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 .
Response to Remarks
The Office Action has been made issued in response to amendment filed March 16, 2026. Claims 1-15, 17-18 and 20-22 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Response to Arguments
Claim Rejections – 35 USC section § 102
On pages 6-7 of the Response, Applicant argues that Kentley in view of Ansari teach does not teach wherein the correlating positions and velocities of proximate objects found in a previous processing cycle with proximate objects identified in a current processing cycle as recited by independent claims 1, 9 and 18; however, the Examiner disagrees. Kentley clearly teaches a kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity (see [p][0047]). Therefore, Kentley teaches wherein the correlating positions and velocities of proximate objects found in a previous processing cycle with proximate objects identified in a current processing cycle.
Claim Rejections – Double Patenting
Applicant has not filed a terminal disclaimer. Therefore, the rejection has not been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1-15, 17-18 and 20-21 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kentley et al. (US 2017/0120904 A1) in view of Ansari (US 2016/0357188 A1).
Regarding claim 1, Kentley teaches a system (systems to implement active safety systems in an autonomous vehicle – see [p][0004]) comprising: a data processor (810, one or more processors - see Fig 8 and [p][0102]); and a vehicle position and velocity estimation module (autonomous vehicle system 301 – see [p][0068] and Fig 3A) which, when executed by the data processor (see [p][0102]), causes the system to: receive input object data (332 and 334 sensor data – see Fig 3B and [p][0076]) from a subsystem of vehicle (320, sensor system - Fig 3B), the input object data including image data from an image generating device (373, cameras – see Fig 3B) and distance data from a distance measuring device (375, LIDARS – see Fig 3B); determine a first position of a proximate object near the vehicle from the image data (a collision between the autonomous vehicle and an object may be predicted based the autonomous vehicle… the object location 257 in a first location – [p][0058]); determine a second position ([t]he object location 257 may change from a first location to a next location due to motion of an object in the environment – [p][0058]) of the proximate object from the distance data (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, such as sensor data from LIDAR 371 – see [p][0076]); correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data (stage 408 may determine that the detected object is of another class and may analyze at a stage 410, based on data accessed from object data store 426, the data representing the object to determine that the classification matches a person riding a skateboard and output data representing the object classification 411 – see [p][0085]); determine a position of the proximate object using the correlated first and second positions (stage 412 a determination may be made that the detected object is in motion and at the stage 418 the object track may be set to dynamic (D) and the location of the object may be tracked at the stage – see [p][0085]); and use the position of the proximate object to navigate the vehicle ([t]he region 1644 may be designated (e.g., by the planner system) as an open region 1645. The planner system may command the drive system (e.g., via steering, propulsion, and braking data) to alter the trajectory of the vehicle 100 from its original trajectory Tav to an avoidance maneuver trajectory Tm, to autonomously navigate the autonomous vehicle 100 into the open region 1645 – see [p][0149]); i wherein the system is further configured to correlate positions and velocities of proximate objects found in a previous processing cycle with proximate objects identified in a current processing cycle (([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see [p][0047]). .
Although Kentley teaches that the LIDAR may be three-dimensional (3D Lidar data – see [p][0081]); Kentley does not expressly teach three-dimensional (3D) position and the use of the 3D position. However, Ansari explicitly teach three-dimensional (3D) position (adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information – see [p][0002]) and the use of the 3D position (using the combined comprehensive 3D model with detailed map information to maneuver the vehicle – see [p][0002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having three-dimensional (3D) position and the use of the 3D position.
Wherein having Kentley three-dimensional (3D) position and the use of the 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 2, Kentley in view of Ansari teach the system of claim 1, Kentley wherein the image generating device is one or more cameras (373, cameras – see Fig 3B) and the second position is a position (a next location due to motion of an object in the environment – [p][0058]); Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 3, Kentley in view of Ansari teach the system of claim 1, Kentley teaches being further configured to determine the first position as a two-dimensional (2D) position of the proximate object using the image data (stage 408 may determine that the detected object is of another class and may analyze at a stage 410, based on data accessed from object data store 426, the data representing the object to determine that the classification matches a person riding a skateboard and output data representing the object classification 411 – see [p][0085]) received from the image generating device (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, s…, Cameras 373 – see [p][0076]).
Regarding claim 4, Kentley in view of Ansari teach the system of claim 1 Kentley teach being further configured to determine the position of the proximate object using a two-dimensional (2D) position and a point cloud of the distance data received from the distance measuring device (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, such as sensor data from LIDAR 371, Cameras 373 – see [p][0076] and in the case of using a 3D Lidar would create point cloud data – see [p][0069]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 5, Kentley in view of Ansari teach the system of claim 1 Kentley teach being further configured to retain the position of the proximate object as tracking data over the plurality of processing cycles (Object dynamics determination 735 may be further configured to access an object dynamics data store 726. Object dynamics data store 726 may include data representing object dynamics. Object dynamics determination 735 may be configured to compare data representing object dynamics with the data representing the object type 733 and the data representing the object location 721 to determine data representing a predicted object motion 737 – see [p][0098]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 6, Kentley in view of Ansari teach the system of claim 5, Kentley teach being further configured to determine a velocity of the proximate object using the position and the tracking data ([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see [p][0047]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 7, Kentley in view of Ansari teach the system of claim 1 Kentley teach wherein the vehicle is an autonomous vehicle (see [p][0040] and Fig 1).
Regarding claim 8, Kentley in view of Ansari teach the system of claim 7 Kentley teach being further configured to output the position of the proximate object to a trajectory planning module of the autonomous vehicle ([p]ath calculator 112 may be configured to generate data representing a trajectory of the autonomous vehicle – see [p][0043]). Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 9, Kentley teaches a method (method to implement active safety systems in an autonomous vehicle – see [p][0004]) comprising: receiving input object data (332 and 334 sensor data – see Fig 3B and [p][0076]) from a subsystem of vehicle (320, sensor system - Fig 3B), the input object data including image data from an image generating device (373, cameras – see Fig 3B) and distance data from a distance measuring device (375, LIDARS – see Fig 3B); determining a first position of a proximate object near the vehicle from the image data (a collision between the autonomous vehicle and an object may be predicted based the autonomous vehicle… the object location 257 in a first location – [p][0058]); determining a second position ([t]he object location 257 may change from a first location to a next location due to motion of an object in the environment – [p][0058]) of the proximate object from the distance data (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, such as sensor data from LIDAR 371 – see [p][0076]); correlating the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data (stage 408 may determine that the detected object is of another class and may analyze at a stage 410, based on data accessed from object data store 426, the data representing the object to determine that the classification matches a person riding a skateboard and output data representing the object classification 411 – see [p][0085]); determining a position of the proximate object using the correlated first and second positions (stage 412 a determination may be made that the detected object is in motion and at the stage 418 the object track may be set to dynamic (D) and the location of the object may be tracked at the stage – see [p][0085]); and using the position of the proximate object to navigate the vehicle ([t]he region 1644 may be designated (e.g., by the planner system) as an open region 1645. The planner system may command the drive system (e.g., via steering, propulsion, and braking data) to alter the trajectory of the vehicle 100 from its original trajectory Tav to an avoidance maneuver trajectory Tm, to autonomously navigate the autonomous vehicle 100 into the open region 1645 – see [p][0149]); wherein the method further comprises: correlating positions and velocities of proximate objects found in a previous processing cycle with proximate objects identified in a current processing cycle (([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see [p][0047]). .
Although Kentley teaches that the LIDAR may be three-dimensional (3D Lidar data – see [p][0081]); Kentley does not expressly teach three-dimensional (3D) position and the use of the 3D position. However, Ansari explicitly teach three-dimensional (3D) position (adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information – see [p][0002]) and the use of the 3D position (using the combined comprehensive 3D model with detailed map information to maneuver the vehicle – see [p][0002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having three-dimensional (3D) position and the use of the 3D position.
Wherein having Kentley three-dimensional (3D) position and the use of the 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 10, Kentley in view of Ansari teach the method of claim 9 Kentley teaches wherein the image generating device is one or more cameras (73, cameras – see Fig 3B).
Regarding claim 11, Kentley in view of Ansari teach the method of claim 9 Kentley teaches wherein the second position is a position (a next location due to motion of an object in the environment – [p][0058]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 12, Kentley in view of Ansari teach the method of claim 9 Kentley teaches being further configured to determining the first position as a two-dimensional (2D) position of the proximate object using the image data received from the image generating device (stage 408 may determine that the detected object is of another class and may analyze at a stage 410, based on data accessed from object data store 426, the data representing the object to determine that the classification matches a person riding a skateboard and output data representing the object classification 411 – see [p][0085]) the image generating device being a plurality of cameras (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, s…, Cameras 373 – see [p][0076]).
Regarding claim 13, Kentley in view of Ansari teach the method of claim 9, Kentley teaches including determining the position of the proximate object using a two-dimensional (2D) position and a point cloud generated by one or more light imaging, detection, and ranging (LIDAR) sensors (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, such as sensor data from LIDAR 371, Cameras 373 – see [p][0076] and in the case of using a 3D Lidar would create point cloud data – see [p][0069]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 14, Kentley in view of Ansari teach the method of claim 9 Kentley teaches being further configured to retain the position of the proximate object as tracking data over the plurality of processing cycles ([o]bject dynamics determination 735 may be further configured to access an object dynamics data store 726. Object dynamics data store 726 may include data representing object dynamics. Object dynamics determination 735 may be configured to compare data representing object dynamics with the data representing the object type 733 and the data representing the object location 721 to determine data representing a predicted object motion 737 – see [p][0098]) a memory device (see [p][0069]) of an in-vehicle control system (301, autonomous vehicle system – see Fig 3A and [p][0067]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 15, Kentley in view of Ansari teach the method of claim 9 Kentley teaches including tracking the position of the proximate object ([o]bject dynamics determination 735 may be further configured to access an object dynamics data store 726. Object dynamics data store 726 may include data representing object dynamics. Object dynamics determination 735 may be configured to compare data representing object dynamics with the data representing the object type 733 and the data representing the object location 721 to determine data representing a predicted object motion 737 – see [p][0098]) and determining a velocity of the proximate object using the position and the tracking data ([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see [p][0047]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 17, Kentley in view of Ansari teach the method of claim 9 Kentley teaches including generating an alert if the 3D position of the proximate object may intersect with a trajectory of the vehicle (an obstacle avoidance maneuvering in an autonomous vehicle 100. In FIG. 16C, an object 1661 has a predicted location Lo that may result in a collision (e.g., a rear-ending) of autonomous vehicle 100. Planner system may determine a predicted impact zone (e.g., a region or probabilities where a collision might occur based on object dynamics). Prior to a predicted collision occurring, planner system may analyze the environment around the vehicle 100 (e.g., using the overlapping sensor fields of the sensor in the sensor system) to determine if there is an open region the vehicle 100 may be maneuvered into – see [p][0152]).
Regarding independent claim 18, Kentley teaches a non-transitory machine-useable storage medium (non-transitory computer readable medium – see [p][0105]) embodying instructions (algorithms – see [p][0105]) which, when executed by at least one processor (10, one or more processors - see Fig 8 and [p][0102]), cause the at least one processor to: receive input object data (332 and 334 sensor data – see Fig 3B and [p][0076]) from a subsystem of vehicle (320, sensor system - Fig 3B), the input object data including image data from an image generating device (373, cameras – see Fig 3B) and distance data from a distance measuring device (375, LIDARS – see Fig 3B); determine a first position of a proximate object near the vehicle from the image data (a collision between the autonomous vehicle and an object may be predicted based the autonomous vehicle… the object location 257 in a first location – [p][0058]); determine a second position ([t]he object location 257 may change from a first location to a next location due to motion of an object in the environment – [p][0058]) of the proximate object from the distance data (perception system 340 may receive sensor data 334 relevant to determine information associated with objects in environment 390, such as sensor data from LIDAR 371 – see [p][0076]); correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data (stage 408 may determine that the detected object is of another class and may analyze at a stage 410, based on data accessed from object data store 426, the data representing the object to determine that the classification matches a person riding a skateboard and output data representing the object classification 411 – see [p][0085]); determine a position of the proximate object using the correlated first and second positions (stage 412 a determination may be made that the detected object is in motion and at the stage 418 the object track may be set to dynamic (D) and the location of the object may be tracked at the stage – see [p][0085]); and use the position of the proximate object to navigate the vehicle ([t]he region 1644 may be designated (e.g., by the planner system) as an open region 1645. The planner system may command the drive system (e.g., via steering, propulsion, and braking data) to alter the trajectory of the vehicle 100 from its original trajectory Tav to an avoidance maneuver trajectory Tm, to autonomously navigate the autonomous vehicle 100 into the open region 1645 – see [p][0149]); wherein non-transitory machine-useable storage medium is further configured to correlate positions of proximate objects found in a previous processing cycle with proximate objects identified in a current processing cycle (([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see [p][0047]). ..
Although Kentley teaches that the LIDAR may be three-dimensional (3D Lidar data – see [p][0081]); Kentley does not expressly teach three-dimensional (3D) position and the use of the 3D position. However, Ansari explicitly teach three-dimensional (3D) position (adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information – see [p][0002]) and the use of the 3D position (using the combined comprehensive 3D model with detailed map information to maneuver the vehicle – see [p][0002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having three-dimensional (3D) position and the use of the 3D position.
Wherein having Kentley three-dimensional (3D) position and the use of the 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 20, Kentley in view of Ansari teach the method of claim 18 Kentley teaches being further configured to determine a velocity of the proximate object([a] kinematics calculator 115 may be configured to compute data representing one or more scalar and/or vector quantities associated with motion of the object 180 in the environment 190, including velocity – see p][0047]) using the position and tracking data corresponding to the position of the proximate object tracked over multiple processing cycles ([o]bject dynamics determination 735 may be further configured to access an object dynamics data store 726. Object dynamics data store 726 may include data representing object dynamics. Object dynamics determination 735 may be configured to compare data representing object dynamics with the data representing the object type 733 and the data representing the object location 721 to determine data representing a predicted object motion 737 – see [p][0098]).
Kentley does not explicitly teach a 3D position.
However, Ansari explicitly teach a 3D position (generating a 3D model of a sensor's field of view – see [p][002]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Ansari of having a 3D position.
Wherein having Kentley a 3D position.
The motivation behind the modification would have been to generate a 3D model of a sensor's field of view to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle since both Kentley and Ansari are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Ansari generates a 3D model of a sensor's field of view and using the generated 3D model for maneuvering the vehicle (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of Ansari (US 2016/0357188 A1)).
Regarding claim 21, Kentley in view of Ansari teach the system of claim 1, wherein the second position is tracked using the distance data received from the distance measuring device over a plurality of processing cycles [o]bject dynamics determination 735 may be further configured to access an object dynamics data store 726. Object dynamics data store 726 may include data representing object dynamics. Object dynamics determination 735 may be configured to compare data representing object dynamics with the data representing the object type 733 and the data representing the object location 721 to determine data representing a predicted object motion 737 – see [p][0098]; the distance data is collected during at least one of the plurality of processing cycles (see [p][0098]).
Claim 22 is rejected under 35 U.S.C. 103(a) as being unpatentable over Kentley et al. (US 2017/0120904 A1) in view of Ansari (US 2016/0357188 A1) as applied to claim 1 further in view of Song et al (NPL titled: Robust Vision-Based Relative-Localization Approach Using an RGB-Depth Camera and LiDAR Sensor Fusion).
Regarding claim 22, Kentley in view of Ansari teach the system of claim 1, Kentley in view of Ansari does not explicitly teach wherein an object missing from a current distance data is still checked for presence in a subsequent cycle in case the current distance data is incomplete, errant, or otherwise compromised.
Song explicitly teaches wherein an object missing from a current distance data is still checked for presence in a subsequent cycle in case the current distance data is incomplete, errant, or otherwise compromised (If the visual tracker fails to find the correct ROI, the localization results using (3) become meaningless. To overcome this critical problem, we propose a depth-tracking approach that uses the depth image from a structured light sensor—viz., the Xtion Pro RGB-D camera. With the proposed approach, depth-only point tracking is performed by segmenting the depth image with region thresholding and an IMM estimator. At first, to remove the background, the initially detected ROI is used to determine the depth of the target. The initial depth of the target is detected with the RANdom SAmple Consensus (RANSAC) plane-fitting algorithm [26]. The RANSAC plane-fitting algorithm detects and removes plane segments from the depth image, and derives the target depth, as shown in Fig. 4 (the green ROI has the largest segmented area).- see section III, subsection C).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kentley as modified by Ansari of having the system comprising: a data processor; and a vehicle position and velocity estimation module which, when executed by the data processor with the teachings of Song of wherein an object missing from a current distance data is still checked for presence in a subsequent cycle in case the current distance data is incomplete, errant, or otherwise compromised.
Wherein having Kentley wherein an object missing from a current distance data is still checked for presence in a subsequent cycle in case the current distance data is incomplete, errant, or otherwise compromised.
The motivation behind the modification would have been to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle by generating modified track-to-track fusion scheme which produces robust localization results, even when one of the trackers fails since both Kentley and Song are method and systems for autonomous navigation wherein Kentley implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself, while Song for generating modified track-to-track fusion scheme which produces robust localization results, even when one of the trackers fails (Please see Abstract of Kentley et al (US 2017/0120904 A1), and [p][0002] of song et al (NPL titled: Robust Vision-Based Relative-Localization Approach Using an RGB-Depth Camera and LiDAR Sensor Fusion), see abstract).
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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-15, 17-18 and 20-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11557128 (herein referred to as Patent’128). Although the claims at issue are not identical, they are not patentably distinct from each other because Patent’128 discloses the first position being determined using semantic segmentation processing of the image data, the semantic segmentation processing including assigning an object categorical label to each pixel in the image data and over a plurality of processing cycles, the input object data being collected during at least one of the plurality of processing cycles not required by the instant claims.
Instant claims
US Patent No.: 11557128
1 A system comprising:a data processor;
1. A system comprising: a data processor;
and a vehicle position and velocity estimation module which, when executed by the data processor, causes the system to:
and a vehicle position and velocity estimation module which, when executed by the data processor, causes the system to:
receive input object data from a subsystem of vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device;
receive input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more light imaging, detection, and ranging (LIDAR) sensors;
determine a first position of a proximate object near the vehicle from the image data;
determine a first position of a proximate object near the autonomous vehicle from the image data, the first position being determined using semantic segmentation processing of the image data, the semantic segmentation processing including assigning an object categorical label to each pixel in the image data
determine a second position of the proximate object from the distance data;
determine a second position of the proximate object from the distance data, the second position being tracked using the distance data received from the distance measuring device over a plurality of processing cycles, the distance data being collected during at least one of the plurality of processing cycles;
correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data;
correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data;
determine a three-dimensional (3D) position of the proximate object using the correlated first and second positions;
determine a three-dimensional (3D) position of the proximate object using the correlated first and second positions;
and use the 3D position of the proximate object to navigate the vehicle.
and track the 3D position of the proximate object over a plurality of processing cycles, the input object data being collected during at least one of the plurality of processing cycles.
Claims 1-15, 17-18 and 20-21are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,557,128 (herein referred to as Patent’128). Although the claims at issue are not identical, they are not patentably distinct from each other because Patent’128 discloses “determine a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determine a velocity of the proximate object using the 3D position and the tracking data; and output the 3D position and velocity of the proximate object relative to the autonomous vehicle.
correlate the proximate object identified from the image data with the proximate object identified and tracked from the distance data, the correlation being configured to match the 2D position of the proximate object detected in the image data with the 3D position of the same proximate object detected in the distance data” not required by the instant claims.
Instant claims
US Patent No.: 10552691
1. A system comprising: a data processor;
1. A system comprising: a data processor;
and a vehicle position and velocity estimation module which, when executed by the data processor, causes the system to:
and a vehicle position and velocity estimation module, executable by the data processor, the vehicle position and velocity estimation module being configured to
receive input object data from a subsystem of vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device;
perform a proximate object position and velocity estimation operation for an autonomous vehicle, the proximate object position and velocity estimation operation being configured to: receive input object data from a subsystem of the autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device being one or more light imaging, detection, and ranging (LIDAR) sensors;
determine a first position of a proximate object near the vehicle from the image data;
determine a second position of the proximate object from the distance data;
determine a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device and semantic segmentation processing of the image data; track a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generate tracking data;
correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data;
correlate the proximate object identified from the image data with the proximate object identified and tracked from the distance data, the correlation being configured to match the 2D position of the proximate object detected in the image data with the 3D position of the same proximate object detected in the distance data; determine a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determine a velocity of the proximate object using the 3D position and the tracking data; and output the 3D position and velocity of the proximate object relative to the autonomous vehicle.
correlate the proximate object identified from the image data with the proximate object identified and tracked from the distance data, the correlation being configured to match the 2D position of the proximate object detected in the image data with the 3D position of the same proximate object detected in the distance data;
determine a three-dimensional (3D) position of the proximate object using the correlated first and second positions;
determine a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data;
and use the 3D position of the proximate object to navigate the vehicle.
determine a velocity of the proximate object using the 3D position and the tracking data; and output the 3D position and velocity of the proximate object relative to the autonomous vehicle
.
Claims 1-15, 17-18 and 20-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11928868 (herein referred to as Patent’868). Although the claims at issue are not identical, they are not patentably distinct from each other because Patent’868 discloses the distance data being collected during at least one of the plurality of processing cycles; not required by the instant claims.
Instant claims
US Patent No.: 11928868
1. A system comprising: a data processor;
1. A system comprising: a data processor;
and a vehicle position and velocity estimation module which, when executed by the data processor, causes the system to:
and a vehicle position and velocity estimation module which, when executed by the data processor, causes the system to:
receive input object data from a subsystem of vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device;
receive input object data from a subsystem of vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more light imaging, detection, and ranging (LIDAR) sensors;
determine a first position of a proximate object near the vehicle from the image data;
determine a first position of a proximate object near the vehicle from the image data;
determine a second position of the proximate object from the distance data;
determine a second position of the proximate object from the distance data, the second position being tracked using the distance data received from the distance measuring device over a plurality of processing cycles, the distance data being collected during at least one of the plurality of processing cycles;
correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data;
correlate the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data;
determine a three-dimensional (3D) position of the proximate object using the correlated first and second positions;
and use the 3D position of the proximate object to navigate the vehicle.
determine a three-dimensional (3D) position of the proximate object using the correlated first and second positions; and use the 3D position of the proximate object to navigate the vehicle.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
May 30, 2026
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