DETAILED ACTION
This is a non-final rejection in response to amendments filed 12/05/2025. Claims 1-12 and 14-20 are pending.
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 Arguments
Applicant's arguments filed 6/20/2025 have been fully considered but they are not persuasive. Applicant argues that Jiang does not teach the sampling-based approach or the constraints-based planning system. However, no evidence is given that the prior art does not teach these features. As discussed below, while not explicitly using these terms, Jiang teaches these features.
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.
Claims 1-12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US 20220081000) in view of Yu (US 20220297718).
Regarding claim 1, Jiang teaches a system for use in a vehicle ([0021] “a system/method generates a driving trajectory for an autonomous driving vehicle (ADV)”), wherein the system comprises:
at least one processor; and at least one non-transitory computer-related media comprising instructions that, upon execution of the instructions by the at least one processor ([0071] “such components can be implemented as software installed and stored in a persistent storage device, which can be loaded and executed in a memory by a processor (not shown) to carry out the processes or operations described throughout this application”), are to cause the at least one processor to:
identify, using a sampling-based path planning system, on a graph that includes a plurality of edges and a plurality of nodes, a reference path through an environment that includes a subset of the plurality of edges ([0042] “Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location” with [0049]-[0050] discussing the reference line at different coordinates in the SL graph where each coordinate is interpreted as a node the lines connecting between each coordinate are edges and where [0042] discusses the reference line being based on all possible routes from the starting location to the destination location (sampling));
identify, ([0066] “processing logic generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory” where [0057] discusses the neural network model being used to optimize the trajectory using various systems including surrounding environment, map information including the predetermined route data (reference path), and traffic rules)
identify, using a constraints computation system, a constrained second path through the environment ([0066] “At block 1206, otherwise, processing logic controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function and the objective function is determined based on at least the one or more bounding conditions”) the constraints computation system configured to calculate constraints that are applied to the reference path (bound condition determiner 402), wherein the constraints computation system and spatial trajectory optimization system are separate path planning systems ([0047] discusses an Fig. 4 shows the bound condition determiner and the model-based trajectory generator as separate systems); and
select, based on the pre-identified rulebook, the first path or the second path as a path along which the vehicle will traverse ([0066] discusses selecting the second trajectory when first trajectory does satisfy bounding conditions with [0021] discussing the bounding conditions are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook);
and control the vehicle based on the selected first or second path ([0021] discusses controlling the autonomous vehicle according to the selected path).
Jiang teaches selecting between trajectories based on bounding conditions, but does not explicitly teach a graph with nodes and edges and the first path being spatially optimized related to the graph or where the spatial model is configured to optimize vehicle states over a prediction horizon.
Yu teaches identify, using a spatial trajectory optimization system, and based on optimization of a spatial model related to the graph and the reference path, a first path through the environment where the spatial model is configured to optimize vehicle states over a prediction horizon ([0057] discusses using the A* searching algorithm to optimize the route of the autonomous vehicle where the A* searching algorithm is interpreted as a spatial trajectory optimization model related to the graph and reference path and where it is interpreted that the algorithm optimizes states over a prediction horizon as it optimizes the route to the destination).
Jiang teaches selecting a trajectory for an autonomous vehicle. Yu teaches using spatial trajectory optimization in trajectory optimization. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Yu with the spatial optimization of Yu as Yu teaches that this provides optimal efficiency in computation [0055].
Regarding claim 2, Jiang teaches wherein the instructions further cause the at least one processor to:
provide an indication of the first path to a path planning system ([0066] discusses the process of generating and selecting trajectories as described above may be performed by hybrid planning module 308 where [0047] discusses the hybrid planning module 308 being a part of planning module 305 which [0043]-[0044] described the planning module as generating the planning and control data to drive the ADV where it is interpreted that the hybrid planning module would have to provide the planning module with the selected trajectory); and
identify, by the path planning system, a subsequent reference path based on at least a portion of the first path ([0045] “planning module 305 plans a next route segment or path segment… planning module 305 plans a target position for the current cycle (e.g., next 5 seconds) based on a target position planned in a previous cycle”).
Regarding claim 3, Jiang teaches wherein the instructions further cause the at least one processor to identify the first path based on velocity constraints, lane constraints, or obstacles in the environment ([0068] “processing logic further smooths the first or the second trajectory based on a smoothing function, wherein the smoothing function is determined based on the one or more bounding conditions. In one embodiment, the one or more bounding conditions includes a lane bound, an obstacle bound, or a traffic light bound” and [0054] further discusses the bounding conditions as including speed decisions).
Regarding claim 4, Jiang teaches wherein the instructions further cause the at least one processor to identify the second path based on velocity constraints, lane constraints, or obstacles in the environment ([0068] “processing logic further smooths the first or the second trajectory based on a smoothing function, wherein the smoothing function is determined based on the one or more bounding conditions. In one embodiment, the one or more bounding conditions includes a lane bound, an obstacle bound, or a traffic light bound”).
Regarding claim 5, Jiang teaches wherein the pre-identified rulebook includes rules related to collision avoidance or traffic laws ([0021] discusses the bounding conditions (pre-identified rulebook) are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook).
Regarding claim 6, Jiang teaches wherein the instructions further cause the at least one processor to identify a trajectory based on the selected first or second path ([0043] states “Planning and control data is generated by planning module 305 including information describing how vehicle 300 would move in a next moving cycle (e.g., next route/path segment)” giving an example including the speed at which the vehicle is moved along the path where a path segment and speed are interpreted as a trajectory where, as described above, the hybrid planning module selects and provides the trajectory to the planning module).
Regarding claim 7, Jiang teaches wherein the instructions further cause the at least one processor to output at least one indication of a control to be used by a control system of the vehicle ([0044] “Based on the planning and control data, control module 306 controls and drives the ADV, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data”).
Regarding claim 8, Jiang teaches a method ([0021] “a system/method generates a driving trajectory for an autonomous driving vehicle (ADV)”) comprising:
identifying, using a sampling-based path planning system, a vehicle based on a graph that includes a plurality of edges and a plurality of nodes, a reference path through an environment that includes a subset of the plurality of edges ([0042] “Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location” with [0049]-[0050] discussing the reference line at different coordinates in the SL graph where each coordinate is interpreted as a node the lines connecting between each coordinate are edges and where [0042] discusses the reference line being based on all possible routes from the starting location to the destination location (sampling));
identifying, ([0066] “processing logic generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory” where [0057] discusses the neural network model being used to optimize the trajectory using various systems including surrounding environment, map information including the predetermined route data (reference path), and traffic rules),
identifying, using a constraints computation system, a constrained second path through the environment based on application of at least one constraint to the reference path ([0066] “At block 1206, otherwise, processing logic controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function and the objective function is determined based on at least the one or more bounding conditions”), the constraints computation system configured to calculate constraints that are applied to the reference path (bound condition determiner 402), wherein the constraints computation system and spatial trajectory optimization system are separate path planning systems ([0047] discusses an Fig. 4 shows the bound condition determiner and the model-based trajectory generator as separate systems); and
selecting, by the at least one processor, the first path or the second path as a path along which the vehicle will traverse based on the pre-identified rulebook [0066] discusses selecting the second trajectory when first trajectory does satisfy bounding conditions with [0021] discussing the bounding conditions are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook)
and controlling, by the at least one processor, the vehicle based on the selected first or second path ([0021] discusses controlling the autonomous vehicle according to the selected path).
Jiang teaches selecting between trajectories based on bounding conditions, but does not explicitly teach a graph with nodes and edges and the first path being spatially optimized related to the graph or where the spatial model is configured to optimize vehicle states over a prediction horizon.
Yu teaches identifying, using a spatial trajectory optimization system, and based on optimization of a spatial model related to the graph and the reference path, a first path through the environment where the spatial model is configured to optimize vehicle states over a prediction horizon ([0057] discusses using the A* searching algorithm to optimize the route of the autonomous vehicle where the A* searching algorithm is interpreted as a spatial trajectory optimization model related to the graph and reference path and where it is interpreted that the algorithm optimizes states over a prediction horizon as it optimizes the route to the destination).
Jiang teaches selecting a trajectory for an autonomous vehicle. Yu teaches using spatial trajectory optimization in trajectory optimization. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Yu with the spatial optimization of Yu as Yu teaches that this provides optimal efficiency in computation [0055].
Regarding claim 9, Jiang teaches identifying, by the at least one processor, a subsequent reference path based on the first path [0045] “planning module 305 plans a next route segment or path segment… planning module 305 plans a target position for the current cycle (e.g., next 5 seconds) based on a target position planned in a previous cycle”.
Regarding claim 10, Jiang teaches identifying, by the at least one processor, the first path or the second path based on velocity constraints, lane constraints, or obstacles in the environment ([0068] “processing logic further smooths the first or the second trajectory based on a smoothing function, wherein the smoothing function is determined based on the one or more bounding conditions. In one embodiment, the one or more bounding conditions includes a lane bound, an obstacle bound, or a traffic light bound”).
Regarding claim 11, Jiang teaches wherein the pre-identified rulebook includes rules related to collision avoidance or traffic laws ([0021] discusses the bounding conditions (pre-identified rulebook) are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook).
Regarding claim 12, Jiang teaches identifying, by the at least one processor, a trajectory that includes a velocity, based on the selected first or second path ([0043] states “Planning and control data is generated by planning module 305 including information describing how vehicle 300 would move in a next moving cycle (e.g., next route/path segment)” giving an example including the speed at which the vehicle is moved along the path where, as described above, the hybrid planning module selects and provides the trajectory to the planning module).
Regarding claim 13, Jiang teaches controlling, by the at least one processor, the vehicle based on the trajectory ([0044] “Based on the planning and control data, control module 306 controls and drives the ADV, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data”).
Regarding claim 14, Jiang teaches one or more non-transitory computer-readable media comprising instructions that, upon execution of the instructions by at least one processor of a vehicle ([0075] “The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both”), are to cause the vehicle to:
identify, using a sampling-based path planning system, a graph that includes a plurality of edges and a plurality of nodes, and a reference path through an environment that includes a subset of the plurality of edges ([0042] “Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location” with [0049]-[0050] discussing the reference line at different coordinates in the SL graph where each coordinate is interpreted as a node the lines connecting between each coordinate are edges and where [0042] discusses the reference line being based on all possible routes from the starting location to the destination location (sampling));
identify,([0066] “processing logic generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory” where [0057] discusses the neural network model being used to optimize the trajectory using various systems including surrounding environment, map information including the predetermined route data (reference path), and traffic rules);
identify, using a constraints computation system, a constrained second path through the environment the constraints computation system configured to calculate constraints that are applied to the reference path ([0047] discusses an Fig. 4 shows the bound condition determiner and the model-based trajectory generator as separate systems), wherein the constraints computation system and spatial trajectory optimization system are separate path planning systems ([0066] “At block 1206, otherwise, processing logic controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function and the objective function is determined based on at least the one or more bounding conditions”); and
select, based on a pre-identified rulebook, the first path or second path as a path along which an autonomous vehicle will traverse ([0066] discusses selecting the second trajectory when first trajectory does satisfy bounding conditions with [0021] discussing the bounding conditions are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook);
and control the vehicle based on the selected first or second path ([0021] discusses controlling the autonomous vehicle according to the selected path).
Jiang teaches selecting between trajectories based on bounding conditions, but does not explicitly teach a graph with nodes and edges and the first path being spatially optimized related to the graph or where the spatial model is configured to optimize vehicle states over a prediction horizon.
Yu teaches identify, using a spatial trajectory optimization system, and based on optimization of a spatial model related to the graph and the reference path, a first path through the environment where the spatial model is configured to optimize vehicle states over a prediction horizon ([0057] discusses using the A* searching algorithm to optimize the route of the autonomous vehicle where the A* searching algorithm is interpreted as a spatial trajectory optimization model related to the graph and reference path and where it is interpreted that the algorithm optimizes states over a prediction horizon as it optimizes the route to the destination).
Jiang teaches selecting a trajectory for an autonomous vehicle. Yu teaches using spatial trajectory optimization in trajectory optimization. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Yu with the spatial optimization of Yu as Yu teaches that this provides optimal efficiency in computation [0055].
Regarding claim 15, Jiang teaches wherein the instructions are further to identify a subsequent reference path based on the first path ([0045] “planning module 305 plans a next route segment or path segment… planning module 305 plans a target position for the current cycle (e.g., next 5 seconds) based on a target position planned in a previous cycle”).
Regarding claim 16, Jiang teaches wherein the instructions are further to identify the first path based on velocity constraints, lane constraints, or obstacles in the environment ([0068] “processing logic further smooths the first or the second trajectory based on a smoothing function, wherein the smoothing function is determined based on the one or more bounding conditions. In one embodiment, the one or more bounding conditions includes a lane bound, an obstacle bound, or a traffic light bound”).
Regarding claim 17, Jiang teaches wherein the instructions are further to identify the second path based on velocity constraints, lane constraints, or obstacles in the environment ([0068] “processing logic further smooths the first or the second trajectory based on a smoothing function, wherein the smoothing function is determined based on the one or more bounding conditions. In one embodiment, the one or more bounding conditions includes a lane bound, an obstacle bound, or a traffic light bound”).
Regarding claim 18, Jiang teaches wherein the pre-identified rulebook includes rules related to collision avoidance or traffic laws ([0021] discusses the bounding conditions (pre-identified rulebook) are based on traffic rules and/or map information and with [0048] giving examples including obstacle, traffic light, yield/overtake and road/lane bounds where the bounding conditions are interpreted as a pre-identified rulebook).
Regarding claim 19, Jiang teaches wherein the instructions are further to identify a trajectory that includes a velocity, based on the selected first or second path ([0043] states “Planning and control data is generated by planning module 305 including information describing how vehicle 300 would move in a next moving cycle (e.g., next route/path segment)” giving an example including the speed at which the vehicle is moved along the path where, as described above, the hybrid planning module selects and provides the trajectory to the planning module).
Regarding claim 20, Jiang teaches wherein the instructions are further to cause the vehicle to traverse the selected first or second path in accordance with the identified trajectory ([0043] states “Planning and control data is generated by planning module 305 including information describing how vehicle 300 would move in a next moving cycle (e.g., next route/path segment)” giving an example of including the speed at which the vehicle is moved along the path where a path segment and speed are interpreted as a trajectory).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE M JACKSON whose telephone number is (303)297-4364. The examiner can normally be reached Monday-Friday 7:00-4:30 MT.
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/D.M.J./Examiner, Art Unit 3657 /ABBY LIN/ Supervisory Patent Examiner, Art Unit 3657