprising:
receiving, by one or more computing devices of the autonomous vehicle, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object;
8. The method of claim 1, wherein the first cluster is obtained from a lidar sensor at the first point in time and the second cluster is from the lidar sensor at the second point in time.
7. A method for tracking objects by an autonomous vehicle, comprising:
receiving, by one or more computing devices of the autonomous vehicle, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object;
9. The method of claim 1, wherein the first cluster is obtained from a first spin at the first point in time and the second cluster is obtained from a second spin at the second point in time, the first and second spins being consecutive spins.
7. A method for tracking objects by an autonomous vehicle, comprising:
receiving, by one or more computing devices of the autonomous vehicle, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object;
11. The method of claim 10, wherein:
upon determining that the current estimated motion characteristic is not within the predetermined tolerance with the adjusted motion characteristic:
updating the current estimated motion characteristic; and
comparing the updated current estimated motion characteristic with the adjusted motion characteristic.
14. A system for operating a vehicle in an autonomous driving mode, the system comprising:
…
determine that the current estimated motion characteristic is not within a predetermined tolerance with the adjusted motion characteristic, indicating that the current estimated motion characteristic has not converged with the adjusted motion characteristic;
upon indication that the current estimated motion characteristic has not converged with the adjusted motion characteristic:
update the current estimated motion characteristic; and
compare the updated current estimated motion characteristic with the adjusted motion characteristic.
12. The method of claim 10, wherein:
upon determining that the current estimated motion characteristic is not within the predetermined tolerance with the adjusted motion characteristic, setting the current estimated motion characteristic to be equal to the adjusted motion characteristic.
15. The system of claim 14, wherein upon determination that the current estimated motion characteristic is not within the predetermined tolerance, the computing system is configured to:
set the current estimated motion characteristic to be equal to the adjusted motion characteristic;
13. The method of claim 12, further comprising:
adjusting, based on the current estimated motion characteristic, one or more points in a given cluster from the second spin and one or more points in corresponding cluster from the first spin;
determining a second adjusted motion characteristic based on the adjusted given and corresponding clusters; and
comparing, the current estimated motion characteristic with the second adjusted motion characteristic;
wherein controlling the vehicle in the autonomous driving mode is further based on comparing the current estimated motion characteristic with the second adjusted motion characteristic.
15. The system of claim 14, wherein upon determination that the current estimated motion characteristic is not within the predetermined tolerance, the computing system is configured to:
…
adjust, based on the current estimated motion characteristic, one or more points in the given cluster from the second spin and one or more points in the given cluster from the first spin;
determine, using the surface matching algorithm, a second adjusted motion characteristic based on the adjusted clusters; and
compare, the current estimated motion characteristic with the second adjusted motion characteristic;
wherein control of the vehicle is further based on comparing the current estimated motion characteristic with the second adjusted motion characteristic.
14. The method of claim 10, wherein the first spin and the second spin are consecutive spins.
3. The method of claim 1, wherein the first and second spins are due to rotation of the sensor about an axis.
16. The system of claim 15, wherein determination of the one or more motion characteristics is performed by the computing system using a surface matching algorithm.
17. A system for operating a vehicle in an autonomous driving mode, the system comprising:
…
determine, using a surface matching algorithm, multiple sets of correspondences between the first and second sets of clusters based on a transformation using an initial estimated motion characteristic for the detected object;
17. The system of claim 15, wherein the computing system is further configured to determine a yaw rate for the detected object.
12. The method of claim 7, further comprising:
…
determining, by the one or more computing devices, a yaw rate for the detected object based on the asymmetry meeting the set of predetermined rules.
18. The system of claim 15, further comprising the vehicle.
16. The system of claim 14, further comprising the vehicle.
19. The system of claim 15, wherein the perception system includes a lidar sensor, and the sensor data is obtained from the lidar sensor.
17. A system for operating a vehicle in an autonomous driving mode, the system comprising:
…
a computing system having one or more processors and memory, the computing system being operatively coupled to the driving system and the perception system, the computing system being configured to:
receive, from a sensor of the perception system, sensor data including a first set of clusters collected from a first set of spins corresponding to a first tracking time point, and a second set of clusters collected from a second set of spins corresponding to a second tracking time point, wherein the first set of clusters and the second set of clusters all correspond to a detected object;
20. The system of claim 15, wherein the first cluster of sensor data at the first point in time and the second cluster of sensor data at the second point in time are obtained from consecutive spins of a sensor of the perception system.
3. The method of claim 1, wherein the first and second spins are due to rotation of the sensor about an axis.
Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions are directed to an onboard vehicle cluster tracking system based on sensor data retrieved from a plurality of spin/radial scans of range points for vehicle navigation (collision avoidance) with detected objects using surface matching algorithm. The difference between the claims at issue is that the claimed invention further discloses/limits the perception system can be a lidar sensor with clusters as point cloud data from consecutive spins. As such a double patenting rejection is warranted here.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached form PTO-892.
Conclusion
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/P.Y.N./Examiner, Art Unit 3661
August 6, 2026
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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