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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/10/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-2 and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Masaki Syouichi et al. (US4651290), hereinafter referred to as Syouichi, in view of Kobilarov Marin et al. (US20200363806A1), hereinafter referred to as Marin, in further view of Kawabe Koji et al. (US20190283743A1), hereinafter referred to as Koji, in further view of Crickmore Roger et al. (CN105122328A), hereinafter referred to as Roger.
Regarding claim 1, Syouichi discloses: a vehicle control system comprising: at least one sensor including a plurality of wheel sensors configured to acquire data related to driving of a vehicle from the vehicle and an external environment (see at least Syouichi, Fig.2a-c, “vehicle speed sensor,” pg.9, par.1, lines 23-25, par.2, which discloses vehicle speed related to the driving in an external environment via wheel speed sensors)
one or more processors configured to process the data related to the driving of the vehicle to determine at least one candidate trajectory (see at least Syouichi, pg.9, par.2, lines 36-43 which discloses processing data related to the driving of a vehicle along a candidate road)
for each of the wheel speed sensors, calculate a difference value between a maximum and a minimum speed measurement over a specified time duration (see at least Syouichi, pg.11, par.5, lines 20-43 which discloses measuring how much the wheel speed and acceleration fluctuates based on taking a maximum and minimum value measured from sensor data over a specified duration of performance on a road surface, this means that for each of the wheel speed sensors, a difference value between a maximum and a minimum speed measurement over a specified time duration is calculated)
compute a variance of a movement value from the difference values and determine a noise level of a road surface associated with the at least one candidate trajectory based on the computed variance (see at least Syouichi, pg.11, par.5, lines 45-64, par.8, lines 15-31, which discloses computing a variance from the difference values, representing the magnitude of variations in wheel revolutions, serving as a parameter indicating the road surface conditions, this means that a variance of movement value is calculated and used to determine a noise level of a road surface associated with the at least one candidate trajectory)
Syouichi is silent on, however, in the same field of endeavor, Marin teaches: calculate bidirectional trajectories information of a current point of a three-dimensional map of a driving environment (see at least Marin, ¶¶ [0068], [0070], [0079], which discloses looking at a current location on a map/model of a driving environment and determining a trajectory that can include bi-directionality, this means calculate bidirectional trajectories information of a current point of a three-dimensional map of a driving environment)
assign weights to positions along the candidate trajectory based on the determined noise level, such that trajectory segments corresponding to lower noise levels are weighted more heavily (see at least Marin, ¶¶ [0019]-[0021], [0025], [0041], [0070]-[0071], which discloses assigning rankings, or weights to a sequence of waypoints along a calculated trajectory from the route planner component to determine the most efficient route based on noise levels (friction, road surface conditions) associated with a value of a road segment)
generate a final valid trajectory from the candidate trajectory based on the weighted positions apply the final valid trajectory to the three-dimensional map (see at least Marin, Fig.7, which disclose the process for controlling an autonomous vehicle based on the final candidate trajectory, ¶¶ 0025], [0041], [0070]-[0071], [0097]-[0098], which discloses generating an optimal trajectory from candidate trajectories based on the weights)
control the vehicle to adjust a travel direction according to the final valid trajectory (see at least Marin, Fig.7, which disclose the process for controlling an autonomous vehicle based on the final candidate trajectory, ¶¶ [0097]-[0098])
It would have been obvious to a person of ordinary skill in the art to modify Syouichi to include calculate bidirectional trajectories information of a current point of a three-dimensional map of a driving environment, assign weights to positions along the candidate trajectory based on the determined noise level, such that trajectory segments corresponding to lower noise levels are weighted more heavily, generate a final valid trajectory from the candidate trajectory based on the weighted positions apply the final valid trajectory to the three-dimensional map, and control the vehicle to adjust a travel direction according to the final valid trajectory as taught by Marin. Incorporating these teachings would allow for an improvement to the base invention of Syouichi that considers different factors in determining a final trajectory according to noise and bidirectional filtering.
Modified Syouichi is silent on, however, in the same field of endeavor, Koji teaches: identify whether a road width of the current point is greater than or equal to a first threshold value (see at least Koji, ¶¶ [0040]-[0041], [0059]-[0060], [0062]-[0063] which discloses identify whether a road width of the current point is greater than or equal to a first threshold value)
It would have been obvious to a person of ordinary skill in the art to change modified Syouichi to include identify whether a road width of the current point is greater than or equal to a first threshold value as taught by Koji. Incorporating the teaching of Koji would allow for the monitoring of bidirectional trajectories in addition to the corresponding noise level that provides an additional layering to the projected trajectory information.
Further modified Syouichi is silent on, however, in the same field of endeavor, Roger teaches:
in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level (see at least Roger. pg.30, par.3, which discloses the generation of various acoustic signature maps in response to the noise elements having an asymmetrical arrangement that varies depending on the direction that the vehicle is traveling, such noise features can thus be arranged to span the entire width of the bi-directional road to monitor traffic moving in both directions, the direction of travel being indicated by the resulting sound pattern, this means in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include: identify whether a road width of the current point is greater than or equal to a first threshold value and in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level as taught by Roger. Incorporating the teachings of Roger into modified Syouichi would allow for an improvement that provides information regarding two-way paths and updates stored data to improve accuracy and reliability.
Regarding claim 2, Syouichi discloses: the system of claim 1, wherein the system further comprises: an input device for receiving a user input for controlling a driving function of the vehicle (see at least Syouichi, pg.12, par. 8, lines 15-29 which discloses an input device for receiving user input for controlling driving of a vehicle; pg.13, par.9, lines 51-63, which discloses various components a vehicle controller that control certain aspects of driving of the vehicle, such as power steering and skid control)
an output device providing information related to the driving of the vehicle (see at least Syouichi, pg.13, par.9, lines 40-49, which disclose an output device that triggers an output signal relative to the detected road condition detected)
a vehicle controller configured to control the driving of the vehicle (see at least Syouichi, pg.13, par.9, lines 51-63, which discloses various components a vehicle controller that control certain aspects of driving of the vehicle, such as power steering and skid control)
Syouichi is silent on, however, in the same field of endeavor, Marin teaches: an imaging device for sensing and imaging the external environment (see at least Marin, ¶¶ [0062]-[0064] which discloses an imaging device to capture image data of an external environment)
It would have been obvious to a person of ordinary skill in the art to modify Syouichi to include an imaging device for sensing and imaging the external environment as taught by Marin. Incorporating the teaching would allow for an improvement to the base invention of Syouichi that provides depth data of an environment, in addition to sensor fusion data of vehicle motion characteristics.
Regarding claim 13, Syouichi discloses: a method for driving a vehicle using a vehicle control system, the method comprising:
receiving, from at least one sensor including a wheel speed sensor, movement values of a vehicle over a time interval (see at least Syouichi, Fig.2a-c, “vehicle speed sensor,” pg.9, par.1, lines 23-25, par.2, which discloses vehicle speed related to the driving in an external environment via wheel speed sensors)
calculating, for each trajectory, a variance of the movement values (see at least Syouichi, pg.11, par.5, lines 20-43 which discloses measuring how much the wheel speed and acceleration fluctuates based on taking a maximum and minimum value measured from sensor data over a specified duration of performance on a road surface, this means that for each of the wheel speed sensors, a difference value between a maximum and a minimum speed measurement over a specified time duration is calculated)
determining a noise level of a road surface associated with the trajectory based on the variance (see at least Syouichi, pg.11, par.5, lines 45-64, par.8, lines 15-31, which discloses computing a variance from the difference values, representing the magnitude of variations in wheel revolutions, serving as a parameter indicating the road surface conditions, this means that a variance of movement value is calculated and used to determine a noise level of a road surface associated with the at least one candidate trajectory)
Syouichi is silent on, however, in the same field of endeavor, Marin teaches:
calculating bidirectional trajectory information for a current point of the vehicle on a three-dimensional map (see at least Marin, ¶¶ [0068], [0070], [0079], which discloses looking at a current location on a map/model of a driving environment and determining a trajectory that can include bi-directionality, this means calculate bidirectional trajectories information of a current point of a three-dimensional map of a driving environment)
assigning a weight to each trajectory based on the noise (see at least Marin, ¶¶ [0019]-[0021], [0025], [0041], [0070]-[0071], which discloses assigning rankings, or weights to a sequence of waypoints along a calculated trajectory from the route planner component to determine the most efficient route based on noise levels (friction, road surface conditions) associated with a value of a road segment)
determining a final valid trajectory by prioritizing lower-noise, higher-confidence trajectory segments (see at least Marin, Fig.7, which disclose the process for controlling an autonomous vehicle based on the final candidate trajectory, ¶¶ 0025], [0041], [0070]-[0071], [0097]-[0098], which discloses generating an optimal trajectory from candidate trajectories based on the weights)
applying the final valid trajectory to the three-dimensional map (see at least Marin, Fig.7, which disclose the process for controlling an autonomous vehicle based on the final candidate trajectory, ¶¶ 0025], [0041], [0070]-[0071], [0097]-[0098], which discloses generating an optimal trajectory from candidate trajectories based on the weights)
controlling the vehicle to adjust a travel direction according to the final valid trajectory (see at least Marin, Fig.7, which disclose the process for controlling an autonomous vehicle based on the final candidate trajectory, ¶¶ [0097]-[0098])
It would have been obvious to a person of ordinary skill in the art to modify Syouichi to include calculate bidirectional trajectories information of a current point of a three-dimensional map of a driving environment, assign weights to positions along the candidate trajectory based on the determined noise level, such that trajectory segments corresponding to lower noise levels are weighted more heavily, generate a final valid trajectory from the candidate trajectory based on the weighted positions apply the final valid trajectory to the three-dimensional map, and control the vehicle to adjust a travel direction according to the final valid trajectory as taught by Marin. Incorporating these teachings would allow for an improvement to the base invention of Syouichi that considers different factors in determining a final trajectory according to noise and bidirectional filtering.
Modified Syouichi is silent on, however, in the same field of endeavor, Koji teaches: identifying whether there is no overlapping section between the bidirectional trajectories in a direction corresponding to a width of the vehicle, in response to determining that the road width is equal to or greater than the first threshold value (see at least Roger. pg.30, par.3, which discloses the generation of various acoustic signature maps in response to the noise elements having an asymmetrical arrangement that varies depending on the direction that the vehicle is traveling, such noise features can thus be arranged to span the entire width of the bi-directional road to monitor traffic moving in both directions, the direction of travel being indicated by the resulting sound pattern, this means in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level)
It would have been obvious to a person of ordinary skill in the art to change modified Syouichi to include identify whether a road width of the current point is greater than or equal to a first threshold value as taught by Roger. Incorporating the teaching of Roger would allow for the monitoring of bidirectional trajectories in addition to the corresponding noise level that provides an additional layering to the projected trajectory information.
Further modified Syouichi is silent on, however, in the same field of endeavor, Roger teaches:
updating the bidirectional trajectory information and the noise level of the current point only in response to determining that the bidirectional trajectories do not overlap (see at least Roger. pg.30, par.3, which discloses the generation of various acoustic signature maps in response to the noise elements having an asymmetrical arrangement that varies depending on the direction that the vehicle is traveling, such noise features can thus be arranged to span the entire width of the bi-directional road to monitor traffic moving in both directions, the direction of travel being indicated by the resulting sound pattern, this means in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include: identify whether a road width of the current point is greater than or equal to a first threshold value and in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level as taught by Roger. Incorporating the teachings of Roger into modified Syouichi would allow for an improvement that provides information regarding two-way paths and updates stored data to improve accuracy and reliability.
Regarding claim 14, Syouichi discloses: the method of claim 13, wherein calculating the variance of the movement values comprises computing a difference between a maximum value and a minimum wheel speed over a pre-defined time window for each wheel speed sensors and using the computed variance to derive the road surface noise level (see at least Syouichi, pg.11, par.5, lines 20-43 which discloses measuring how much the wheel speed and acceleration fluctuates based on taking a maximum and minimum value measured from sensor data over a specified duration of performance on a road surface, this means that for each of the wheel speed sensors, a difference value between a maximum and a minimum speed measurement over a specified time duration is calculated, pg.11, par.5, lines 45-64, par.8, lines 15-31, which discloses computing a variance from the difference values, representing the magnitude of variations in wheel revolutions, serving as a parameter indicating the road surface conditions, this means that a variance of movement value is calculated and used to determine a noise level of a road surface associated with the at least one candidate trajectory)
Regarding claim 15, Syouichi is silent on, however, in the same field of endeavor, Roger teaches: the method of claim 13, wherein updating the bidirectional trajectory information and the noise level is suppressed when the bidirectional trajectories partially overlap within a vehicle width threshold, thereby preventing unreliable map updates (see at least Roger. pg.30, par.3, which discloses the generation of various acoustic signature maps in response to the noise elements having an asymmetrical arrangement that varies depending on the direction that the vehicle is traveling, such noise features can thus be arranged to span the entire width of the bi-directional road to monitor traffic moving in both directions, the direction of travel being indicated by the resulting sound pattern, this means in response to a determination that the road width is greater than or equal to the threshold and that there is no overlapping section between bidirectional trajectories in a vehicle width direction, update the bidirectional trajectory information and the corresponding noise level)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include: wherein updating the bidirectional trajectory information and the noise level is suppressed when the bidirectional trajectories partially overlap within a vehicle width threshold, thereby preventing unreliable map updates as taught by Roger. Incorporating the teachings of Koji into modified Syouichi would allow for an improvement that provides information regarding two-way paths and updates stored data to improve accuracy and reliability.
Regarding claim 16, Syouichi is silent on, however, in the same field of endeavor, Marin teaches: the method of claim 13, wherein the weight applied to each trajectory is a confidence score that is inversely proportional to the noise level, and the final valid trajectory is selected by maximizing the aggregated confidence along the trajectory (see at least Marin, ¶¶ [0019]-[0021], [0025], [0041], [0070]-[0071], which discloses assigning rankings, or weights to a sequence of waypoints along a calculated trajectory from the route planner component to determine the most efficient route based on noise levels (friction, road surface conditions) associated with a value of a road segment, this means weight applied to each trajectory is a confidence score that is inversely proportional to the noise level, and the final valid trajectory is selected by maximizing the aggregated confidence along the trajectory)
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over further modified Syouichi in view of Ibraham Faroog et al. (US20180045832A1), hereinafter referred to as Faroog.
Regarding claim 7, further modified Syouichi discloses: a vehicle control system comprising:
at least one sensor for acquiring data related to the driving of the vehicle from a vehicle and an external environment (see at least Syouichi, Fig.2a-c, “vehicle speed sensor,” pg.9, par.1, lines 23-25, par.2, which discloses vehicle speed related to the driving in an external environment via wheel speed sensors)
a vehicle controller configured to control the driving of the vehicle and one or more processors (see at least Syouichi, pg.13, par.9, lines 51-63, which discloses various components a vehicle controller that control certain aspects of driving of the vehicle, such as power steering and skid control) configured to:
process the data related to driving of a vehicle (see at least Syouichi, pg.9, par.2, lines 36-43 which discloses processing data related to the driving of a vehicle along a candidate road)
acquire other vehicle information and lane information using the at least one sensor (see at least Koji, ¶¶ [0006]-[0010], [0031], [0040]-[0041] which discloses acquiring other vehicle and lane information (for example, whether or not the object is changing lanes or trying to change lanes)
transmit the other vehicle information and the lane information to a server (see at least Koji, ¶¶ [0006]-[0010], [0031], [0040]-[0041] which discloses acquiring other vehicle and lane information (for example, whether or not the object is changing lanes or trying to change lanes)
Further modified Syouichi is silent on, however, in the same field of endeavor, Faroog teaches: receive, from the server, information related to an adjacent vehicle including an indication of a detected or predicted lane change by the adjacent vehicle (see at least Faroog, Fig.31, ¶¶ [0038]-[0039], [0138], [0149], [0229] which discloses information related to vehicles, or a vehicle (node or nodes) within proximity of a host vehicle, including an indication of a detected or predicted lane change)
identify whether the adjacent vehicle has changed lanes based on sensor data and predicted trajectory (see at least Faroog, ¶¶ [0229] which discloses identifying and detecting vehicles in proximity executing lane changes)
transmit information related to whether the adjacent vehicle has changed the lane along with confidence data based on road surface conditions or sensor reliability to the server (see at least Faroog, ¶¶ [0138, [0229], discloses the transmission of map generated information related to whether a vehicle has changed lanes based on the confidence of nodes reported to the server, this means transmitting information related to whether the adjacent vehicle has changed the lane along with confidence data based on road surface conditions or sensor reliability)
wherein the server determines whether the lane change of the adjacent vehicle is valid by aggregating data from multiple vehicles and applying filtering based on reported noise levels, trajectory consistency, or historical behavior patterns (see at least Faroog, ¶¶ [0138], [0229], discloses the transmission of map generated information related to whether a vehicle has changed lanes based on the confidence of nodes reported to the server, this means the server determines whether the lane change of the adjacent vehicle is valid by aggregating data from multiple vehicles and applying filtering based on reported noise levels, trajectory consistency, or historical behavior patterns)
the vehicle controller is configured to control the driving of the vehicle based on whether the lane change of the adjacent vehicle is valid (see at least Faroog, ¶¶ [0138], [0229], [0379], [0384] which discloses controlling the driving of the vehicle based on whether the lane change of the adjacent vehicle is valid)
It would have been obvious to a person of ordinary skill in the art to change further modified Syouichi to include receive, from the server, information related to an adjacent vehicle including an indication of a detected or predicted lane change by the adjacent vehicle, identify whether the adjacent vehicle has changed lanes based on sensor data and predicted trajectory, transmit information related to whether the adjacent vehicle has changed the lane along with confidence data based on road surface conditions or sensor reliability to the server, wherein the server determines whether the lane change of the adjacent vehicle is valid by aggregating data from multiple vehicles and applying filtering based on reported noise levels, trajectory consistency, or historical behavior patterns, and the vehicle controller is configured to control the driving of the vehicle based on whether the lane change of the adjacent vehicle is valid as taught by Faroog. Incorporating the teachings would allow for an improvement to the invention of further modified Faroog that provides vehicle-to-vehicle application that offers a novel quality filter that can detect noise and the onset of drift in GNSS signals by evaluating up to four metrics that compare the qualities of kinematic variables, speed, heading angle change, curvature, and lateral displacement.
Regarding claim 8, further modified Syouichi discloses: the system of claim 7, wherein the system further comprises:
an input device for receiving a user input for controlling a driving function of the vehicle (see at least Syouichi, pg.12, par. 8, lines 15-29 which discloses an input device for receiving user input for controlling driving of a vehicle; pg.13, par.9, lines 51-63, which discloses various components a vehicle controller that control certain aspects of driving of the vehicle, such as power steering and skid control)
an output device for providing information related to the driving of the vehicle (see at least Syouichi, pg.13, par.9, lines 40-49, which disclose an output device that triggers an output signal relative to the detected road condition detected)
Syouichi is silent on, however, in the same field of endeavor, Marin teaches: an imaging device for sensing and imaging the external environment (see at least Marin, ¶¶ [0062]-[0064] which discloses an imaging device to capture image data of an external environment)
It would have been obvious to a person of ordinary skill in the art to modify Syouichi to include an imaging device for sensing and imaging the external environment as taught by Marin. Incorporating the teaching would allow for an improvement to the base invention of Syouichi that provides depth data of an environment, in addition to sensor fusion data of vehicle motion characteristics.
Regarding claim 9, modified Syouichi discloses: the system of claim 7, wherein the one or more processors are configured to transmit GPS information, information about at least one of the vehicle and the adjacent vehicle, and the lane information recognized using the at least one sensor to the server (see at least Marin, ¶¶ [0054], [0067]-[0070] which discloses wherein the one or more processors are configured to transmit GPS information, information about at least one of the vehicle and the adjacent vehicle, and the lane information recognized using the at least one sensor to the server)
It would have been obvious to a person of ordinary skill in the art to modify Syouichi to include wherein the one or more processors are configured to transmit GPS information, information about at least one of the vehicle and the adjacent vehicle, and the lane information recognized using the at least one sensor to the server as taught by Marin. Incorporating the teaching would allow for an improvement to the base invention of Syouichi that provides navigational data of an environment, in addition to sensor fusion data of vehicle motion characteristics.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over further modified Syouichi in view of Shida Mitsuhisa et al. (US20150294571A1), hereinafter referred to as Mitsuhisa.
Regarding claim 10, further modified Syouichi discloses: the system of claim 7, wherein when a line on an actual road is recognized, the one or more processors are configured to:
determine similarity of a field of view range of a line toward a position of the adjacent vehicle and a longitudinal distance of an object with each other (see at least Marin, ¶¶ [0030], which discloses determining if the detected curvature values are with adjacent frames expected within the range, [0063]-[0064] which discloses using a reference trajectory component to determine extrinsic characteristics of a field of view
Further modified Syouichi is silent on, however, in the same field of endeavor, Mitsuhisa teaches: identify whether a signal of the FOV range of the line is discontinuous and thus identify whether the adjacent vehicle has changed the lane (see at least Mitsuhisa, ¶¶ [0036]-[0038] which disclose the yaw rate and azimuthal boundary)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include generate a virtual line based on a yaw rate based on the vehicle having the system and identify whether the adjacent vehicle crosses the virtual line and thus identify whether the adjacent vehicle has changed the lane as taught by Mitsuhisa. Doing so would allow for a yaw rate command determination for when a line on the road is not recognized as well as in the event of a heading adjacent vehicle.
Regarding claim 11, further modified Syouichi is silent on, however, in the same field of endeavor, Mitsuhisa teaches: the system of claim 7, wherein when the line on the road is not recognized, the one or more processors are configured to:
generate a virtual line based on a yaw rate based on the vehicle having the system (see at least Mitsuhisa, ¶¶ [0036]-[0038] which disclose the yaw rate and azimuthal boundary)
identify whether the adjacent vehicle crosses the virtual line and thus identify whether the adjacent vehicle has changed the lane (see at least Mitsuhisa, ¶¶ [0036]-[0038] which discloses identifying whether the adjacent vehicle crosses the virtual line and thus identify whether the adjacent vehicle has changed the lane)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include generate a virtual line based on a yaw rate based on the vehicle having the system and identify whether the adjacent vehicle crosses the virtual line and thus identify whether the adjacent vehicle has changed the lane as taught by Mitsuhisa. Doing so would allow for a yaw rate command determination for when a line on the road is not recognized as well as in the event of a heading adjacent vehicle.
Regarding claim 12, further modified Syouichi is silent on, however, in the same field of endeavor, Mitsuhisa teaches: the system of claim 7, wherein the server is configured to exclude unnecessary line change trajectory as a line change other than the valid line change in calculating a trajectory (see at least Mitsuhisa, ¶¶ [0036]-[0038] which discloses wherein the server is configured to exclude unnecessary line change trajectory as a line change other than the valid line change in calculating a trajectory)
It would have been obvious to a person of ordinary skill in the art to further change modified Syouichi to include wherein the server is configured to exclude unnecessary line change trajectory as a line change other than the valid line change in calculating a trajectory as taught by Mitsuhisa. Doing so would allow for a yaw rate command determination for when a line on the road is not recognized as well as in the event of a heading adjacent vehicle
Conclusion
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/KIRSTEN JADE M SANTOS/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664