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 Amendment
Claims 1, 6, 12, 15, have been amended.
Claims 21-22 are new.
Claims 3 and 17 have been canceled.
The amendments submitted 05-26-2026 are being considered by the examiner.
Claims 1-2, 4-16, and 18-22 are currently pending. The official correspondence below is an after non-final.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-11, 15-16, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Ostafew (US 20210031760 A1) in view of Maurer (US 20150210311 A1) and Gochev (US 20190161080 A1).
REGARDING CLAIM 1, Ostafew discloses, a sensor (Ostafew: [0041]); one or more processors (Ostafew: [0006]); and memory (Ostafew: [0006]) storing processor-executable instructions (Ostafew: [0006]) that, when executed by the one or more processors, configure the vehicle to: receive sensor data of an environment from the sensor (Ostafew: [0032]); identify an object at an object location in the environment based on the sensor data (Ostafew: [0044]), wherein the object is currently blocking a first vehicle trajectory associated with the vehicle (Ostafew: [0154]); and the object is currently blocking the first vehicle trajectory (Ostafew: [0154] when the adjusted drivable area, accounting for static objects, contains a static blockage, the process 800 adjusts the discrete-time speed plan such that the AV comes to a stop a prescribed distance before the static blockage) based at least in part on the object location being within a drivable area associated with the first vehicle trajectory (Ostafew: [0162] At operation 870, the process 800 adjusts the discrete-time speed plan for dynamic objects. When the adjusted drivable area (accounting for both static and dynamic objects at this point) contains a dynamic object travelling in the same direction ... the AV follows the blocking object at a comfortable speed and distance; [0208] the longitudinal constraint object impedes (i.e., obstructs) the path of the AV at the current speed of the AV. As such, the trajectory planner of the AV may not need to adjust the trajectory of the AV but may need to adjust (such as, by the discrete-time speed plan module 528 of the trajectory planner) the speed of the AV to avoid a collision with the dynamic object); determine a predicted object trajectory associated with the object (Ostafew: [0155-0157]) moving through the environment (Ostafew: [0035] An external object can be a dynamic (i.e., moving) object, such as a pedestrian, a remote vehicle, a motorcycle, a bicycle, etc. The dynamic object can be oncoming (toward the vehicle) or can be moving in the same direction as the vehicle. The dynamic object can be moving longitudinally or laterally with respect to the vehicle. A static object can become a dynamic object, and vice versa) wherein the predicted object trajectory is based at least in part on a classification associated with the object (Ostafew: [0216]; [0242]), the classification being based at least in part on object data (Ostafew: [0095]) comprising at least one of an object size, the object location, or an object action (Ostafew: [0095]); determine a likelihood (Ostafew: [0043]) that the object, over a portion of the predicted object trajectory associated with the object moving through the environment in a same direction of travel (Ostafew: [0060]; [0090]) as the vehicle in a same lane as the vehicle (Ostafew: [0035]; [0090]; [FIG. 3(344)]), continues to block the first vehicle trajectory for a non-zero threshold period of time (Ostafew: [ABS] determining a time of arrival of the AV at the hazard zone; determining a contingency trajectory for the AV; controlling the AV according to the contingency trajectory; and, in response to the hazard object intruding into the path of the AV, controlling the AV to perform a maneuver to avoid the hazard object. The contingency trajectory includes at least one of a lateral contingency or a longitudinal contingency. The contingency trajectory is determined using the time of arrival of the AV at the hazard zone; [0039]); determine that the likelihood meets or exceeds a threshold likelihood (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine a confidence associated with an accuracy of the predicted object trajectory (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine, based at least in part on the confidence, a modified drivable area associated with navigating the environment (Ostafew: [0047]; [0090]; [FIG. 3(348, 366)]; [0262-0264]); determine a second vehicle trajectory based on the predicted object trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), the modified drivable area (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), and the confidence (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), wherein the second vehicle trajectory is associated with the vehicle navigating around the object (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]); and control the vehicle according to the second vehicle trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]).
To the examiner’s best understanding, Ostafew discloses “limits the first vehicle trajectory for a non-zero threshold period of time” [ABS].
However, alternatively, and in the same field of endeavor, Maurer discloses, continues to block the first vehicle trajectory for a non-zero threshold period of time (Maurer: [0045]), for the benefit of minimizing collision probability.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Ostafew to include a plurality of threshold times before performing an avoidance maneuver taught by Maurer. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to minimize collision probability.
The examiner respectfully submits, Ostafew, as modified, discloses, the object is currently blocking the first vehicle trajectory based at least in part on the object location being within a drivable area associated with the first vehicle trajectory; moving through the environment continues to block the first vehicle trajectory for a non-zero threshold period of time.
However, in the alternative, and in the same field of endeavor, Gochev discloses, “[0028] the feature(s) can include a location of the object relative to a travel way (e.g., relative to the left or right lane markings, curbs, etc.), a location of the object relative to the autonomous vehicle (e.g., a distance between the location of the vehicle and the object), one or more characteristic(s) of the object relative to a planned vehicle trajectory and/or route associated with the autonomous vehicle (e.g., whether the object is moving parallel, towards, or away from the vehicle's current/future motion trajectory/travel route or a predicted point of intersection with the vehicle), etc. In some implementations, the feature(s) determined for a particular object may depend at least in part on the class of that object. For example, the predicted path for a vehicle or bicycle traveling on a roadway may be different than that associated with a pedestrian traveling on a sidewalk; [0030] For instance, the vehicle computing system can include, employ, and/or otherwise leverage a blocking model configured to determine whether an object within the surrounding environment of the autonomous vehicle is blocking or not blocking the autonomous vehicle. To do so, the vehicle computing system can evaluate the predicted motion trajectory of the object as well as the planned motion trajectory of the vehicle (e.g., the latest planned motion trajectory indicative of the current/future motion of the vehicle). In some implementations, an object can be considered blocking in the event the object is predicted to be located within the planned motion trajectory of the autonomous vehicle (e.g., with distance buffers on either side of the trajectory) and/or within a travel lane of the autonomous vehicle; [0033] The vehicle computing system (e.g., the motion planning system) can input data into the blocking model and receive an output. For instance, the vehicle computing system (e.g., the motion planning system) can obtain data indicative of the blocking model from an accessible memory onboard the autonomous vehicle and/or from a memory that is remote from the vehicle (e.g., via a wireless network). The vehicle computing system can input the predicted motion trajectory of the object into the blocking model. In some implementations, the vehicle computing system can input other data into the blocking model such as, for example, map data, data associated with the autonomous vehicle, etc. The blocking model can process the data to determine whether the object is blocking or is not blocking the autonomous vehicle at each respective time step of the predicted trajectory. For instance, at each time step, the blocking model can determine if the object is blocking the autonomous vehicle based on the predicted position of that object within a travel lane, relative to the vehicle's planned path; [0062], [0064], [0080], [0088]”, for the benefit of determining an optimized motion plan.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Ostafew to include performing an avoidance maneuver when the vehicle is in the same lane and traveling in the same direction taught by Gochev. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to determining an optimized motion plan.
REGARDING CLAIM 2, Ostafew, as modified, remain as applied above to claim 1, and further, Maurer also discloses, determine a first distance between a lane marker depicting an edge of a lane and the location associated with the object (Maurer: [FIG. 2]); determine a second distance comprising the first distance minus a width of the vehicle (Maurer: [FIG. 2]); and determine a vehicle speed associated with the second distance (Maurer: [0034]; [0045]); wherein the second vehicle trajectory is associated with navigating around the object at the second distance and the vehicle speed (Maurer: [0034]; [0045]).
Maurer does not explicitly recite the terminology "and determine a vehicle speed associated with the distance". However, Maurer discloses a second state of motion, orientation, speed, and acceleration of a vehicle, and time to collision with pedestrian. It is the examiners assertion that time-to-collision teaches "speed associated with distance". Thus, teaching "determine a vehicle speed associated with the second distance". Additionally, duplication (or repeating) of essential parts/steps is of ordinary skill in the art and does not set apart the claimed subject matter from the prior art.
REGARDING CLAIM 5, Ostafew, as modified, remain as applied above to claim 1, and further, Maurer also discloses, the predicted object trajectory is based on at least one of: a machine learned algorithm; a top-down representation of the environment; a discretized probability distribution; a temporal logic formula; or a tree search method (Maurer: [ABS]).
The above description is interpreted as top-down or stepwise, which are parallel teachings.
REGARDING CLAIM 6, Ostafew discloses, identify an object at an object location in the environment based on the sensor data (Ostafew: [0044]), wherein the object is currently blocking a first vehicle trajectory associated with the vehicle (Ostafew: [0154]); and the object is currently blocking the first vehicle trajectory (Ostafew: [0154]) based at least in part on the object location being within a drivable area associated with the first vehicle trajectory (Ostafew: [0162]; [0208]); determine a predicted object trajectory associated with the object (Ostafew: [0155-0157]) moving through the environment (Ostafew: [0035]) wherein the predicted object trajectory is based at least in part on a classification associated with the object (Ostafew: [0216]; [0242]), the classification being based at least in part on object data (Ostafew: [0095]) comprising at least one of an object size, the object location, or an object action (Ostafew: [0095]); determine a likelihood (Ostafew: [0043]) that the object, over a portion of the predicted object trajectory associated with the object moving through the environment in a same direction of travel (Ostafew: [0060]; [0090]) as the vehicle in a same lane as the vehicle (Ostafew: [0035]; [0090]; [FIG. 3(344)]), continues to block the first vehicle trajectory for a non-zero threshold period of time (Ostafew: [ABS]; [0039]); determine that the likelihood meets or exceeds a threshold likelihood (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine a confidence associated with an accuracy of the predicted object trajectory (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine, based at least in part on the confidence, a modified drivable area associated with navigating the environment (Ostafew: [0047]; [0090]; [FIG. 3(348, 366)]; [0262-0264]); determine a second vehicle trajectory based on the predicted object trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), the modified drivable area (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), and the confidence (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), wherein the second vehicle trajectory is associated with the vehicle navigating around the object (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]); and control the vehicle according to the second vehicle trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]).
To the examiner’s best understanding, Ostafew discloses “limits the first vehicle trajectory for a non-zero threshold period of time” [ABS].
However, alternatively, and in the same field of endeavor, Maurer discloses, continues to block the first vehicle trajectory for a non-zero threshold period of time (Maurer: [0045]), for the benefit of minimizing collision probability.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Ostafew to include a plurality of threshold times before performing an avoidance maneuver taught by Maurer. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to minimize collision probability.
REGARDING CLAIM 7, Ostafew, as modified, remain as applied above to claim 6, and further, Maurer teaches, the vehicle trajectory is a first vehicle trajectory and wherein determining that the object is currently blocking the vehicle trajectory is based determining that the object impedes a progress of the vehicle along the first vehicle trajectory (Maurer: [0045]; [FIG. 2, 4]), the method further comprising: determining a threshold distance to navigate around the object (Maurer: [FIG. 4]); and determining a second vehicle trajectory based on the threshold distance (Maurer: [0045]), wherein controlling the vehicle comprises causing the vehicle to traverse the environment based the second vehicle trajectory (Maurer: [ABS]).
REGARDING CLAIM 8, Ostafew, as modified, remain as applied above to claim 6, and further, Maurer also teaches, determining the likelihood that the object will continue to block the vehicle trajectory comprises: determining an object trajectory (Maurer: [0025]); and determining, based on the object trajectory, that the object will substantially remain at the location that is associated with the vehicle trajectory and will continue to impede the progress of the vehicle traveling on the vehicle trajectory (Maurer: [ABS]; [0045]).
REGARDING CLAIM 9, Ostafew, as modified, remain as applied above to claim 6, and further, controlling the vehicle comprises causing the vehicle to circumnavigate the object according a second vehicle trajectory (Maurer: [FIG. 2, 4]; [0035-0036]; see FIG. 5.), the method further comprising: identifying a second object based on the sensor data (Maurer: [0037]; see FIG. 5.); determining a second object trajectory associated with the second object; determining an intersection between the second object trajectory and the second vehicle trajectory; controlling the vehicle based on the intersection (Maurer: [0037-0041]).
REGARDING CLAIM 10, Ostafew, as modified, remain as applied above to claim 6, and further, the location of the object is a first location of the object, the method further comprising: determining a second location of the object (Maurer: [FIG. 2]); determining that the second location of the object does not impede a progress of the vehicle traveling on the vehicle trajectory (Maurer: [FIG. 2] T, T'); determining, based on the second location of the object not impeding the progress of the vehicle, that the object is not at blocking the vehicle trajectory (Maurer: [FIG. 2] T, T'); and controlling the vehicle according to the vehicle trajectory (Maurer: [FIG. 2, 4]; [0035-0036]; see FIG. 5.).
REGARDING CLAIM 11, Ostafew, as modified, remain as applied above to claim 6, and further, Maurer also teaches, the likelihood that the object will continue to block the vehicle trajectory is based on at least one of: a classification of the object (Maurer: [FIG. 2]; [0045]); a position of the object (Maurer: [0032]); the location of the object in the environment (Maurer: [0032]); a size of the object (Maurer: [0040]); a level of stability associated with the object (Maurer: [0032]); a velocity of the object (Maurer: [0032]); or a change in the velocity of the object (Maurer: [0032]).
REGARDING CLAIM 15, Ostafew discloses, identify an object at an object location in the environment based on the sensor data (Ostafew: [0044]), wherein the object is currently blocking a first vehicle trajectory associated with the vehicle (Ostafew: [0154]); and the object is currently blocking the first vehicle trajectory (Ostafew: [0154]) based at least in part on the object location being within a drivable area associated with the first vehicle trajectory (Ostafew: [0162]; [0208]); determine a predicted object trajectory associated with the object (Ostafew: [0155-0157]) moving through the environment (Ostafew: [0035]) wherein the predicted object trajectory is based at least in part on a classification associated with the object (Ostafew: [0216]; [0242]), the classification being based at least in part on object data (Ostafew: [0095]) comprising at least one of an object size, the object location, or an object action (Ostafew: [0095]); determine a likelihood (Ostafew: [0043]) that the object, over a portion of the predicted object trajectory associated with the object moving through the environment in a same direction of travel (Ostafew: [0060]; [0090]) as the vehicle in a same lane as the vehicle (Ostafew: [0035]; [0090]; [FIG. 3(344)]), continues to block the first vehicle trajectory for a non-zero threshold period of time (Ostafew: [ABS]; [0039]); determine that the likelihood meets or exceeds a threshold likelihood (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine a confidence associated with an accuracy of the predicted object trajectory (Ostafew: [0090]; [0096]; [0100], [0318], [0323]); determine, based at least in part on the confidence, a modified drivable area associated with navigating the environment (Ostafew: [0047]; [0090]; [FIG. 3(348, 366)]; [0262-0264]); determine a second vehicle trajectory based on the predicted object trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), the modified drivable area (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), and the confidence (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]), wherein the second vehicle trajectory is associated with the vehicle navigating around the object (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]); and control the vehicle according to the second vehicle trajectory (Ostafew: [0090]; [FIG. 3(348, 366)]; [0262-0264]).
To the examiner’s best understanding, Ostafew discloses “limits the first vehicle trajectory for a non-zero threshold period of time” [ABS].
However, alternatively, and in the same field of endeavor, Maurer discloses, continues to block the first vehicle trajectory for a non-zero threshold period of time (Maurer: [0045]), for the benefit of minimizing collision probability.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Ostafew to include a plurality of threshold times before performing an avoidance maneuver taught by Maurer. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to minimize collision probability.
REGARDING CLAIM 16, Ostafew, as modified, remain as applied above to claim 15, and further, Maurer teaches, determining a first distance between a lane marker depicting an edge of a lane and the location of the object (Maurer: [FIG. 2]); determining a second distance comprising the first distance minus a width of the vehicle (Maurer: [FIG. 2]); and determining a second vehicle trajectory based at least in part on the second distance (Maurer: [0034]; [0045]), wherein controlling the vehicle comprises causing the vehicle to travel according to the second vehicle trajectory (Maurer: [0034]; [0045]).
REGARDING CLAIM 20, Ostafew, as modified, remain as applied above to claim 15, and further, Maurer also teaches, the likelihood that the object will continue to block the vehicle trajectory is based on at least one of: a classification of the object (Maurer: [FIG. 2]; [0045]); a position of the object (Maurer: [0032]); the location of the object in the environment (Maurer: [0032]); a size of the object (Maurer: [0040]); a level of stability associated with the object (Maurer: [0032]); a velocity of the object (Maurer: [0032]); or a change in the velocity of the object (Maurer: [0032]).
REGARDING CLAIM 21, Ostafew, as modified, remain as applied above to claim 1, and further, Ostafew, as modified, also discloses, determine a first set of costs (Gochev: [0025] considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan) associated with stopping (Gochev: [0019] A vehicle action can be indicative of whether the autonomous vehicle should, for example, stay ahead of the object (e.g., pass, maintain lead distance, etc.), stay behind the object (e.g., queue, stop, etc.), and/or ignore that object during that time step); determine a second set of costs (Gochev: [0025] considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan) associated with navigating around the object (Gochev: [0019] A vehicle action can be indicative of whether the autonomous vehicle should, for example, stay ahead of the object (e.g., pass, maintain lead distance, etc.), stay behind the object (e.g., queue, stop, etc.), and/or ignore that object during that time step; [0025] which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. By way of example, the motion planning system can determine that the vehicle can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.)); determine a third set of costs associated with yielding to the object (Gochev: [0038] the vehicle action model can determine that the autonomous vehicle should queue behind the pedestrian (e.g., decelerate, stop, etc.); [0077] The second set of labels 312B-D can indicate that the vehicle 304 is to queue for (e.g., decelerate for, stop for, yield for, etc.)); comparing the sets of costs (Gochev: [0025] The motion planning system can determine a motion plan for the autonomous vehicle based at least in part on the predicted data (and/or other data). The motion plan can include vehicle actions with respect to the objects within the surrounding environment of the autonomous vehicle as well as the predicted movements. For instance, the motion planning system can implement an optimization planner that includes an optimization algorithm, which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. By way of example, the motion planning system can determine that the vehicle can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.). A motion plan can include a planned motion trajectory of the autonomous vehicle. The planned motion trajectory can be indicative of a trajectory that the autonomous vehicle is to follow for a particular time period. The motion plan can also indicate speed(s), acceleration(s), and/or other operating parameters/actions of the autonomous vehicle); and determining a minimum cost action for the vehicle based at least in part on comparing the sets of costs (Gochev: [0025] considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan; [0038] The vehicle computing system can determine a motion of the autonomous vehicle based at least in part on the vehicle action sequence. For instance, the motion planning system can generate cost data indicative of an effect of performing the respective vehicle action for each time step. The cost data can include a cost function indicative of a cost (e.g., over time) of controlling the motion of the autonomous vehicle (e.g., the trajectory, speed, or other controllable parameters of the autonomous vehicle) to perform the respective vehicle action (e.g., pass, queue behind, ignore, etc.). The autonomy computing system can determine a motion plan for the autonomous vehicle based at least in part on the cost data. For example, an optimizer can consider the cost data associated with the respective vehicle actions of the vehicle action sequence as well as other cost functions to determine optimized variables that make up the motion plan. For example, based on the vehicle action sequences, objects can be determined as leading_actors, trailing_actors, and/or pass_actors. The motion planning system can generate fences for each of the objects (e.g., ACC fences for leading_actors and push fences for trailing_actors). The motion planning system can determine a planned motion trajectory for the vehicle to follow based at least in part on these fences).
Claims 4, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ostafew (US 20210031760 A1) in view of Maurer (US 20150210311 A1) and Gochev (US 20190161080 A1) as applied to claim 15 above, and further in view of Paris (US 20180326982 A1).
REGARDING CLAIM 4, Ostafew, as modified, remain as applied above to claim 1, and further, Maurer discloses, determine that a motion of the object is in accordance with the predicted object trajectory (Maurer: [ABS]); wherein determining the second vehicle trajectory is based on determining that the motion of the object is in accordance with the predicted object trajectory (Maurer: [ABS]).
Ostafew, as modified, does not explicitly disclose, emit at least one of an audio signal or a visual signal in a direction associated with the object.
However, in the same field of endeavor, Paris discloses, “In one variation, the autonomous vehicle can also selectively execute Block S150 based on whether the autonomous vehicle has determined that the pedestrian has visually observed the autonomous vehicle. For example, the autonomous vehicle can implement methods and techniques described above to estimate the gaze of the pedestrian in Block S120. If the autonomous vehicle determines that the pedestrian's gaze has not yet met the autonomous vehicle and the pedestrian is within a threshold distance of the autonomous vehicle's planned route, the pedestrian's estimated path is within a threshold distance of the autonomous vehicle's planned route, and/or the confidence score for the pedestrian's path is less than the threshold confidence, the autonomous vehicle can: broadcast an audio track (e.g., an audible alarm signal); track the pedestrian following replay of the audio track in Block S122, as described below; and then cease broadcast of the audio track once the autonomous vehicle determines that the gaze of the pedestrian has intersected the autonomous vehicle” (Paris: [0047]); [FIG. 1] Element S150, based on object location; [FIG. 2] emitting at least one of an audio signal or a visual signal in a direction based at least in part on the location of the object can be observed, for the benefit of calculating a revised intent of the pedestrian based on actions of the pedestrian following replay of the audio track.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Ostafew to include broadcasting an alarm taught by Paris. One of ordinary skill in the art would have been motivated to make this modification in order to calculate a revised intent of the pedestrian based on actions of the pedestrian following replay of the audio track.
REGARDING CLAIM 12, Ostafew, as modified, remain as applied above to claim 6, and further, Ostafew, as modified, does not explicitly disclose, emitting at least one of an audio signal or a visual signal in a direction based at on the location of the object; and performing at least one of: based on determining that the location of the object remains the same, controlling the vehicle around the location; or based on determining a change in the location of the object to a second location that does not cause the object to block the vehicle trajectory, controlling the vehicle according to the vehicle trajectory.
However, in the same field of endeavor, Paris teaches, “In one variation, the autonomous vehicle can also selectively execute Block S150 based on whether the autonomous vehicle has determined that the pedestrian has visually observed the autonomous vehicle. For example, the autonomous vehicle can implement methods and techniques described above to estimate the gaze of the pedestrian in Block S120. If the autonomous vehicle determines that the pedestrian's gaze has not yet met the autonomous vehicle and the pedestrian is within a threshold distance of the autonomous vehicle's planned route, the pedestrian's estimated path is within a threshold distance of the autonomous vehicle's planned route, and/or the confidence score for the pedestrian's path is less than the threshold confidence, the autonomous vehicle can: broadcast an audio track (e.g., an audible alarm signal); track the pedestrian following replay of the audio track in Block S122, as described below; and then cease broadcast of the audio track once the autonomous vehicle determines that the gaze of the pedestrian has intersected the autonomous vehicle” (Paris: [0047]); (Paris: [FIG. 1]) Element S150, based on object location; (Paris: [FIG. 2]) emitting at least one of an audio signal or a visual signal in a direction based at least in part on the location of the object can be observed; (Paris: [FIG. 2]) Flow chart, based at least in part on determining that the location of the object remains substantially the same, controlling the vehicle around the location; or based at least in part on determining a change in the location of the object to a second location that does not at least partially block the vehicle trajectory, controlling the vehicle according to the vehicle trajectory, can be observed, for the benefit of collision mitigation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a vehicle disclosed by a modified Ostafew to include pedestrian tracking taught by Paris. One of ordinary skill in the art would have been motivated to make this modification in order to calculate a revised intent of the pedestrian based on actions of the pedestrian following replay of the audio track.
REGARDING CLAIM 13, Ostafew, as modified, remain as applied above to claim 6, and further, Ostafew, as modified, does not explicitly disclose, receiving at least one of an audio signal via a microphone or a visual signal via a camera; and determining that the at least one of the audio signal or the visual signal comprises an indication that the object does not intend to move out of the vehicle trajectory, wherein the likelihood that the object will continue to block the vehicle trajectory is based at least in part on the indication.
However, in the same field of endeavor, Paris teaches, “The autonomous vehicle can additionally or alternatively include: a set of infrared emitters configured to project structured light into a field near the autonomous vehicle; a set of infrared detectors (e.g., infrared cameras); and a processor configured to transform images output by the infrared detector(s) into a depth map of the field. The autonomous vehicle can also include one or more color cameras facing outwardly from the front, rear, and left lateral and right lateral sides of the autonomous vehicle. For example, each camera can output a video feed containing a sequence of digital photographic images (or “frames”), such as at a rate of 20 Hz. Furthermore, the autonomous vehicle can include a set of infrared proximity sensors arranged along the perimeter of the base of the autonomous vehicle and configured to output signals corresponding to proximity of objects and pedestrians within one meter of the autonomous vehicle. The controller within the autonomous vehicle can thus fuse data streams from the LIDAR sensor(s), the color camera(s), and the proximity sensor(s) into one real-time scan image of surfaces (e.g., surfaces of roads, sidewalks, road vehicles, pedestrians, etc.) around the autonomous vehicle per scan cycle, as shown in FIG. 1. Alternatively, the autonomous vehicle can stitch digital photographic images—output by multiple color cameras arranged throughout the autonomous vehicle—into a scan data or 3D point cloud of a scene around the autonomous vehicle” (Paris: [0016]); “the autonomous vehicle can track the pedestrian's motion, revise the predicted intent of the pedestrian according to the pedestrian's post-prompt motion, and calculate an increased confidence score for the revised intent of the pedestrian given stronger direct motion of the pedestrian” (Paris: [0012]), for the benefit of collision mitigation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a vehicle disclosed by Maurer in view of Shalev-Shwartz to include pedestrian tracking taught by Paris. One of ordinary skill in the art would have been motivated to make this modification in order to calculate a revised intent of the pedestrian based on actions of the pedestrian following replay of the audio track.
REGARDING CLAIM 19, Ostafew, as modified, remain as applied above to claim 15, and further, Ostafew, as modified, do not explicitly disclose, the instructions further cause the processors to perform operations comprising: receiving an indication that the object does not intend to move out of the vehicle trajectory, wherein the likelihood that the object will continue to at block the vehicle trajectory is based on the indication.
However, in the same field of endeavor, Paris teaches, “the autonomous vehicle can track the pedestrian's motion, revise the predicted intent of the pedestrian according to the pedestrian's post-prompt motion, and calculate an increased confidence score for the revised intent of the pedestrian given stronger direct motion of the pedestrian” (Paris: [0012]), for the benefit of collision mitigation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a vehicle disclosed by Maurer in view of Shalev-Shwartz to include pedestrian tracking taught by Paris. One of ordinary skill in the art would have been motivated to make this modification in order to mitigate collision with a pedestrian or other object.
Claim 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ostafew (US 20210031760 A1) in view of Maurer (US 20150210311 A1) and Gochev (US 20190161080 A1) as applied to claims 6 and 15 above, and further in view of Zhang (US 20190072966 A1).
REGARDING CLAIM 14, Ostafew, as modified, remain as applied above to claim 6, and further, Ostafew, as modified, discloses maneuvering a vehicle with consideration for passenger comfort [0046].
Ostafew, as modified, does not explicitly disclose, controlling the vehicle comprises determining an action for the vehicle to take, wherein the action comprises at least one of: maintaining a stopped position in the vehicle trajectory; or determining a second vehicle trajectory around the object; and wherein the action is determined based on at least one of: a safety cost associated with the action, wherein the safety cost is on a relative state between the object and the vehicle; a comfort cost associated with the action, wherein the comfort cost is based on the relative state between the object and the vehicle; a progress cost associated with the action, wherein the progress cost is based on a vehicle delay associated with the action; or an operational rules cost associated with the action, wherein the operational rules cost is based on one or more regulations associated with the environment.
However, in the same field of endeavor, Zhang teaches, [0058] The trajectory processing module 173 can score the first proposed trajectory as related to the predicted trajectories for any of the proximate agents. The score for the first proposed trajectory relates to the level to which the first proposed trajectory complies with pre-defined goals for the host vehicle 105, including safety, efficiency, legality, passenger comfort, and the like. Minimum score thresholds for each goal can be pre-defined. For example, score thresholds related to turning rates, acceleration or stopping rates, speed, spacing, etc. can be pre-defined and used to determine if a proposed trajectory for host vehicle 105 may violate a pre-defined goal. If the score for the first proposed trajectory, as generated by the trajectory processing module 173 based on the predicted trajectories for any of the proximate agents, may violate a pre-defined goal, the trajectory processing module 173 can reject the first proposed trajectory and the trajectory processing module 173 can generate a second proposed trajectory, for the benefit of creating alternative trajectories with the safest score possible.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a vehicle disclosed by a modified Ostafew to include safety and comfort scores taught by Zhang. One of ordinary skill in the art would have been motivated to make this modification in order to crate alternative trajectories with the safest score possible.
REGARDING CLAIM 18, Ostafew, as modified, remain as applied above to claim 15, and further, Ostafew, as modified, do not explicitly disclose, controlling the vehicle comprises: determining a first trajectory for the vehicle to take to navigate around the object; determining a second trajectory for the vehicle to take to navigate around the object determining a first cost associated with the first trajectory and a second cost associated with the second trajectory, wherein the first cost and the second cost comprise at least one of a safety cost, a comfort cost, a progress cost, or an operational rules cost associated with the respective trajectories; determining that the first cost associated with the first trajectory is less than the second cost associated with the second trajectory; and causing the vehicle to navigate around the object according to the first trajectory.
However, in the same field of endeavor, Zhang teaches, “The trajectory processing module 173 can score the first proposed trajectory as related to the predicted trajectories for any of the proximate agents. The score for the first proposed trajectory relates to the level to which the first proposed trajectory complies with pre-defined goals for the host vehicle 105, including safety, efficiency, legality, passenger comfort, and the like. Minimum score thresholds for each goal can be pre-defined. For example, score thresholds related to turning rates, acceleration or stopping rates, speed, spacing, etc. can be pre-defined and used to determine if a proposed trajectory for host vehicle 105 may violate a pre-defined goal. If the score for the first proposed trajectory, as generated by the trajectory processing module 173 based on the predicted trajectories for any of the proximate agents, may violate a pre-defined goal, the trajectory processing module 173 can reject the first proposed trajectory and the trajectory processing module 173 can generate a second proposed trajectory… Minimum score thresholds for each goal can be pre-defined. For example, score thresholds related to turning rates, acceleration or stopping rates, speed, spacing, etc. can be pre-defined and used to determine if a proposed trajectory for host vehicle 105 may violate a pre-defined goal. If the score for the first proposed trajectory, as generated by the trajectory processing module 173 based on the predicted trajectories for any of the proximate agents, may violate a pre-defined goal, the trajectory processing module 173 can reject the first proposed trajectory and the trajectory processing module 173 can generate a second proposed trajectory. The second proposed trajectory and the current context of the host vehicle 105 can be provided to the trajectory prediction module 175 for the generation of a new set of predicted trajectories and confidence levels for each proximate agent as related to the second proposed trajectory and the context of the host vehicle 105. The new set of predicted trajectories and confidence levels for each proximate agent as generated by the trajectory prediction module 175 can be output from the trajectory prediction module 175 and provided to the trajectory processing module 173. Again, the trajectory processing module 173 can use the predicted trajectories and confidence levels for each proximate agent corresponding to the second proposed trajectory to determine if any of the predicted trajectories for the proximate agents may cause the vehicle 105 to violate a pre-defined goal based on a related score being below a minimum acceptable threshold. If the score for the second proposed trajectory, as generated by the trajectory processing module 173 based on the new set of predicted trajectories for any of the proximate agents, may violate a pre-defined goal, the trajectory processing module 173 can reject the second proposed trajectory and the trajectory processing module 173 can generate a third proposed trajectory. This process can be repeated until a proposed trajectory generated by the trajectory processing module 173 and processed by the trajectory prediction module 175 results in predicted trajectories and confidence levels for each proximate agent that cause the proposed trajectory for the host vehicle 105 to satisfy the pre-defined goals based on a related score being at or above a minimum acceptable threshold. Alternatively, the process can be repeated until a time period or iteration count is exceeded. If the process of an example embodiment as described above results in predicted trajectories, confidence levels, and related scores that satisfy the pre-defined goals, the corresponding proposed trajectory 220 is provided as an output from the prediction-based trajectory planning module 200 as shown in FIG. 6” (Zhang: [0058]), for the benefit of creating alternative trajectories with the safest score possible.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a vehicle disclosed by Maurer in view of Shalev-Shwartz to include safety and comfort scores taught by Zhang. One of ordinary skill in the art would have been motivated to make this modification in order to crate alternative trajectories with the safest score possible.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ostafew (US 20210031760 A1) in view of Maurer (US 20150210311 A1) and Gochev (US 20190161080 A1) as applied to claim 21 above, and further in view of Cronin (US 20180203455 A1).
REGARDING CLAIM 22, Ostafew, as modified, remain as applied above to claim 21, and further, Ostafew, as modified, also discloses, the first set of costs comprises a first safety cost (Gochev: [0019] increasing vehicle, passenger, and object safety; [0040] the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a first comfort cost (Gochev: [0040] the vehicle computing system can proactively control the motion of the autonomous vehicle on a more granular level to avoid sudden movements that place stress on the vehicle's systems and confuse or frighten users (e.g., passengers of the vehicle). Moreover, the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a first progress cost (Gochev: [0025] which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan; [0038] the motion planning system can generate cost data indicative of an effect of performing the respective vehicle action for each time step. The cost data can include a cost function indicative of a cost (e.g., over time) of controlling the motion of the autonomous vehicle (e.g., the trajectory, speed, or other controllable parameters of the autonomous vehicle) to perform the respective vehicle action (e.g., pass, queue behind, ignore, etc.)), and a first operational rules cost (Gochev: [0048] the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle); the second set of costs comprises a second safety cost (Gochev: [0019] increasing vehicle, passenger, and object safety; [0040] the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a second comfort cost (Gochev: [0040] the vehicle computing system can proactively control the motion of the autonomous vehicle on a more granular level to avoid sudden movements that place stress on the vehicle's systems and confuse or frighten users (e.g., passengers of the vehicle). Moreover, the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a second progress cost (Gochev: [0025] which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan; [0038] the motion planning system can generate cost data indicative of an effect of performing the respective vehicle action for each time step. The cost data can include a cost function indicative of a cost (e.g., over time) of controlling the motion of the autonomous vehicle (e.g., the trajectory, speed, or other controllable parameters of the autonomous vehicle) to perform the respective vehicle action (e.g., pass, queue behind, ignore, etc.)), and a second operational rules cost (Gochev: [0025] considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan); the third set of costs comprises a third safety cost (Gochev: [0019] increasing vehicle, passenger, and object safety; [0040] the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a third comfort cost (Gochev: [0040] the vehicle computing system can proactively control the motion of the autonomous vehicle on a more granular level to avoid sudden movements that place stress on the vehicle's systems and confuse or frighten users (e.g., passengers of the vehicle). Moreover, the autonomous vehicle can operate in a manner that is safer for the objects in the vehicle's surroundings and for the vehicle itself), a third progress cost (Gochev: [0025] which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan; [0038] the motion planning system can generate cost data indicative of an effect of performing the respective vehicle action for each time step. The cost data can include a cost function indicative of a cost (e.g., over time) of controlling the motion of the autonomous vehicle (e.g., the trajectory, speed, or other controllable parameters of the autonomous vehicle) to perform the respective vehicle action (e.g., pass, queue behind, ignore, etc.)), and a third operational rules cost (Gochev: [0025] considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan; [0048] the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle); and comparing the sets of costs comprises comparing the first progress cost to the second progress cost (Gochev: [0025] The motion planning system can determine a motion plan for the autonomous vehicle based at least in part on the predicted data (and/or other data). The motion plan can include vehicle actions with respect to the objects within the surrounding environment of the autonomous vehicle as well as the predicted movements. For instance, the motion planning system can implement an optimization planner that includes an optimization algorithm, which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. By way of example, the motion planning system can determine that the vehicle can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.). A motion plan can include a planned motion trajectory of the autonomous vehicle. The planned motion trajectory can be indicative of a trajectory that the autonomous vehicle is to follow for a particular time period. The motion plan can also indicate speed(s), acceleration(s), and/or other operating parameters/actions of the autonomous vehicle) based at least in part on comparing a delay associated with stopping to a delay associated with navigating around the object (Gochev: [0019] A vehicle action can be indicative of whether the autonomous vehicle should, for example, stay ahead of the object (e.g., pass, maintain lead distance, etc.), stay behind the object (e.g., queue, stop, etc.), and/or ignore that object during that time step; [0025] which considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. By way of example, the motion planning system can determine that the vehicle can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.); [0038] the motion planning system can generate cost data indicative of an effect of performing the respective vehicle action for each time step. The cost data can include a cost function indicative of a cost (e.g., over time) of controlling the motion of the autonomous vehicle (e.g., the trajectory, speed, or other controllable parameters of the autonomous vehicle) to perform the respective vehicle action (e.g., pass, queue behind, ignore, etc.)).
Gochev does not explicitly recite the terminology “progress cost”. However, Gochev discloses cost based upon speed limits, traffic lights, and other traffic controls [0025], and cost over time associated trajectory, speed, and other controls associated with the vehicle [0038]. Which, is a “progress” or “time” cost.
Further, Gochev does not explicitly recite the terminology “first”, “second”, or “third”. However, Gochev discloses trajectory, speed, traffic lights, and more (see above). Further still, Gochev discloses comparing, which implies a plurality. Regarding “third”, see speed, traffic lights, trajectory, etc., above. Further regarding “third”, tertiary duplicative cost is considered a duplication of essential steps/parts that is well within the scope of customary practices for one of ordinary skill (also see mpep §2144: routine customization/optimization- “Further, in considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom (mpep 2144.01). Where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation … a change in form, proportions, or degree “will not sustain a patent” … It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions (mpep 2144.05.II.A).”) and is typically not regarded as an inventive concept.
The examiner respectfully submits, Gochev discloses first, second, and third “progress”. However, in the alternative, and in the same field of endeavor, Cronin discloses, “when a difference between the first cost including a travel time or a travel distance expected when the autonomous vehicle travels along the first path and the second cost including a travel time or a travel distance expected when the autonomous vehicle travels along the second path is equal to or less than a threshold value; [0115]; [0130-0144] cost may include a travel time, a travel distance, and fuel consumption”, for the benefit of determining a cost incurred when the autonomous vehicle travels along the detour path.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by a modified Ostafew to include navigational costs taught by Cronin. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to determining a cost incurred when the autonomous vehicle travels along the detour path.
Response to Arguments
Applicant’s arguments, beginning on page 11, received 05-26-2026, with respect to the rejections under 35 USC §112(a) and (b) have been fully considered and are persuasive. The rejections under 35 USC §112(a) and (b) has been withdrawn.
Applicant’s arguments with respect to the rejection of the independent claims under 35 USC §103, obviousness, have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McCawley (US 20200241541 A1)
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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/A.S./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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