DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-13 filed on 02/24/2025 have been examined.
This Office Action is in response to the Applicant’s amendments and remarks filed on - 07/08/2026. Claims 1-3, 5-7, and 10-13 have been amended. Claims 4 and 8-9 have been canceled. Claims 1-3, 5-7, and 10-13 are currently pending and addressed below.
Response to Remarks/Arguments
Applicant’s accompanying amendments and arguments, on page 8 of the Applicant Arguments/Remarks (hereinafter referred to as the “Remarks”), filed 07/08/2026, with respect to Claim Interpretation under 35 U.S.C. 112(f) stating “… The claims have been amended so as to eliminate the language that invokes such an interpretation where appropriate. Accordingly, Applicant requests that the assertion that any elements of the present claims are subject to interpretation under 35 U.S.C. § 112(f) be withdrawn…” have been considered and are persuasive. Therefore, the Examiner has withdrawn Claim Interpretation under 35 U.S.C. 112(f).
Applicant’s accompanying amendments and arguments, on pages 8-20 of the Applicant Remarks, filed 07/08/2026, with respect to the rejection of claims 1 and 12-13, and the corresponding dependent claims under 35 U.S.C. 101 stating “… Applicant notes that claim 1 as amended recites controlling a drive device of the moving object to cause the moving object to travel along the target trajectory. In this respect, claim 1 as amended specifies generating a target trajectory by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points. Applicant asserts that, as a practical matter, generating a target trajectory and controlling a drive device in the manner claimed cannot be performed in the human mind where the human mind is not equipped to perform such claim elements… Accordingly, Applicant requests that the rejection of claim 1 as amended under § 101 be withdrawn. Also, Applicant requests that the rejection of claims 12 and 13 as amended under § 101 be withdrawn for reasons similar in scope to those set forth above with respect to claim 1 as amended…” have been considered and are persuasive. Therefore, the Examiner has withdrawn the rejection of the claims under 35 U.S.C. 101.
Applicant’s accompanying amendments and arguments, on pages 20-22 of the Applicant Remarks, filed 07/08/2026, with respect to the rejection of claims 1 and 12-13, and the corresponding dependent claims under 35 U.S.C. 102 stating “… Independent claim 1 has been amended to recited in part as follows: generate, as the target trajectory, a trajectory from a position of the moving object to a target position, the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points… Huang states that path 456 may be a curve such as a Bezier curve (see paragraphs [0037], [0071 ]-[0076], [0106]- [0107]). However, Applicant asserts that a Bezier curve or a smoothed node-based path fails to teach or suggest a trajectory configured by combining a plurality of clothoid curves whose curvatures change linearly with respect to distance with three curvature change points at which the curvature changes non-linearly. Applicant further notes that Kondekar (US Pub. 2012/0101725 A1, hereinafter "Kondekar") and Timmons et al. (US Pub. 2011/0190972 A1, hereinafter "Timmons") fail to teach or suggest at least the elements of claim 1 as amended… Applicant asserts that Huang fails to teach or suggest the elements of claim 1 as amended. Accordingly, Applicant requests that the rejection of claim 1 be withdrawn. Also, Applicant requests that the rejection of claims 11-13 as amended be withdrawn at least for reasons similar in scope to those set forth above with respect to claim 1 as amended…” have been considered but are moot due to the amendments and added limitations provided above. Upon further consideration, a new ground(s) of rejection is made in view of Moshchuk et al. US 20130054128 A1 (“Moshchuk”).
Claim Objections
Claims 1 and 11-13 are objected to because of the following informalities:
Claims 1 and 11-13 currently recite “… the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points…”. Appropriate correction is required.
Applicant is advised to amend claims 1 and 11-13 to specifically recite:
“… the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and [[a]] curvature change points at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points…”
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-3, 5-7, and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. US 20230051486 A1 (“Huang”) in view of Moshchuk et al. US 20130054128 A1 (“Moshchuk”).
For claim 1, Huang discloses a moving object control system that controls an operation of a moving object (See at least the Abstract – “… an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps…” and [0034] of Huang – “… The trajectory 118 may comprise instructions for controller(s) 116 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate, which may result in a vehicle position, vehicle velocity, and/or vehicle acceleration that tracks the path generated by the guidance system…”), the moving object control system comprising:
one or more processors (See at least [0053] of Huang – “… The vehicle computing device(s) 204 may include processor(s) 218 …”); and
a memory storing instructions which, when the instructions are executed by the one or more processors (See at least [0053]-[0054] of Huang – “… The vehicle computing device(s) 204 may include processor(s) 218 and memory 220 communicatively coupled with the one or more processors 218… may be any suitable processor capable of executing instructions to process data and perform operations as described herein… The memory 220 … may store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems…”), cause the moving object control system to:
obtain information of a sensor configured to recognize a periphery of the moving object (See at least [0046]-[0047] of Huang – “… vehicle 202 may include a vehicle computing device(s) 204, sensor(s) 206, emitter(s) 208… Vehicle computing device(s) 204 may represent computing device(s) 106 and sensor(s) 206 may represent sensor(s) 104… sensor(s) 206 may represent sensor(s) 104 and may include lidar sensors… image sensors … time of flight cameras… environment sensors … The sensor(s) 206 may provide input to the vehicle computing device(s)…”);
generate a dynamic prediction map (See at least [0071] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process 400 for determining a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity map… a computing device of an autonomous vehicle … may accomplish all the operations …”) including information indicating a position of a static obstacle recognized based on the information of the sensor (See at least [0037] of Huang – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data … the static data may indicate a likelihood that an object exists at a location in the environment …”) and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor (See at least [0037] – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data… the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment. In some instances, the dynamic data may comprise multiple frames associated with different times steps at intervals up to a prediction horizon (i.e., a maximum time/distance for which dynamic data is predicted…” and [0066]-[0070] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle… wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”); and
generate a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map (See at least [0037] – “… the guidance system 114 discussed herein may generate a path 132 based at least in part on environment data 134 generated from sensor data captured by sensor(s) 104. For example, the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data. For example, the static data may indicate a likelihood that an object exists at a location in the environment and the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment…”, [0042] – “… The planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route…”, [0106] – “… operation 452 may comprise determining a path associated with the lowest cost node… determining the path 456 …” and Fig. 4C of Huang – path 456 determined in consideration of static and dynamic objects shown in map);
generate, as the target trajectory, a trajectory from a position of the moving object to a target position (See at least [0014] of Huang – “… determining the route between the start (current) position and an end position associated with the end location based at least in part on a map stored by the autonomous vehicle…”); and
control a drive device of the moving object to cause the moving object to travel along the target trajectory (See at least [0042] – “… the planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route… to control the vehicle to traverse the route (e.g., in order to avoid any of the detected objects… generate a drive control signal that may be transmitted to the controller(s) 116 for actuating drive components of the vehicle 102…” and [0052] of Huang – “… The drive component(s) 212 may include … a motor to propel the vehicle…”).
Huang fails to specifically disclose the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points.
However, Moshchuk, in the same field of endeavor teaches the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points (See at least [0047]-[0048] – “… optimal path curvature 310 may be defined by a piece-wise linear function of trajectory curvature 312 and time 314, trajectory curvature 312 and longitudinal distance 316… Optimal path curvature 310 may, for example, include multiple path curvature segments, trajectory curvature parts, or curvature segments (e.g., path curvature segments 330, 332, 334, 336, 338, and/or 340 or other curve segments) connected by waypoints or breakpoints (e.g., waypoints 350, 352, 354, 356, and/or 358 or other waypoints). Waypoints may, for example, be the points or transitions between curve segments, points at which the slope or characteristics of optimal path curvature 310 change… during first path curvature segment 330 and second path curvature segment 332, host vehicle 10 may increase to maximum curvature or another curvature to steer away from the object 202. During third path curvature segment 334, host vehicle 10 may, for example, have avoided object 202 and may be reducing curvature to maximum negative curvature or other curvature to correct and/or compensate path curvature after avoiding object 202. During fourth path curvature segment 336, path curvature may, for example, remain constant and vehicle 10 may counter steer to correct or align vehicle position in target lane 230…” and Fig. 4 of Moshchuk – optimal path around an object is determined by combining trajectories in which a curvature of each of the trajectories changes linearly with respect to a longitudinal distance and curvature changes point at which the curvature changes non-linearly. Examiner notes that the flat curvature segments in Fig. 4 correspond to non-linear curvature changes, while the sloped segments in Fig. 4 correspond to linear curvature changes, and the waypoints are the curvature change points that combine the plurality of trajectories). Thus, Huang discloses a system for an autonomous vehicle that obtains environment data using images to identify static and dynamic obstacles and generate a map that predicts positions of the identified obstacles in a future time in order to determine a path of travel for the autonomous vehicle, while Moshchuk teaches a collision avoidance system for a vehicle that generates a path to avoid an object by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and curvature change points at which the curvature changes non-linearly at a plurality of curvature change points.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the moving object control system, moving object, control method, and non-transitory computer readable storage medium as disclosed in Huang to include the feature of the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points as taught by Moshchuk, with a reasonable expectation of success, in order to correct and compensate path curvature after avoiding an object as specified in at least [0048] of Moshchuk.
For claim 2, Huang discloses wherein the dynamic prediction map includes information on predicted time-series positions of the dynamic obstacle as the information indicating the position of the dynamic obstacle that changes with time (See at least [0066] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle. The perception component may comprise one or more ML models that receive sensor data and predict motion of an object. For example, the one or more ML models may output a series of frames, wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”).
For claim 3, Huang discloses wherein the one or more processors generate the dynamic prediction map by using a first map indicating the position of the static obstacle recognized based on the information of the sensor, a second map indicating the position of the dynamic obstacle recognized based on the information of the sensor, and information on predicted time-series positions of the dynamic obstacle (See at least [0076] – “… At operation 410, example process 400 may comprise determining a cost map based at least in part on the environment data… operation 410 may include concatenating or otherwise combining the static data 406 and the dynamic data 408 and/or generating cost map(s) 412 for one or more time steps and/or vehicle modes. The interval of time between the cost map(s) may be the same or different than the interval between the dynamic data 408 time steps and/or the time steps between the layers of nodes generated at operation 414…” and Fig. 4A of Huang – cost map 412).
For claim 5, Huang discloses wherein the one or more processors generate a first trajectory of the moving object as a reference route, and generate a second trajectory configured by combining a plurality of trajectories as the target trajectory based on the first trajectory (See at least [0035] – “… the autonomous vehicle 102 has … determined a route 120 defining a start position 122, an end position 124, and a curve between the start position 122 and the end position 124 (note that the curve comprises a straight line and/or one or more curves…”, [0042] – “… The planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories…”, [0079] – “… The cost may additionally or alternatively be based at least in part on a deviation cost that is based at least in part on a deviation of the position, heading, velocity, and/or curvature specified by the position in the multivariate space from the route … and/or a displacement along the route…”, Fig. 4C – the plurality of segments corresponding to the path between each node of the set of nodes that determine path 456, and [0105]-[0107] of Huang – “… determining a path may comprise iteratively determining different combinations of connections between nodes of different node layers and selecting connections to add to a directed graph based at least in part on reducing the running total cost of the combination of connections… path 456 may be a curve, such as a Bezier curves, as illustrated in FIG. 4C, although a path output by the guidance system may comprise a coarse path, which may include a list of the coordinates of the nodes connected by the path … process 400 may further comprise transmitting the coarse path to another component, such as a smoothing component of the planning component, that may receive the coarse path and smooth the coarse path into a smooth path that has curvatures and accelerations that are drivable by the particular type of vehicle being controlled...”).
For claim 6, Huang discloses wherein the one or more processors generate the first trajectory of the moving object based on the position of the static obstacle indicated on the first map (See at least [0008] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process for generating a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity probability map...”).
For claim 7, Huang discloses wherein the one or more processors generate the second trajectory based on the first trajectory and the position of the dynamic obstacle that changes with time and is included in the dynamic prediction map (See at least [0071]-[0107] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process 400 for determining a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity map... determining a path may comprise iteratively determining different combinations of connections between nodes of different node layers and selecting connections to add to a directed graph based at least in part on reducing the running total cost of the combination of connections... path 456 may be a curve, such as a Bezier curves, as illustrated in FIG. 4C…”).
For claim 10, Huang discloses wherein the information of the sensor is image information captured by an imaging unit of the moving object (See at least [0031] – “… the vehicle 102 may receive sensor data from sensor(s) 104 of the vehicle 102. For example, the sensor(s) 104 may include … a lidar sensor… a time of flight (ToF) camera, a depth camera … an image sensor … a camera…” and [0041] of Huang – “… The object classifications, which may be part of the environment data 134 and determined by the perception component 110… The data produced by the perception component 110 may be collectively referred to as perception data… perception data may comprise outputs of sensor specific pipelines (e.g., vision, lidar…”),
the one or more processors further cause the moving object control system to predict, based on the image information, the position of the dynamic obstacle that changes with time (See at least [0037] – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data… the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment. In some instances, the dynamic data may comprise multiple frames associated with different times steps at intervals up to a prediction horizon (i.e., a maximum time/distance for which dynamic data is predicted…” and [0066]-[0070] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle… wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”).
For claim 11, Huang discloses a moving object (See at least the Abstract – “… an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps…” and [0034] of Huang – “… The trajectory 118 may comprise instructions for controller(s) 116 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate, which may result in a vehicle position, vehicle velocity, and/or vehicle acceleration that tracks the path generated by the guidance system…”) comprising:
one or more processors (See at least [0053] of Huang – “… The vehicle computing device(s) 204 may include processor(s) 218 …”); and
a memory storing instructions which, when the instructions are executed by the one or more processors (See at least [0053]-[0054] of Huang – “… The vehicle computing device(s) 204 may include processor(s) 218 and memory 220 communicatively coupled with the one or more processors 218… may be any suitable processor capable of executing instructions to process data and perform operations as described herein… The memory 220 … may store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems…”), cause the moving object to:
obtain information of a sensor configured to recognize a periphery of the moving object (See at least [0046]-[0047] of Huang – “… vehicle 202 may include a vehicle computing device(s) 204, sensor(s) 206, emitter(s) 208… Vehicle computing device(s) 204 may represent computing device(s) 106 and sensor(s) 206 may represent sensor(s) 104… sensor(s) 206 may represent sensor(s) 104 and may include lidar sensors… image sensors … time of flight cameras… environment sensors … The sensor(s) 206 may provide input to the vehicle computing device(s)…”);
generate a dynamic prediction map (See at least [0071] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process 400 for determining a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity map… a computing device of an autonomous vehicle … may accomplish all the operations …”) including information indicating a position of a static obstacle recognized based on the information of the sensor (See at least [0037] of Huang – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data … the static data may indicate a likelihood that an object exists at a location in the environment …”) and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor (See at least [0037] – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data… the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment. In some instances, the dynamic data may comprise multiple frames associated with different times steps at intervals up to a prediction horizon (i.e., a maximum time/distance for which dynamic data is predicted…” and [0066]-[0070] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle… wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”);
generate a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map (See at least [0037] – “… the guidance system 114 discussed herein may generate a path 132 based at least in part on environment data 134 generated from sensor data captured by sensor(s) 104. For example, the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data. For example, the static data may indicate a likelihood that an object exists at a location in the environment and the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment…”, [0042] – “… The planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route…”, [0106] – “… operation 452 may comprise determining a path associated with the lowest cost node… determining the path 456 …” and Fig. 4C of Huang – path 456 determined in consideration of static and dynamic objects shown in map);
generate, as the target trajectory, a trajectory from a position of the moving object to a target position (See at least [0014] of Huang – “… determining the route between the start (current) position and an end position associated with the end location based at least in part on a map stored by the autonomous vehicle…”); and
control a drive device of the moving object to cause the moving object to travel along the target trajectory (See at least [0042] – “… the planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route… to control the vehicle to traverse the route (e.g., in order to avoid any of the detected objects… generate a drive control signal that may be transmitted to the controller(s) 116 for actuating drive components of the vehicle 102…” and [0052] of Huang – “… The drive component(s) 212 may include … a motor to propel the vehicle…”).
Huang fails to specifically disclose the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points.
However, Moshchuk, in the same field of endeavor teaches the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points (See at least [0047]-[0048] – “… optimal path curvature 310 may be defined by a piece-wise linear function of trajectory curvature 312 and time 314, trajectory curvature 312 and longitudinal distance 316… Optimal path curvature 310 may, for example, include multiple path curvature segments, trajectory curvature parts, or curvature segments (e.g., path curvature segments 330, 332, 334, 336, 338, and/or 340 or other curve segments) connected by waypoints or breakpoints (e.g., waypoints 350, 352, 354, 356, and/or 358 or other waypoints). Waypoints may, for example, be the points or transitions between curve segments, points at which the slope or characteristics of optimal path curvature 310 change… during first path curvature segment 330 and second path curvature segment 332, host vehicle 10 may increase to maximum curvature or another curvature to steer away from the object 202. During third path curvature segment 334, host vehicle 10 may, for example, have avoided object 202 and may be reducing curvature to maximum negative curvature or other curvature to correct and/or compensate path curvature after avoiding object 202. During fourth path curvature segment 336, path curvature may, for example, remain constant and vehicle 10 may counter steer to correct or align vehicle position in target lane 230…” and Fig. 4 of Moshchuk – optimal path around an object is determined by combining trajectories in which a curvature of each of the trajectories changes linearly with respect to a longitudinal distance and curvature changes point at which the curvature changes non-linearly. Examiner notes that the flat curvature segments in Fig. 4 correspond to non-linear curvature changes, while the sloped segments in Fig. 4 correspond to linear curvature changes, and the waypoints are the curvature change points that combine the plurality of trajectories). Thus, Huang discloses a system for an autonomous vehicle that obtains environment data using images to identify static and dynamic obstacles and generate a map that predicts positions of the identified obstacles in a future time in order to determine a path of travel for the autonomous vehicle, while Moshchuk teaches a collision avoidance system for a vehicle that generates a path to avoid an object by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and curvature change points at which the curvature changes non-linearly at a plurality of curvature change points.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the moving object control system, moving object, control method, and non-transitory computer readable storage medium as disclosed in Huang to include the feature of the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points as taught by Moshchuk, with a reasonable expectation of success, in order to correct and compensate path curvature after avoiding an object as specified in at least [0048] of Moshchuk.
For claim 12, Huang discloses a control method of a moving object control system that controls an operation of a moving object (See at least the Abstract – “… an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps…” and [0034] of Huang – “… The trajectory 118 may comprise instructions for controller(s) 116 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate, which may result in a vehicle position, vehicle velocity, and/or vehicle acceleration that tracks the path generated by the guidance system…”), the control method comprising:
obtaining information of a sensor configured to recognize a periphery of the moving object (See at least [0046]-[0047] of Huang – “… vehicle 202 may include a vehicle computing device(s) 204, sensor(s) 206, emitter(s) 208… Vehicle computing device(s) 204 may represent computing device(s) 106 and sensor(s) 206 may represent sensor(s) 104… sensor(s) 206 may represent sensor(s) 104 and may include lidar sensors… image sensors … time of flight cameras… environment sensors … The sensor(s) 206 may provide input to the vehicle computing device(s)…”);
generating a dynamic prediction map (See at least [0071] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process 400 for determining a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity map… a computing device of an autonomous vehicle … may accomplish all the operations …”) including information indicating a position of a static obstacle recognized based on the information of the sensor (See at least [0037] of Huang – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data … the static data may indicate a likelihood that an object exists at a location in the environment …”) and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor (See at least [0037] – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data… the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment. In some instances, the dynamic data may comprise multiple frames associated with different times steps at intervals up to a prediction horizon (i.e., a maximum time/distance for which dynamic data is predicted…” and [0066]-[0070] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle… wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”);
generating a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map (See at least [0037] – “… the guidance system 114 discussed herein may generate a path 132 based at least in part on environment data 134 generated from sensor data captured by sensor(s) 104. For example, the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data. For example, the static data may indicate a likelihood that an object exists at a location in the environment and the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment…”, [0042] – “… The planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route…”, [0106] – “… operation 452 may comprise determining a path associated with the lowest cost node… determining the path 456 …” and Fig. 4C of Huang – path 456 determined in consideration of static and dynamic objects shown in map);
generating, as the target trajectory, a trajectory from a position of the moving object to a target position (See at least [0014] of Huang – “… determining the route between the start (current) position and an end position associated with the end location based at least in part on a map stored by the autonomous vehicle…”); and
controlling a drive device of the moving object to cause the moving object to travel along the target trajectory (See at least [0042] – “… the planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route… to control the vehicle to traverse the route (e.g., in order to avoid any of the detected objects… generate a drive control signal that may be transmitted to the controller(s) 116 for actuating drive components of the vehicle 102…” and [0052] of Huang – “… The drive component(s) 212 may include … a motor to propel the vehicle…”).
Huang fails to specifically disclose the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points.
However, Moshchuk, in the same field of endeavor teaches the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points (See at least [0047]-[0048] – “… optimal path curvature 310 may be defined by a piece-wise linear function of trajectory curvature 312 and time 314, trajectory curvature 312 and longitudinal distance 316… Optimal path curvature 310 may, for example, include multiple path curvature segments, trajectory curvature parts, or curvature segments (e.g., path curvature segments 330, 332, 334, 336, 338, and/or 340 or other curve segments) connected by waypoints or breakpoints (e.g., waypoints 350, 352, 354, 356, and/or 358 or other waypoints). Waypoints may, for example, be the points or transitions between curve segments, points at which the slope or characteristics of optimal path curvature 310 change… during first path curvature segment 330 and second path curvature segment 332, host vehicle 10 may increase to maximum curvature or another curvature to steer away from the object 202. During third path curvature segment 334, host vehicle 10 may, for example, have avoided object 202 and may be reducing curvature to maximum negative curvature or other curvature to correct and/or compensate path curvature after avoiding object 202. During fourth path curvature segment 336, path curvature may, for example, remain constant and vehicle 10 may counter steer to correct or align vehicle position in target lane 230…” and Fig. 4 of Moshchuk – optimal path around an object is determined by combining trajectories in which a curvature of each of the trajectories changes linearly with respect to a longitudinal distance and curvature changes point at which the curvature changes non-linearly. Examiner notes that the flat curvature segments in Fig. 4 correspond to non-linear curvature changes, while the sloped segments in Fig. 4 correspond to linear curvature changes, and the waypoints are the curvature change points that combine the plurality of trajectories). Thus, Huang discloses a system for an autonomous vehicle that obtains environment data using images to identify static and dynamic obstacles and generate a map that predicts positions of the identified obstacles in a future time in order to determine a path of travel for the autonomous vehicle, while Moshchuk teaches a collision avoidance system for a vehicle that generates a path to avoid an object by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and curvature change points at which the curvature changes non-linearly at a plurality of curvature change points.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the moving object control system, moving object, control method, and non-transitory computer readable storage medium as disclosed in Huang to include the feature of the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points as taught by Moshchuk, with a reasonable expectation of success, in order to correct and compensate path curvature after avoiding an object as specified in at least [0048] of Moshchuk.
For claim 13, Huang discloses a non-transitory computer readable storage medium storing a program for causing a moving object control system that controls an operation of a moving object to perform a control method of the moving object control system (See at least the Abstract – “… an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps…” and [0053]-[0054] of Huang – “… The vehicle computing device(s) 204 may include processor(s) 218 and memory 220 communicatively coupled with the one or more processors 218… may be any suitable processor capable of executing instructions to process data and perform operations as described herein… Memory 220 … may be examples of non-transitory computer-readable media … may store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems…”), the control method comprising:
obtaining information of a sensor configured to recognize a periphery of the moving object (See at least [0046]-[0047] of Huang – “… vehicle 202 may include a vehicle computing device(s) 204, sensor(s) 206, emitter(s) 208… Vehicle computing device(s) 204 may represent computing device(s) 106 and sensor(s) 206 may represent sensor(s) 104… sensor(s) 206 may represent sensor(s) 104 and may include lidar sensors… image sensors … time of flight cameras… environment sensors … The sensor(s) 206 may provide input to the vehicle computing device(s)…”);
generating a dynamic prediction map (See at least [0071] of Huang – “… FIGS. 4A-4C illustrates a pictorial flow diagram of an example process 400 for determining a path for controlling an autonomous vehicle based at least in part on environment data, which may be generated from sensor data, and may comprise an occupancy map indicating static objects and/or a dynamic object predicted position and/or velocity map… a computing device of an autonomous vehicle … may accomplish all the operations …”) including information indicating a position of a static obstacle recognized based on the information of the sensor (See at least [0037] of Huang – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data … the static data may indicate a likelihood that an object exists at a location in the environment …”) and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor (See at least [0037] – “… the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data… the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment. In some instances, the dynamic data may comprise multiple frames associated with different times steps at intervals up to a prediction horizon (i.e., a maximum time/distance for which dynamic data is predicted…” and [0066]-[0070] of Huang – “… FIGS. 3A and 3B depict an example of dynamic data 300 and dynamic data 302 that may have been determined by the perception component based at least in part on sensor data associated with an object in an environment surrounding a vehicle… wherein each frame is associated with a different future time… may indicate a likelihood that an object will occupy a portion of the environment corresponding to the discrete portion at the time associated with the frame…”);
generating a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map (See at least [0037] – “… the guidance system 114 discussed herein may generate a path 132 based at least in part on environment data 134 generated from sensor data captured by sensor(s) 104. For example, the perception component 112 may generate all or part of environment data 134, which may comprise static data and/or dynamic data. For example, the static data may indicate a likelihood that an object exists at a location in the environment and the dynamic data may indicate a likelihood that an object occupies or will occupy a location in the environment…”, [0042] – “… The planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route…”, [0106] – “… operation 452 may comprise determining a path associated with the lowest cost node… determining the path 456 …” and Fig. 4C of Huang – path 456 determined in consideration of static and dynamic objects shown in map);
generating, as the target trajectory, a trajectory from a position of the moving object to a target position (See at least [0014] of Huang – “… determining the route between the start (current) position and an end position associated with the end location based at least in part on a map stored by the autonomous vehicle…”); and
controlling a drive device of the moving object to cause the moving object to travel along the target trajectory (See at least [0042] – “… the planning component 112 may use the perception data received from perception component 110 and/or a path received from the guidance system 114, to determine one or more trajectories, control motion of the vehicle 102 to traverse a path or route… to control the vehicle to traverse the route (e.g., in order to avoid any of the detected objects… generate a drive control signal that may be transmitted to the controller(s) 116 for actuating drive components of the vehicle 102…” and [0052] of Huang – “… The drive component(s) 212 may include … a motor to propel the vehicle…”).
Huang fails to specifically disclose the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points.
However, Moshchuk, in the same field of endeavor teaches the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points (See at least [0047]-[0048] – “… optimal path curvature 310 may be defined by a piece-wise linear function of trajectory curvature 312 and time 314, trajectory curvature 312 and longitudinal distance 316… Optimal path curvature 310 may, for example, include multiple path curvature segments, trajectory curvature parts, or curvature segments (e.g., path curvature segments 330, 332, 334, 336, 338, and/or 340 or other curve segments) connected by waypoints or breakpoints (e.g., waypoints 350, 352, 354, 356, and/or 358 or other waypoints). Waypoints may, for example, be the points or transitions between curve segments, points at which the slope or characteristics of optimal path curvature 310 change… during first path curvature segment 330 and second path curvature segment 332, host vehicle 10 may increase to maximum curvature or another curvature to steer away from the object 202. During third path curvature segment 334, host vehicle 10 may, for example, have avoided object 202 and may be reducing curvature to maximum negative curvature or other curvature to correct and/or compensate path curvature after avoiding object 202. During fourth path curvature segment 336, path curvature may, for example, remain constant and vehicle 10 may counter steer to correct or align vehicle position in target lane 230…” and Fig. 4 of Moshchuk – optimal path around an object is determined by combining trajectories in which a curvature of each of the trajectories changes linearly with respect to a longitudinal distance and curvature changes point at which the curvature changes non-linearly. Examiner notes that the flat curvature segments in Fig. 4 correspond to non-linear curvature changes, while the sloped segments in Fig. 4 correspond to linear curvature changes, and the waypoints are the curvature change points that combine the plurality of trajectories). Thus, Huang discloses a system for an autonomous vehicle that obtains environment data using images to identify static and dynamic obstacles and generate a map that predicts positions of the identified obstacles in a future time in order to determine a path of travel for the autonomous vehicle, while Moshchuk teaches a collision avoidance system for a vehicle that generates a path to avoid an object by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and curvature change points at which the curvature changes non-linearly at a plurality of curvature change points.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the moving object control system, moving object, control method, and non-transitory computer readable storage medium as disclosed in Huang to include the feature of the trajectory being configured by combining a plurality of trajectories in which a curvature of each of the trajectories changes linearly with respect to a distance and a curvature change point at which the curvature changes non-linearly, wherein the trajectory has three of the curvature change points as taught by Moshchuk, with a reasonable expectation of success, in order to correct and compensate path curvature after avoiding an object as specified in at least [0048] of Moshchuk.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J HERRERA whose telephone number is (571)270-5271. The examiner can normally be reached M-F 10:00 AM to 6:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FADEY JABR can be reached at (571)272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.J.H./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
