CTFR 18/960,673 CTFR 98538 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims The following is a non-final, first office action in response to the communication filed on 02/09/2026. Claims 1—2 and 4—5 are currently pending. Priority The Applicant’s claim for benefit of Japanese Patent Application Number JP2023-214509 filed on 12/20/2023, has been received and acknowledged. Information Disclosure Statement Information Disclosure Statement received 11/26/2024, 8/18/2025, and 04/20/2026 have been reviewed and considered. Response to Arguments Applicant’s amendments, filed 02/09/2026, with respect to the drawing objection have been fully considered and are persuasive. The drawing objection of FIGs 2A—5B is withdrawn. Applicant’s arguments and amendments, filed 02/09/2026, with respect to the rejection of claims 1—5 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of claims 1—2 and 4—5 under 35 U.S.C. 112(b) has been withdrawn. Examiner notes claim 3 has been cancelled. Applicant’s arguments and amendments, filed 02/09/2026, with respect to the rejection(s) of claim(s) 1—2 and 4—5 under 35 U.S.C. 102 and 35 U.S.C. 103 have been fully considered and are persuasive in overcoming the previous rejection of record. However, upon further consideration, a new ground(s) of rejection is made in view of Yoshihara as a single reference. Therefore, the previous rejection over the prior art has been withdrawn and replaced with the rejection using Yoshihara as provided below. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim(s) 1—2 and 4—5 is /are reject ed under 35 U.S.C. 103 as being unpatentable over Published US P atent Application to Yoshihara et al., hereinafter “Yoshihara” ( US 20210253136 A1 ) . Regarding cla im 1 , Yoshihara discloses [a] driving assistance device comprising: an in-vehicle sensor (para. [0007], “ a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle ”; para. [0045], “the vehicle system 1 includes a camera 10, a radar device 12, a light detection and ranging (LIDAR) sensor 14 , a physical object recognition device 16, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40 , a navigation device 50, a map positioning unit (MPU) 60, driving operating elements 80, an automated driving control device 100, a travel driving force output device 200, a brake device 210, and a steering device 220.”; para. [0059], “[t]he recognizer 122 recognizes states of a position, a velocity, acceleration, and the like of a physical object around the vehicle M on the basis of information input from the camera 10, the radar device 12, and the LIDAR sensor 14 via the physical object recognition device 16 .” The sensors that provide data to the recognizer constitute in-vehicle sensors) that acquires information related to an own vehicle (vehicle sensor 40; para. [0052], “[t]he vehicle sensor 40 includes a vehicle speed sensor configured to detect the speed of the vehicle M, an acceleration sensor configured to detect acceleration, a yaw rate sensor configured to detect angular velocity around a vertical axis, a direction sensor configured to detect a direction of the vehicle M, and the like.”) and information related to a preceding vehicle (camera 10, see para. [0046]; radar device 12, see para. [0047]; and LIDAR sensor 14, see para. [0048]) traveling ahead of the own vehicle; and a processor capable of executing a speed adjustment process (para. [0057], “[t]he automated driving control device 100 includes, for example, a first controller 120, a second controller 160, and a storage 170. Each of the first controller 120 and the second controller 160 is implemented , for example, by a hardware processor such as a central processing unit (CPU) executing a program (software).”; para. [0058], “[t]he first controller 120 includes , for example, a recognizer 122, an information manager 124, and the action plan generator 126 .”) by controlling (para. [0007], “a travel controller configured to control behavior of the vehicle based on the predicted probability derived by the deriver.”); a drive device and/or a braking device (para. [0056], “driving operating elements 80 include an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a steering wheel variant, a joystick , and other operators. A sensor configured to detect an amount of operation or the presence or absence of an operation is attached to the driving operating element 80, and a detection result thereof is output to the automated driving control device 100 or some or all of the travel driving force output device 200, the brake device 210, and the steering device 220.”) of the own vehicle based on the information related to the own vehicle and/or the information related to the preceding vehicle (para. [0007], “ a deriver configured to derive a predicted probability that the other vehicle will travel in the future along each of routes which are assumed when a plurality of routes along which the other vehicle is able to travel are assumed on a road on which the other vehicle recognized by the recognizer travels ”; para. [0059], “[t]he recognizer 122 recognizes states of a position, a velocity, acceleration, and the like of a physical object around the vehicle M on the basis of information input from the camera 10, the radar device 12, and the LIDAR sensor 14 via the physical object recognition device 16 .”) to adjust the speed of the own vehicle (see FIG. 3 which depicts the workflow of action generator 126 which takes three derivers, 130, 132, and 134, and outputs an adjustment action at action determiner 140. See para. [0072] for a high-level description of the workflow of action generator 126) , wherein: in a first specific scene, (i) the own vehicle and the preceding vehicle are traveling on the leftmost lane or a left-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 107 and 109 which are directed to “left turn only lane” and “go straight/left turn only lane” respectively; para. [0086], “[l]eft turn only lane; Case that the next base path is within a predetermined distance from the base path along which the vehicle travels and the vehicle is traveling in a lane in which a “left turn” is associated with the attribute of the base path in front of the vehicle (see No. 107 in FIGS. 4 and 6). In this case, for example, the probability is higher in the order of the left turn direction probability, the straight direction probability, and the right turn direction probability.) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road ) , where and (ii) the turn signal of the preceding vehicle indicates a left turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability, the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability , the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking .” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle, and the state model 176 (step S110).”) ; and in a second specific scene, where (i) the own vehicle and the preceding vehicle are traveling on the rightmost lane or a right-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 106 and 108 which are directed to “right turn only lane” and “go straight/right turn only lane” respectively; para. [0084], “[r]ight turn only lane… the vehicle is traveling in a lane in which a ‘right turn’ is associated with an attribute of the base path in front of the vehicle (see No. 106 in FIGS. 4 and 6). In this case, for example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability.) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road) , where and (ii) the turn signal of the preceding vehicle indicates a right turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability, the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking.” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle, and the state model 176 (step S110).”) , determine that the first specific scene or the second specific scene occurs (the limitations of the first scene and second scene are taken as inputs, by way of first deriver 130, second deriver 132, and third deriver 134, to the action generator 126 as depicted in FIG. 3. Yoshihara describes multiple scene configurations as provided by FIG. 4, 8, 9, and 11 which include configurations which read on the first and second scene as provided above. See para. [0072] for a high-level description of the workflow of action generator 126.) based upon the determination that the first specific scene or the second specific scene occurs, execute, as the speed adjustment process (the output from the action plan generator 126 workflow as depicted in FIG. 3 is an action generated by the action determiner 140 where the actions include accelerating and decelerating; para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0118], “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels. The characteristics of the road include a road structure or a combination of a location (a position) of the road and a structure of the road, the location of the road, restrictions (for example, laws and regulations) on the road on which the other vehicle is traveling, and the like. For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions . For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Examiner notes that at least some embodiments of the first and second scene (e.g., vehicles located in a turning lane) would be associated, respectively, with the trajectories OR3 and OR1 as depicted in FIG. 14; para. [0128], “ it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1, the action determiner 140 causes the vehicle M to decelerate, for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) : a deceleration process that controls the drive device and/or the braking device so that the own vehicle decelerates at a predetermined deceleration rate (see citation above to para. [0064]—[0065], para. [0118], and para. [0128]) , wherein the predetermined deceleration rate is smaller than a deceleration rate used in a vehicle-following process, wherein the vehicle-following process adjusts the speed of the own vehicle so that a distance between the own vehicle and the preceding vehicle matches a target value in scenes other than when the first specific scene or the second specific scene occurs (para. [0137], “the automated driving control device 100 can improve the ride comfort of the vehicle M of the occupant by controlling the vehicle M so that the change in the acceleration of the vehicle M does not become large without limiting the behavior of the other vehicle m to one type of behavior. For example, the automated driving control device 100 predicts the behavior of the other vehicle m and controls the vehicle M in advance on the basis of a prediction result , thereby restricting the acceleration or deceleration of the vehicle M from becoming higher than or equal to a predetermined value . Because the automated driving control device 100 can determine a degree of the above-described restriction on the basis of a position of the other vehicle m after a predetermined time when the prediction has been performed, smoother control of the vehicle M can be implemented.” Examiner notes that the own car which is following the preceding car does not merely match the speed of the preceding car but predicts a trajectory of the preceding car which may further restrict the acceleration of the own car in order to reduce large changes in acceleration) ; or an acceleration suppression process that controls the drive device (driving force output device 200) and/or the braking device (braking device 210) so that the acceleration of the own vehicle is suppressed (para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200 , the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0128], “[t]he action determiner 140 determines an action of the vehicle M on the basis of an estimation result of the estimator 138. The action determiner 140 integrates behaviors of the vehicle according to the behaviors of the other vehicle which are assumed on the basis of the predicted probability and controls the behavior of the vehicle on the basis of the behaviors of the vehicle after the integration. For example, the action determiner 140 determines a velocity, acceleration, and a position of the vehicle on the basis of a position of another vehicle at each time . For example, the action determiner 140 derives the behavior of the vehicle for each traveling pattern in which the other vehicle travels on the basis of the trajectories OR1 to OR3. The action determiner 140 derives the behavior of the vehicle on the basis of an integrated index corresponding to the traveling pattern . For example, the action determiner 140 derives a reflection rate for the behavior of the vehicle on the basis of the integrated index for each traveling pattern and further determines the behavior of the vehicle on the basis of the derived reflection rate . For example, when an integrated index corresponding to the traveling pattern of the trajectory OR1 is greater than the integrated indices of the traveling patterns of the other trajectories , the reflection rate of the behavior of the vehicle according to the traveling pattern of the trajectory OR1 becomes greater than the reflection rates of the behaviors of the vehicle according to the traveling patterns of the other trajectories. Because it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1 , the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) , and when an operation of the turn signal of the preceding vehicle is stopped while the deceleration process or the acceleration suppression process is being executed, execution of the deceleration process or the acceleration suppression process is continued for a predetermined period of time (Yoshihara reacts to data which is gathered and analyzed while driving where the vehicle is driven according to a plan generated from previously gathered data. Examiner notes there is an inherent lag in time between when a turning signal of a preceding vehicle is turned off, when a sensor records that the signal was turned off, and when the sensed data is used to make a modification to the operation of the vehicle. As such, the system of Yoshihara would implicitly include a lag time. Furthermore, given the workflow of Yoshihara as set forth above, turning off the signal would not necessarily cause the system to determine that the traveling pattern had changed from OR3 and/or OR1 to OR2 as depicted in FIG. 14. For example, the information provided from each deriver is weighted such that some information is given more consideration in identifying which travelling pattern is occurring. See para. [0118] which states “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels… For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions. For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Para. [0106] states “[t]he weight associated with the event in which the blinking state of the lamp of the direction indicator and the behavior of the other vehicle are inconsistent as in No. 211 and No. 214 in FIG. 9 is less than a weight associated with the event in which the direction indicator has simply blinked as in No. 201 or No. 202 in FIG. 9.” As such, Yoshihara performs the recited limitation because turning off the signal (e.g., second deriver) would not cause the vehicle to change the previous plan created by the action generator where the first deriver is weighted more heavily.). Regarding claim 2 , Yoshihara discloses wherein the predetermined period of time is a first point in time at which the execution of the acceleration suppression process began to a second point in time (para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0118], “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels… For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions. For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .”; and para. [0128], “[t]he action determiner 140 determines an action of the vehicle M … when an integrated index corresponding to the traveling pattern of the trajectory OR1 is greater than the integrated indices of the traveling patterns of the other trajectories , the reflection rate of the behavior of the vehicle according to the traveling pattern of the trajectory OR1 becomes greater than the reflection rates of the behaviors of the vehicle according to the traveling patterns of the other trajectories. Because it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1 , the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.” The action plan generator creates a trajectory of travel points for the own vehicle where each travel point has an associated scheduled time such that each acceleration or deceleration action includes a predetermined period of time for the action to be performed where each period includes a first point in time when the action began.). Regarding claim 4 , Yoshihara discloses [a] driving assistance method comprising: an information acquisition step (see S102 of FIG. 18; acquire information for use in process where the process is depicted in FIG. 3) of acquiring information (para. [0007], “ a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle ”; para. [0045], “the vehicle system 1 includes a camera 10, a radar device 12, a light detection and ranging (LIDAR) sensor 14 , a physical object recognition device 16, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40 , a navigation device 50, a map positioning unit (MPU) 60, driving operating elements 80, an automated driving control device 100, a travel driving force output device 200, a brake device 210, and a steering device 220.”; para. [0059], “[t]he recognizer 122 recognizes states of a position, a velocity, acceleration, and the like of a physical object around the vehicle M on the basis of information input from the camera 10, the radar device 12, and the LIDAR sensor 14 via the physical object recognition device 16 .”) related to an own vehicle (vehicle sensor 40; para. [0052], “[t]he vehicle sensor 40 includes a vehicle speed sensor configured to detect the speed of the vehicle M, an acceleration sensor configured to detect acceleration, a yaw rate sensor configured to detect angular velocity around a vertical axis, a direction sensor configured to detect a direction of the vehicle M, and the like.”) and information related to a preceding vehicle (camera 10, see para. [0046]; radar device 12, see para. [0047]; and LIDAR sensor 14, see para. [0048]) traveling ahead of the own vehicle (see FIG. 14) ; and a speed adjustment step of adjusting the speed of the own vehicle (the output from the action plan generator 126 workflow as depicted in FIG. 3 is an action generated by the action determiner 140 where the actions include accelerating and decelerating; para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .” The first and second scene would be associated, respectively, with the trajectories OR3 and OR1 as depicted in FIG. 14; para. [0128], “[b]ecause it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1, the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) by controlling a drive device and/or a braking device of the own vehicle (para. [0056], “driving operating elements 80 include an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a steering wheel variant, a joystick , and other operators. A sensor configured to detect an amount of operation or the presence or absence of an operation is attached to the driving operating element 80, and a detection result thereof is output to the automated driving control device 100 or some or all of the travel driving force output device 200, the brake device 210, and the steering device 220.”) based on the information related to the own vehicle and/or the information related to the preceding vehicle (para. [0128], “ [t]he action determiner 140 determines an action of the vehicle M on the basis of an estimation result of the estimator 138. The action determiner 140 integrates behaviors of the vehicle according to the behaviors of the other vehicle which are assumed on the basis of the predicted probability and controls the behavior of the vehicle on the basis of the behaviors of the vehicle after the integration.”) , wherein: in a first specific scene, (i) the own vehicle and the preceding vehicle are traveling on the leftmost lane or a left-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 107 and 109 which are directed to “left turn only lane” and “go straight/left turn only lane” respectively; para. [0086], “[l]eft turn only lane; Case that the next base path is within a predetermined distance from the base path along which the vehicle travels and the vehicle is traveling in a lane in which a “left turn” is associated with the attribute of the base path in front of the vehicle (see No. 107 in FIGS. 4 and 6). In this case, for example , the probability is higher in the order of the left turn direction probability , the straight direction probability, and the right turn direction probability.) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road ) , where and (ii) the turn signal of the preceding vehicle indicates a left turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability, the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability , the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking .” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle , and the state model 176 (step S110).”) ; and in a second specific scene, where (i) the own vehicle and the preceding vehicle are traveling on the rightmost lane or a right-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 106 and 108 which are directed to “right turn only lane” and “go straight/right turn only lane” respectively; para. [0084], “[r]ight turn only lane… the vehicle is traveling in a lane in which a ‘right turn’ is associated with an attribute of the base path in front of the vehicle (see No. 106 in FIGS. 4 and 6). In this case, for example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability.”) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road) , where and (ii) the turn signal of the preceding vehicle indicates a right turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability, the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking.” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle, and the state model 176 (step S110).”) , determine that the first specific scene or the second specific scene occurs (the limitations of the first scene and second scene are taken as inputs, by way of first deriver 130, second deriver 132, and third deriver 134, to the action generator 126 as depicted in FIG. 3. Yoshihara describes multiple scene configurations as provided by FIG. 4, 8, 9, and 11 which include configurations which read on the first and second scene as provided above. See para. [0072] for a high-level description of the workflow of action generator 126.) , based upon the determination that the first specific scene or the second specific scene occurs, execute, as the speed adjustment process (the output from the action plan generator 126 workflow as depicted in FIG. 3 is an action generated by the action determiner 140 where the actions include accelerating and decelerating; para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0118], “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels. The characteristics of the road include a road structure or a combination of a location (a position) of the road and a structure of the road, the location of the road, restrictions (for example, laws and regulations) on the road on which the other vehicle is traveling, and the like. For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions . For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Examiner notes that at least some embodiments of the first and second scene (e.g., vehicles located in a turning lane) would be associated, respectively, with the trajectories OR3 and OR1 as depicted in FIG. 14; para. [0128], “ it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1, the action determiner 140 causes the vehicle M to decelerate, for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) : a deceleration process that controls the drive device and/or the braking device so that the own vehicle decelerates at a predetermined deceleration rate (see citation above to para. [0064]—[0065], para. [0118], and para. [0128]) , wherein the predetermined deceleration rate is smaller than a deceleration rate used in a vehicle-following process, wherein the vehicle-following process adjusts the speed of the own vehicle so that a distance between the own vehicle and the preceding vehicle matches a target value in scenes other than when the first specific scene or the second specific scene occurs (para. [0137], “the automated driving control device 100 can improve the ride comfort of the vehicle M of the occupant by controlling the vehicle M so that the change in the acceleration of the vehicle M does not become large without limiting the behavior of the other vehicle m to one type of behavior. For example, the automated driving control device 100 predicts the behavior of the other vehicle m and controls the vehicle M in advance on the basis of a prediction result , thereby restricting the acceleration or deceleration of the vehicle M from becoming higher than or equal to a predetermined value . Because the automated driving control device 100 can determine a degree of the above-described restriction on the basis of a position of the other vehicle m after a predetermined time when the prediction has been performed, smoother control of the vehicle M can be implemented.” Examiner notes that the own car which is following the preceding car does not merely match the speed of the preceding car but predicts a trajectory of the preceding car which may further restrict the acceleration of the own car in order to reduce large changes in acceleration) ; or an acceleration suppression process that controls the drive device (driving force output device 200) and/or the braking device (braking device 210) so that the acceleration of the own vehicle is suppressed (para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200 , the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0128], “[t]he action determiner 140 determines an action of the vehicle M on the basis of an estimation result of the estimator 138. The action determiner 140 integrates behaviors of the vehicle according to the behaviors of the other vehicle which are assumed on the basis of the predicted probability and controls the behavior of the vehicle on the basis of the behaviors of the vehicle after the integration. For example, the action determiner 140 determines a velocity, acceleration, and a position of the vehicle on the basis of a position of another vehicle at each time . For example, the action determiner 140 derives the behavior of the vehicle for each traveling pattern in which the other vehicle travels on the basis of the trajectories OR1 to OR3. The action determiner 140 derives the behavior of the vehicle on the basis of an integrated index corresponding to the traveling pattern . For example, the action determiner 140 derives a reflection rate for the behavior of the vehicle on the basis of the integrated index for each traveling pattern and further determines the behavior of the vehicle on the basis of the derived reflection rate . For example, when an integrated index corresponding to the traveling pattern of the trajectory OR1 is greater than the integrated indices of the traveling patterns of the other trajectories , the reflection rate of the behavior of the vehicle according to the traveling pattern of the trajectory OR1 becomes greater than the reflection rates of the behaviors of the vehicle according to the traveling patterns of the other trajectories. Because it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1 , the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) , and when an operation of the turn signal of the preceding vehicle is stopped while the deceleration process or the acceleration suppression process is being executed, execution of the deceleration process or the acceleration suppression process is continued for a predetermined period of time (Yoshihara reacts to data which is gathered and analyzed while driving where the vehicle is driven according to a plan generated from previously gathered data. Examiner notes there is an inherent lag in time between when a turning signal of a preceding vehicle is turned off, when a sensor records that the signal was turned off, and when the sensed data is used to make a modification to the operation of the vehicle. As such, the system of Yoshihara would implicitly include a lag time. Furthermore, given the workflow of Yoshihara as set forth above, turning off the signal would not necessarily cause the system to determine that the traveling pattern had changed from OR3 and/or OR1 to OR2 as depicted in FIG. 14. For example, the information provided from each deriver is weighted such that some information is given more consideration in identifying which travelling pattern is occurring. See para. [0118] which states “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels… For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions. For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Para. [0106] states “[t]he weight associated with the event in which the blinking state of the lamp of the direction indicator and the behavior of the other vehicle are inconsistent as in No. 211 and No. 214 in FIG. 9 is less than a weight associated with the event in which the direction indicator has simply blinked as in No. 201 or No. 202 in FIG. 9.” As such, Yoshihara performs the recited limitation because turning off the signal (e.g., second deriver) would not cause the vehicle to change the previous plan created by the action generator where the first deriver is weighted more heavily.). Regarding claim 5 , Yoshihara discloses [a] storage medium storing a driving assistance program (para. [0148], “the automated driving control device 100 has a configuration in which a communication controller 100-1, a CPU 100-2, a RAM 100-3 used as a working memory, a ROM 100-4 storing a boot program and the like, a storage device 100-5 such as a flash memory or an HDD , a drive device 100-6, and the like are mutually connected by an internal bus or a dedicated communication line. The communication controller 100-1 communicates with components other than the automated driving control device 100. The storage device 100-5 stores a program 100-5 a to be executed by the CPU 100-2 . This program is loaded into the RAM 100-3 by a direct memory access (DMA) controller (not shown) or the like and executed by the CPU 100-2. Thereby, some or all of the recognizer 122, the information manager 124, and the action plan generator 126 are implemented.”) that causes a computer provided in the own vehicle to execute: an information acquisition step (see S102 of FIG. 18; acquire information for use in process where the process is depicted in FIG. 3) of acquiring information (para. [0007], “ a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle ”; para. [0045], “the vehicle system 1 includes a camera 10, a radar device 12, a light detection and ranging (LIDAR) sensor 14 , a physical object recognition device 16, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40 , a navigation device 50, a map positioning unit (MPU) 60, driving operating elements 80, an automated driving control device 100, a travel driving force output device 200, a brake device 210, and a steering device 220.”; para. [0059], “[t]he recognizer 122 recognizes states of a position, a velocity, acceleration, and the like of a physical object around the vehicle M on the basis of information input from the camera 10, the radar device 12, and the LIDAR sensor 14 via the physical object recognition device 16 .”) related to an own vehicle (vehicle sensor 40; para. [0052], “[t]he vehicle sensor 40 includes a vehicle speed sensor configured to detect the speed of the vehicle M, an acceleration sensor configured to detect acceleration, a yaw rate sensor configured to detect angular velocity around a vertical axis, a direction sensor configured to detect a direction of the vehicle M, and the like.”) and information related to a preceding vehicle (camera 10, see para. [0046]; radar device 12, see para. [0047]; and LIDAR sensor 14, see para. [0048]) traveling ahead of the own vehicle (see FIG. 14) ; and a speed adjustment step of adjusting the speed of the own vehicle (the output from the action plan generator 126 workflow as depicted in FIG. 3 is an action generated by the action determiner 140 where the actions include accelerating and decelerating; para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .” The first and second scene would be associated, respectively, with the trajectories OR3 and OR1 as depicted in FIG. 14; para. [0128], “[b]ecause it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1, the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) by controlling a drive device and/or a braking device of the own vehicle (para. [0056], “driving operating elements 80 include an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a steering wheel variant, a joystick , and other operators. A sensor configured to detect an amount of operation or the presence or absence of an operation is attached to the driving operating element 80, and a detection result thereof is output to the automated driving control device 100 or some or all of the travel driving force output device 200, the brake device 210, and the steering device 220.”) based on the information related to the own vehicle and/or the information related to the preceding vehicle (para. [0128], “ [t]he action determiner 140 determines an action of the vehicle M on the basis of an estimation result of the estimator 138. The action determiner 140 integrates behaviors of the vehicle according to the behaviors of the other vehicle which are assumed on the basis of the predicted probability and controls the behavior of the vehicle on the basis of the behaviors of the vehicle after the integration.”) , wherein: in a first specific scene, (i) the own vehicle and the preceding vehicle are traveling on the leftmost lane or a left-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 107 and 109 which are directed to “left turn only lane” and “go straight/left turn only lane” respectively; para. [0086], “[l]eft turn only lane; Case that the next base path is within a predetermined distance from the base path along which the vehicle travels and the vehicle is traveling in a lane in which a “left turn” is associated with the attribute of the base path in front of the vehicle (see No. 107 in FIGS. 4 and 6). In this case, for example , the probability is higher in the order of the left turn direction probability , the straight direction probability, and the right turn direction probability.) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road ) , where and (ii) the turn signal of the preceding vehicle indicates a left turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability, the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability , the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking .” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle , and the state model 176 (step S110).”) ; and in a second specific scene, where (i) the own vehicle and the preceding vehicle are traveling on the rightmost lane or a right-turn lane of a general road composed of multiple lanes (para. [0074], “[t]he first deriver 130 derives the first index (a probability) on the basis of a structure of a road on which another vehicle is present . The first deriver 130 derives the first index on the basis of, for example, the structure of the road recognized by the recognizer 122 and the structural information 172 . When the map information (the second map information 62) is associated with information indicating the structure of the road, the first deriver 130 may estimate a position where the other vehicle is present and identify the structure of the road associated with the estimated position in the map information.”; FIG. 4 provides various embodiments of structural information 172 including No. 106 and 108 which are directed to “right turn only lane” and “go straight/right turn only lane” respectively; para. [0084], “[r]ight turn only lane… the vehicle is traveling in a lane in which a ‘right turn’ is associated with an attribute of the base path in front of the vehicle (see No. 106 in FIGS. 4 and 6). In this case, for example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability.”) , or on a single-lane general road (FIG. 4 provides various embodiments of structural information 172 including No. 101 which is directed to a single lane road) , where and (ii) the turn signal of the preceding vehicle indicates a right turn (para. [0094], “[t]he second deriver 132 derives a second index (a probability) on the basis of the explicit action of the other vehicle . The explicit action is, for example, a direction indicated by the direction indicator or an ON state of a brake lamp. The explicit action may be an explicit action indicated by the vehicle or the occupant of the vehicle in addition to the direction indicated by the direction indicator and may be, for example, a gesture explicitly indicated by the occupant.”; para. [0096], “[t]he second deriver 132 identifies an event (content of the event) of the state information 174 that matches the explicit action of the other vehicle recognized by the recognizer 122 and derives a probability and a weight associated with the identified event . For example, the probability is higher in the order of the right turn direction probability , the straight direction probability, and the left turn direction probability when the lamp of the right direction indicator is blinking and the probability is higher in the order of the left turn direction probability, the straight direction probability, and the right turn direction probability when the lamp of the left direction indicator is blinking.” See FIG. 8 and 9 which provides various signaling scenarios) , and (iii) the preceding vehicle is not decelerating (para. [0109], “[t]he third deriver 134 derives a third index (a probability) on the basis of the implicit action implicitly indicated by the other vehicle . The implicit action is, for example, an action different from an action in which the vehicle explicitly indicates a destination and is, for example, an action indicating the traveling state of the other vehicle. The implicit action is indicated by one or more information elements of a velocity of the other vehicle, acceleration of the other vehicle , and a position of the other vehicle. The position of the other vehicle is, for example, a position of the other vehicle with respect to the lane in which the other vehicle travels .”; para. [0144], “the third deriver 134 derives the third index on the basis of a velocity, acceleration, a position of the other vehicle, and the state model 176 (step S110).”) , determine that the first specific scene or the second specific scene occurs (the limitations of the first scene and second scene are taken as inputs, by way of first deriver 130, second deriver 132, and third deriver 134, to the action generator 126 as depicted in FIG. 3. Yoshihara describes multiple scene configurations as provided by FIG. 4, 8, 9, and 11 which include configurations which read on the first and second scene as provided above. See para. [0072] for a high-level description of the workflow of action generator 126.) , based upon the determination that the first specific scene or the second specific scene occurs, execute, as the speed adjustment process (the output from the action plan generator 126 workflow as depicted in FIG. 3 is an action generated by the action determiner 140 where the actions include accelerating and decelerating; para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200, the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0118], “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels. The characteristics of the road include a road structure or a combination of a location (a position) of the road and a structure of the road, the location of the road, restrictions (for example, laws and regulations) on the road on which the other vehicle is traveling, and the like. For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions . For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Examiner notes that at least some embodiments of the first and second scene (e.g., vehicles located in a turning lane) would be associated, respectively, with the trajectories OR3 and OR1 as depicted in FIG. 14; para. [0128], “ it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1, the action determiner 140 causes the vehicle M to decelerate, for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) : a deceleration process that controls the drive device and/or the braking device so that the own vehicle decelerates at a predetermined deceleration rate (see citation above to para. [0064]—[0065], para. [0118], and para. [0128]) , wherein the predetermined deceleration rate is smaller than a deceleration rate used in a vehicle-following process, wherein the vehicle-following process adjusts the speed of the own vehicle so that a distance between the own vehicle and the preceding vehicle matches a target value in scenes other than when the first specific scene or the second specific scene occurs (para. [0137], “the automated driving control device 100 can improve the ride comfort of the vehicle M of the occupant by controlling the vehicle M so that the change in the acceleration of the vehicle M does not become large without limiting the behavior of the other vehicle m to one type of behavior. For example, the automated driving control device 100 predicts the behavior of the other vehicle m and controls the vehicle M in advance on the basis of a prediction result , thereby restricting the acceleration or deceleration of the vehicle M from becoming higher than or equal to a predetermined value . Because the automated driving control device 100 can determine a degree of the above-described restriction on the basis of a position of the other vehicle m after a predetermined time when the prediction has been performed, smoother control of the vehicle M can be implemented.” Examiner notes that the own car which is following the preceding car does not merely match the speed of the preceding car but predicts a trajectory of the preceding car which may further restrict the acceleration of the own car in order to reduce large changes in acceleration) ; or an acceleration suppression process that controls the drive device (driving force output device 200) and/or the braking device (braking device 210) so that the acceleration of the own vehicle is suppressed (para. [0064]—[0065], “ The action plan generator 126 generates a target trajectory according to an activated event. Details of the action plan generator 126 will be described below. The second controller 160 controls the travel driving force output device 200 , the brake device 210 , and the steering device 220 so that the vehicle M passes through the target trajectory generated by the action plan generator 126 at a scheduled time .”; para. [0128], “[t]he action determiner 140 determines an action of the vehicle M on the basis of an estimation result of the estimator 138. The action determiner 140 integrates behaviors of the vehicle according to the behaviors of the other vehicle which are assumed on the basis of the predicted probability and controls the behavior of the vehicle on the basis of the behaviors of the vehicle after the integration. For example, the action determiner 140 determines a velocity, acceleration, and a position of the vehicle on the basis of a position of another vehicle at each time . For example, the action determiner 140 derives the behavior of the vehicle for each traveling pattern in which the other vehicle travels on the basis of the trajectories OR1 to OR3. The action determiner 140 derives the behavior of the vehicle on the basis of an integrated index corresponding to the traveling pattern . For example, the action determiner 140 derives a reflection rate for the behavior of the vehicle on the basis of the integrated index for each traveling pattern and further determines the behavior of the vehicle on the basis of the derived reflection rate . For example, when an integrated index corresponding to the traveling pattern of the trajectory OR1 is greater than the integrated indices of the traveling patterns of the other trajectories , the reflection rate of the behavior of the vehicle according to the traveling pattern of the trajectory OR1 becomes greater than the reflection rates of the behaviors of the vehicle according to the traveling patterns of the other trajectories. Because it is predicted that the vehicle will decelerate in front of the intersection when the other vehicle has a traveling pattern of the trajectory OR1 , the action determiner 140 causes the vehicle M to decelerate , for example, so that an inter-vehicle distance between the other vehicle and the vehicle is not less than or equal to a threshold value.”) , and when an operation of the turn signal of the preceding vehicle is stopped while the deceleration process or the acceleration suppression process is being executed, execution of the deceleration process or the acceleration suppression process is continued for a predetermined period of time (Yoshihara reacts to data which is gathered and analyzed while driving where the vehicle is driven according to a plan generated from previously gathered data. Examiner notes there is an inherent lag in time between when a turning signal of a preceding vehicle is turned off, when a sensor records that the signal was turned off, and when the sensed data is used to make a modification to the operation of the vehicle. As such, the system of Yoshihara would implicitly include a lag time. Furthermore, given the workflow of Yoshihara as set forth above, turning off the signal would not necessarily cause the system to determine that the traveling pattern had changed from OR3 and/or OR1 to OR2 as depicted in FIG. 14. For example, the information provided from each deriver is weighted such that some information is given more consideration in identifying which travelling pattern is occurring. See para. [0118] which states “ the index deriver 136 may set weights of the first to third indices on the basis of characteristics of a road on which another vehicle travels… For example, the index deriver 136 may set the weight of the first index greater than the weights of the other indices when there are restrictions on the road and it is necessary for the other vehicle to travel in compliance with the restrictions. For example, this is because, when the other vehicle is traveling in a left turn only lane, the probability that the other vehicle will turn left is high regardless of the second index or the third index .” Para. [0106] states “[t]he weight associated with the event in which the blinking state of the lamp of the direction indicator and the behavior of the other vehicle are inconsistent as in No. 211 and No. 214 in FIG. 9 is less than a weight associated with the event in which the direction indicator has simply blinked as in No. 201 or No. 202 in FIG. 9.” As such, Yoshihara performs the recited limitation because turning off the signal (e.g., second deriver) would not cause the vehicle to change the previous plan created by the action generator where the first deriver is weighted more heavily.). Conclusion 07-40 AIA 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 URSULA NORRIS whose telephone number is (703)756-4731. The examiner can normally be reached Monday to Friday, 7 AM to 4 PM. 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, TARA SCHIMPF can be reached at 571-270-7741. 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. /U.L.N./Examiner, Art Unit 3676 /TARA SCHIMPF/Supervisory Patent Examiner, Art Unit 3676 Application/Control Number: 18/960,673 Page 2 Art Unit: 3676 Application/Control Number: 18/960,673 Page 3 Art Unit: 3676 Application/Control Number: 18/960,673 Page 4 Art Unit: 3676 Application/Control Number: 18/960,673 Page 5 Art Unit: 3676 Application/Control Number: 18/960,673 Page 6 Art Unit: 3676 Application/Control Number: 18/960,673 Page 7 Art Unit: 3676 Application/Control Number: 18/960,673 Page 8 Art Unit: 3676 Application/Control Number: 18/960,673 Page 9 Art Unit: 3676 Application/Control Number: 18/960,673 Page 10 Art Unit: 3676 Application/Control Number: 18/960,673 Page 11 Art Unit: 3676 Application/Control Number: 18/960,673 Page 12 Art Unit: 3676 Application/Control Number: 18/960,673 Page 13 Art Unit: 3676 Application/Control Number: 18/960,673 Page 14 Art Unit: 3676 Application/Control Number: 18/960,673 Page 15 Art Unit: 3676 Application/Control Number: 18/960,673 Page 16 Art Unit: 3676 Application/Control Number: 18/960,673 Page 17 Art Unit: 3676 Application/Control Number: 18/960,673 Page 18 Art Unit: 3676 Application/Control Number: 18/960,673 Page 19 Art Unit: 3676 Application/Control Number: 18/960,673 Page 20 Art Unit: 3676 Application/Control Number: 18/960,673 Page 21 Art Unit: 3676 Application/Control Number: 18/960,673 Page 22 Art Unit: 3676 Application/Control Number: 18/960,673 Page 23 Art Unit: 3676 Application/Control Number: 18/960,673 Page 24 Art Unit: 3676 Application/Control Number: 18/960,673 Page 25 Art Unit: 3676 Application/Control Number: 18/960,673 Page 26 Art Unit: 3676 Application/Control Number: 18/960,673 Page 27 Art Unit: 3676 Application/Control Number: 18/960,673 Page 28 Art Unit: 3676 Application/Control Number: 18/960,673 Page 29 Art Unit: 3676 Application/Control Number: 18/960,673 Page 30 Art Unit: 3676 Application/Control Number: 18/960,673 Page 31 Art Unit: 3676 Application/Control Number: 18/960,673 Page 32 Art Unit: 3676 Application/Control Number: 18/960,673 Page 33 Art Unit: 3676 Application/Control Number: 18/960,673 Page 34 Art Unit: 3676
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