-1Notice 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 .
Information Disclosure Statement
The information disclosure statement submitted on 10/22/2025 have been considered by the
Examiner and made of record in the application.
Response to Amendments
Claims 1, 6, and 11 are amended.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 6, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kumano et al. (US 11,667,281 B2) in view of Floor (US 2023/0339505 Al) and in further view of Kessler (US 12,286,099 B2).
Regarding Claim 1 Kumano teaches A computer-implemented method of generating a destination for an emergency response of an autonomous vehicle, comprising at least one processor and memory (Pg. 1 – Abstract – “A vehicle control method includes recognizing an object, generating a target trajectory of a vehicle, and automatically controlling driving of the vehicle on the basis of the target trajectory, calculating a region between a first virtual line, which passes through a reference point using the vehicle as a reference and a first point present in the vicinity of an outer edge of the object, and a second virtual line, which passes through the reference point and a second point present in the vicinity of the outer edge of the object, as a region through which the vehicle should avoid to travel” & See Also Pg. 18 – [Col. 2 – lines 47-51] – “A ninth aspect is a program is provided to execute a computer mounted on a vehicle to: recognize an object present around the vehicle; generate one or a plurality of 50 target trajectories along which the vehicle is to travel on the basis of the recognized object;” & See Also Pg. 20 – [Col. 6 – lines 24 - 26] – “are realized by executing a program (software) using a hardware processor such as a central processing unit (CPU)…. a flash memory” (equates to A computer-implemented method of generating a destination for an emergency response of an autonomous vehicle, comprising at least one processor and memory as the quote shows the vehicle detecting an object and thus an emergency is identified wherein a route is generated to avoid said emergency via a trajectory where a destination is contained within. Second and third quote show the method being computer implemented wherein a processor executes the method steps and similarly comprises a memory. )) the method comprising: generating forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle; (Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.” (equates to the method comprising: generating forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle; as the quote shows the front side or forward direction of the vehicle being sensed via a camera and the forward perception information is generated via a sensor fusion result including the camera data as seen from the second quote.)) setting a destination generation area based on the forward perception information; (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to setting a destination generation area based on the forward perception information as the figures and combination of quotes show how the previously mapped forward perception information is used to generate a risk area and by inputting a risk area in a deep neural network one can extrapolate the trajectory needed for a vehicle is mitigate any obstacle detected and thus seen by fig. 8 and the trajectory generated a destination are ais generated to be able to move the vehicle through the risk area. )) generating a candidate destination in the destination generation area based on the forward perception (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to generating a candidate destination in the destination generation area based on the forward perception information and a current heading range of the autonomous vehicle; as the figure 10 and the last quote show how a trajectory is generated via a risk area assessment and thus a candidate destination is generated based on the forward perception. )) and when the candidate destination is provided as a plurality of candidate destinations, (Pg. 13 – Fig. 14 & See Also Pg. 25 – Col. 16 – lines 1-3 – “That is, as shown, the total four target trajectories TR referred to as TRl, TR2, TR3 and TR4 are generated.” (equates to and when the candidate destination is provided as a plurality of candidate destinations as the quote and figure shows trajectories being generated that have different candidate destinations.)) selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. (Pg. 4 – fig. 3 & See Also Pg. 6 – Fig. 7 & See Also Pg. 13 – Fig. 14 & See Also Pg. 22 – Col. 10 – lines 10 – 18 – “showing a variation in the risk potential p in the X direction at a certain coordinate y4. The coordinate y4 is intermediate coordinates between yl and y2, and the preceding vehicle ml is present at the coordinate y4. For this reason, the risk potential p is highest at the coordinates 15 (x3, y4), the risk potential p at the coordinates (x2, y4) farther from the preceding vehicle ml than the coordinates (x3, y4) is lower than the risk potential at the coordinates (x3, y4),” & See Also Pg. 23 – Col. 12 – lines 19-22 – “generates one or a plurality of target trajectories TR on the basis of the output result of the DNN models MDLl to which the risk region RA is input.” & See Also Pg. 10 – Fig. 11 – s110 – “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” (equates to selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. As the figures shows the maximum vertical distance the vehicle would travel within a destination area and then assigns risk value to each of the locations. The trajectory that is then selected is based on the maximum vertical distance the vehicle travels within the destination area and its associated risk value.)) wherein the autonomous vehicle maneuvers to the selected destination. (Pg. 23 – [Col. 12 – lines 44 – 49 ] – “Returning to the description of FIG. 2, the second controller 160 controls the traveling driving power output device 200, the brake device 210, and the steering device 220 such that the host vehicle M passes through the target trajectory TR generated by the target trajectory generating part 146 on time as scheduled.” (equates to wherein the autonomous vehicle maneuvers to the selected destination as the quote shows a control of the autonomous vehicle based along a given trajectory wherein the trajectory necessarily comprises a selected destination.))
Yet Kumano fails to teach generating a maximum deceleration value and a current heading range of the autonomous vehicle; and the maximum deceleration value and the current heading range of the autonomous vehicle.
Floor teaches and a current heading range of the autonomous vehicle; (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” & See Also Pg. 15 – [0024] – “For autonomous vehicles, navigating the autonomous vehicle in confined spaces may be a complex task” (equates to and a current heading range of the autonomous vehicle as the quote shows a range of heading values to which the vehicle is constrained to travelling within and the second quote showing the autonomous driving capabilities of the vehicle)) information and a current heading range of the autonomous vehicle; (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” )
Yet both Kumano- Floor fail to teach generating a maximum deceleration value; the maximum deceleration value.
Kessler teaches generating a maximum deceleration value (Pg. 11 – [Col. 1 – lines 36 – 44 & 55- 57] – “A method of decelerating a plurality of vehicles along a roadway may include, at a first vehicle, receiving, from an adjacent downstream vehicle, a first braking initiation signal and a first deceleration value indicating a deceleration rate of the adjacent downstream vehicle, determining a first distance to the adjacent downstream vehicle, and determining, based at least in part on the first distance, a second deceleration value configured to prevent the first vehicle from colliding with the adjacent downstream vehicle… The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding” (equates to generating a maximum deceleration value. As the quote shows a range of decelerations generated to ensure the vehicle does not collide with the detected surrounding vehicles wherein the end of the quote shows how a band of the range of generated values includes a maximum generated deceleration value. ) ) the maximum deceleration value. (Pg. 11 – col. 1 – lines 55-57 – “The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding”” ) It would have been an advantageous addition to the method disclosed by Kumano – Floor to include generating a maximum deceleration value; the maximum deceleration value as this allows for a more robust trajectory generation system to be actualized wherein collision prevention is further prevented based on calculation of a maximum braking application and thus a maximum deceleration that can be had for safe passage of the host vehicle.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include generating a maximum deceleration value; the maximum deceleration value as this allows for a safe passage to be had for the host vehicle based on the vehicles deceleration capabilities.
Regarding Claim 3 Kumano- Floor -Kessler teaches (Kumano teaches the following limitations: ) The method of claim 1, further comprising: perceiving an object in front of the autonomous vehicle based on the forward perception information (Pg. 21 – Col. 7 – lines 2-6 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes, for example, a bicycle, an motorcycle, a four-wheeled automobile, a pedestrian” & See Also Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” (equates to perceiving an object in front of the autonomous vehicle based on the forward perception information as the recognition part 130 does the perceiving from the forward perception data of the camara and sensor fusion result.)) and determining a location and movement direction of the object; (Pg. 19 – Col. 4 – lines 37-45 – “The radar device 12 radiates radio waves such as millimeter waves or the like to surroundings of the host vehicle M, and simultaneously, detects the radio waves (reflected 40 waves) reflected by the object to detect a position (a distance and an azimuth) of at least the object. The radar device 12 is attached to an arbitrary place of the host vehicle M. The radar device 12 may detect a position and a speed of the object using a frequency modulated continuous wave (FM- 45 CW) method” (equates to and determining a location and movement direction of the object as the radar is seen to detect location and direction of the object, as well as, the speed of the object thus a movement direction is attained via the speed and direction detected. )) and setting a risk area based on the location and movement direction of the object and excluding the risk area from the destination generation area. (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to and setting a risk area based on the location and movement direction of the object and excluding the risk area from the destination generation area. as the first quote shows the incorporation of the radar data which is previously mapped to the location and movement direction information that is gathered. This information is used for a risk assessment for the vehicle and a trajectory is generated that is minimizing the risk and thus excluding the risk from the trajectory calculation. ))
Regarding Claim 4 Kumano-Floor-Kessler teaches (Kumano teaches the following limitations:) The method of claim 3, wherein, in the generating of the candidate destination, a point that is reached only after passing through the risk area is excluded from the candidate destination. (Pg. 13 – fig. 14 & See Also Pg. 25 – Col. 16 – lines 35 – 41 - “FIG. 14 is a view showing an example of the excluded target trajectory TR. In the example shown, in the four target trajectories TR, TRl is present inside the traveling avoidance region AAl and TR4 is present inside the traveling avoidance region AA3. In this case, the target trajectory generating part 146 excludes the target trajectory TRl and TR4.” (equates to in the generating of the candidate destination, a point that is reached only after passing through the risk area is excluded from the candidate destination as the quote shows the trajectories tr1 and tr4 going through a risk zone and these trajectories are excluded based on the fact the host vehicle would hit these surrounding vehicles if the trajectories were taken and thus the candidate destination that would be attained via taking either two trajectories is cancelled based on the deemed risk area.) )
Regarding Claim 6 Kumano teaches A system for generating a destination for an emergency response of an autonomous vehicle, (Pg. 1 – title – “VEHICLE CONTROL METHOD, VEHICLE CONTROL DEVICE, AND STORAGE MEDIUM” & See Also Pg. 1 – Abstract – “A vehicle control method includes recognizing an object, generating a target trajectory of a vehicle, and automatically controlling driving of the vehicle on the basis of the target trajectory, calculating a region between a first virtual line, which passes through a reference point using the vehicle as a reference and a first point present in the vicinity of an outer edge of the object, and a second virtual line, which passes through the reference point and a second point present in the vicinity of the outer edge of the object, as a region through which the vehicle should avoid to travel” (equates to A system for generating a destination for an emergency response of an autonomous vehicle as the quote shows the vehicle detecting an object and thus an emergency is identified wherein a route is generated to avoid said emergency via a trajectory where a destination is contained within. Title shows the art’s ability to act as system. )) the system comprising: a memory configured to store computer-readable instructions; (Pg. 20 – Col. 6 – lines 32-34 – “The program may be previously stored in a storage device (a storage device including a non-transient storage medium) such as an HDD, a flash memory” (equates to the system comprising: a memory configured to store computer-readable instructions as the quote shows a program which is cited through the office action which can be contained in a memory. )) and at least one processor configured to execute the instructions, (Pg. 20 – Col. 6 – lines 23- 26 – “The first controller 120 and the second controller 160 are realized by executing a program (software) using a hardware processor such as a central processing unit (CPU),”) wherein the at least one processor is configured to execute the instructions to: receive from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle; ((Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.” (equates to receive from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle as the quote shows the front side or forward direction of the vehicle being sensed via a camera and the forward perception information is generated via a sensor fusion result including the camera data as seen from the second quote.))) generate forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle; (Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.” (equates to generate forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle; as the quote shows the front side or forward direction of the vehicle being sensed via a camera and the forward perception information is generated via a sensor fusion result including the camera data as seen from the second quote.)) set a destination generation area based on the forward perception information; (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to setting a destination generation area based on the forward perception information as the figures and combination of quotes show how the previously mapped forward perception information is used to generate a risk area and by inputting a risk area in a deep neural network one can extrapolate the trajectory needed for a vehicle is mitigate any obstacle detected and thus seen by fig. 8 and the trajectory generated a destination are ais generated to be able to move the vehicle through the risk area. )) generate a candidate destination in the destination generation area based on the forward perception information (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to generating a candidate destination in the destination generation area based on the forward perception information and a current heading range of the autonomous vehicle; as the figure 10 and the last quote show how a trajectory is generated via a risk area assessment and thus a candidate destination is generated based on the forward perception. )) and when the candidate destination is provided as a plurality of candidate destinations, (Pg. 13 – Fig. 14 & See Also Pg. 25 – Col. 16 – lines 1-3 – “That is, as shown, the total four target trajectories TR referred to as TRl, TR2, TR3 and TR4 are generated.” (equates to and when the candidate destination is provided as a plurality of candidate destinations as the quote and figure shows trajectories being generated that have different candidate destinations.)) select one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. (Pg. 4 – fig. 3 & See Also Pg. 6 – Fig. 7 & See Also Pg. 13 – Fig. 14 & See Also Pg. 22 – Col. 10 – lines 10 – 18 – “showing a variation in the risk potential p in the X direction at a certain coordinate y4. The coordinate y4 is intermediate coordinates between yl and y2, and the preceding vehicle ml is present at the coordinate y4. For this reason, the risk potential p is highest at the coordinates 15 (x3, y4), the risk potential p at the coordinates (x2, y4) farther from the preceding vehicle ml than the coordinates (x3, y4) is lower than the risk potential at the coordinates (x3, y4),” & See Also Pg. 23 – Col. 12 – lines 19-22 – “generates one or a plurality of target trajectories TR on the basis of the output result of the DNN models MDLl to which the risk region RA is input.” & See Also Pg. 10 – Fig. 11 – s110 – “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” (equates to selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. As the figures shows the maximum vertical distance the vehicle would travel within a destination area and then assigns risk value to each of the locations. The trajectory that is then selected is based on the maximum vertical distance the vehicle travels within the destination area and its associated risk value.)) wherein the autonomous vehicle maneuvers to the selected destination. (Pg. 23 – [Col. 12 – lines 44 – 49 ] – “Returning to the description of FIG. 2, the second controller 160 controls the traveling driving power output device 200, the brake device 210, and the steering device 220 such that the host vehicle M passes through the target trajectory TR generated by the target trajectory generating part 146 on time as scheduled.” (equates to wherein the autonomous vehicle maneuvers to the selected destination as the quote shows a control of the autonomous vehicle based along a given trajectory wherein the trajectory necessarily comprises a selected destination.))
Yet Kumano fails to teach generate a maximum deceleration value and a current heading range of the autonomous vehicle; the maximum deceleration value and the and the current heading range of the autonomous vehicle.
Floor teaches and a current heading range of the autonomous vehicle; (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” & See Also Pg. 15 – [0024] – “For autonomous vehicles, navigating the autonomous vehicle in confined spaces may be a complex task” (equates to and a current heading range of the autonomous vehicle as the quote shows a range of heading values to which the vehicle is constrained to travelling within and the second quote showing the autonomous driving capabilities of the vehicle)) information and a current heading range of the autonomous vehicle; (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” )
Yet both Kumano- Floor fail to teach generating a maximum deceleration value; the maximum deceleration value.
Kessler teaches generating a maximum deceleration value (Pg. 11 – [Col. 1 – lines 36 – 44 & 55- 57] – “A method of decelerating a plurality of vehicles along a roadway may include, at a first vehicle, receiving, from an adjacent downstream vehicle, a first braking initiation signal and a first deceleration value indicating a deceleration rate of the adjacent downstream vehicle, determining a first distance to the adjacent downstream vehicle, and determining, based at least in part on the first distance, a second deceleration value configured to prevent the first vehicle from colliding with the adjacent downstream vehicle… The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding” (equates to generating a maximum deceleration value. As the quote shows a range of decelerations generated to ensure the vehicle does not collide with the detected surrounding vehicles wherein the end of the quote shows how a band of the range of generated values includes a maximum generated deceleration value. ) ) the maximum deceleration value. (Pg. 11 – col. 1 – lines 55-57 – “The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding”” ) It would have been an advantageous addition to the method disclosed by Kumano – Floor to include generating a maximum deceleration value; the maximum deceleration value as this allows for a more robust trajectory generation system to be actualized wherein collision prevention is further prevented based on calculation of a maximum braking application and thus a maximum deceleration that can be had for safe passage of the host vehicle.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include generating a maximum deceleration value; the maximum deceleration value as this allows for a safe passage to be had for the host vehicle based on the vehicles deceleration capabilities.
Regarding Claim 8 Kumano- Floor-Kessler teaches (Kumano discloses the following limitations: )The system of claim 6, wherein the at least one processor perceives an object in front of the autonomous vehicle based on the forward perception information, (Pg. 27 – col. 19 – lines 22-24 – “A vehicle control device is configured to include… at least one processor…” & See Also Pg. 21 – Col. 7 – lines 2-6 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes, for example, a bicycle, an motorcycle, a four-wheeled automobile, a pedestrian” & See Also Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” (equates to wherein the at least one processor perceives an object in front of the autonomous vehicle based on the forward perception information as the first quote shows the device of the cited art containing a processor in which the recognition part 130 does the perceiving from the forward perception data of the camara and sensor fusion result.)) determines a location and movement direction of the object, (Pg. 19 – Col. 4 – lines 37-45 – “The radar device 12 radiates radio waves such as millimeter waves or the like to surroundings of the host vehicle M, and simultaneously, detects the radio waves (reflected 40 waves) reflected by the object to detect a position (a distance and an azimuth) of at least the object. The radar device 12 is attached to an arbitrary place of the host vehicle M. The radar device 12 may detect a position and a speed of the object using a frequency modulated continuous wave (FM- 45 CW) method” (equates to and determining a location and movement direction of the object as the radar is seen to detect location and direction of the object, as well as, the speed of the object thus a movement direction is attained via the speed and direction detected. )) sets a risk area based on the location and movement direction of the object, and excludes the risk area from the destination generation area. . (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates sets a risk area based on the location and movement direction of the object, and excludes the risk area from the destination generation area. as the first quote shows the incorporation of the radar data which is previously mapped to the location and movement direction information that is gathered. This information is used for a risk assessment for the vehicle and a trajectory is generated that is minimizing the risk and thus excluding the risk from the trajectory calculation. ))
Regarding Claim 9 Kumano- Floor-Kessler teaches (Kumano discloses the following:) The system of claim 8, wherein the at least one processor excludes a point that is reached only after passing through the risk area from the candidate destination. (Pg. 13 – fig. 14 & See Also Pg. 25 – Col. 16 – lines 35 – 41 - “FIG. 14 is a view showing an example of the excluded target trajectory TR. In the example shown, in the four target trajectories TR, TRl is present inside the traveling avoidance region AAl and TR4 is present inside the traveling avoidance region AA3. In this case, the target trajectory generating part 146 excludes the target trajectory TRl and TR4.” (equates to in the generating of the candidate destination, a point that is reached only after passing through the risk area is excluded from the candidate destination as the quote shows the trajectories tr1 and tr4 going through a risk zone and these trajectories are excluded based on the fact the host vehicle would hit these surrounding vehicles if the trajectories were taken and thus the candidate destination that would be attained via taking either two trajectories is cancelled based on the deemed risk area.) )
Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Kumano- Floor-Kessler as previously mapped and in further view of Park (KR102841665B)
Regarding Claim 2 Kumano- Floor-Kessler teaches (Kumano teaches the following limitations: ) The method of claim 1, from the forward perception information. (Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.”)
Yet Kumano- Floor fails to teach further comprising excluding lane information.
Park teaches further comprising excluding lane information (Pg. 20 – [0152] – “The topology data may be understood as data about road information from which information about a lane is excluded”). It would have been an advantageous addition to the method disclosed by Kumano- Floor to include further comprising excluding lane information as this allows the data to be taken in to solely rely upon the detection of objects and does not worry about the path the vehicle has to take in respect to the lane markers as the risk is avoided by simply detecting and maneuvering around it based on the destination decided.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include further comprising excluding lane information as this ensures the vehicle is only worried about traveling around a detected risk and not worrying about driving within a lane to do so, thus allowing more options to avoid a designated risk area.
Regarding Claim 7 Kumano-Floor-Kessler teaches (Kumano teaches the following limitations: ) The system of claim 6, wherein the at least one processor (Pg. 20 – Col. 6 – lines 24 -25 – “…are realized by executing a program (software) using a hardware processor…”) from the forward perception information. (Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.”)
Yet Kumano-Floor fails to teach excludes lane information
Park teaches excludes lane information (Pg. 20 – [0152] – “The topology data may be understood as data about road information from which information about a lane is excluded”). It would have been an advantageous addition to the method disclosed by Kumano- Floor to include excludes lane information as this allows the data to be taken in to solely rely upon the detection of objects and does not worry about the path the vehicle has to take in respect to the lane markers as the risk is avoided by simply detecting and maneuvering around it based on the destination decided.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include excludes lane information as this ensures the vehicle is only worried about traveling around a detected risk and not worrying about driving within a lane to do so, thus allowing more options to avoid a designated risk area.
Claims 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kumano- Floor-Kessler as previously mapped and in further view of SHARMA BANJADE (US 2022/0388505 Al)
Regarding Claim 5 Kumano- Floor-Kessler teaches (Kumano teaches the following limitations: ) The method of claim 1, wherein, in the selecting of the destination, (Pg. 10 – Fig. 11 – s110 “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” & See Also Pg. 25 – Col. 16 – lines 49-50 – “select the target trajectory TR with a higher evaluation as an optimal target trajectory TR” (equates to wherein, in the selecting of the destination, as the quote shows the trajectory being selected wherein a trajectory has a destination based on the path it specifies.)) when the candidate destination is provided as the plurality of candidate destinations, (Pg. 13 – Fig. 14 & See Also Pg. 25 – Col. 16 – lines 1-3 – “That is, as shown, the total four target trajectories TR referred to as TRl, TR2, TR3 and TR4 are generated.” (equates to and when the candidate destination is provided as a plurality of candidate destinations as the quote and figure shows trajectories being generated that have different candidate destinations.)) one destination is selected from among the candidate destinations (Pg. 10 – Fig. 11 – s110 “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” & See Also Pg. 25 – Col. 16 – lines 49-50 – “select the target trajectory TR with a higher evaluation as an optimal target trajectory TR”)
Yet Kumano-Floor fails to teach one destination is selected from among the candidate destinations based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations.
SHARMA BANJADE teaches based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations. (Pg. 14 – Fig. 12 & See Also Pg. 67 – [0362] – “For example, the use of the device (e.g., VRU device 117) navigation system, which provides assistance to the user (e.g., VRU 116) to select the best trajectory for reaching its planned destination” & See Also Pg. 41 – [0060] - “The LoD is the estimated distance of the VRU 116 from the ego-vehicle and VRU 116 along the direction of heading as shown by scenario 400a. The MSLoD is the minimum longitudinal separation of the VRU 116 from the ego-V-ITS-S 110 and VRU 116 for considered to be safe.” & See Also Pg. 42 – [0077] – “The dead reckoning module 822 is configurable or operable to determine or estimate the VRU 116 position, location, speed, heading/angular-direction (approach…” (equates to based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as the first quote shows the unit 116 being able to calculate a trajectory of the vehicle and the following quote shows the lateral distance between the vehicle and the risk assessed area to be defined by a longitudinal distance or a maximum vertical movement distance is maintained. And the last quote shows how the heading of the vehicle is calculated and thus both the heading and vertical distance calculated by the unit 116 can be used in the trajectory calculation or destination determination of the same unit. )) It would have been an advantageous addition to the system disclosed by Kumano-Floor to include based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as this limitation allows for additional vehicle metrics between itself and risk deemed area to be considered allowing for a safer destination to be considered as it is based on real time data of vehicular travel.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as more real time data of the vehicle allow for a more data driven approach to be considered in risk mitigation.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kumano- Floor-Kessler as previously mapped and in further view of SHARMA BANJADE (US-20220388505-A1)
Regarding Claim 10 Kumano-Floor-Kessler teaches (Kumano teaches the following limitations: ) The system of claim 6, wherein, when there are the plurality of candidate destinations, (Pg. 13 – Fig. 14 & See Also Pg. 25 – Col. 16 – lines 1-3 – “That is, as shown, the total four target trajectories TR referred to as TRl, TR2, TR3 and TR4 are generated.” (equates to and when the candidate destination is provided as a plurality of candidate destinations as the quote and figure shows trajectories being generated that have different candidate destinations.)) the at least one processor selects one destination from among the candidate destinations (Pg. 10 – Fig. 11 – s110 “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” & See Also Pg. 25 – Col. 16 – lines 49-50 – “select the target trajectory TR with a higher evaluation as an optimal target trajectory TR”)
Yet Kumano-Floor fails to teach based on the maximum vertical movement distance and a heading value maintained to reach each of the candidate destinations.
SHARMA BANJADE teaches based on the maximum vertical movement distance and a heading value maintained to reach each of the candidate destinations. (Pg. 14 – Fig. 12 & See Also Pg. 67 – [0362] – “For example, the use of the device (e.g., VRU device 117) navigation system, which provides assistance to the user (e.g., VRU 116) to select the best trajectory for reaching its planned destination” & See Also Pg. 41 – [0060] - “The LoD is the estimated distance of the VRU 116 from the ego-vehicle and VRU 116 along the direction of heading as shown by scenario 400a. The MSLoD is the minimum longitudinal separation of the VRU 116 from the ego-V-ITS-S 110 and VRU 116 for considered to be safe.” & See Also Pg. 42 – [0077] – “The dead reckoning module 822 is configurable or operable to determine or estimate the VRU 116 position, location, speed, heading/angular-direction (approach…” (equates to based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as the first quote shows the unit 116 being able to calculate a trajectory of the vehicle and the following quote shows the lateral distance between the vehicle and the risk assessed area to be defined by a longitudinal distance or a maximum vertical movement distance is maintained. And the last quote shows how the heading of the vehicle is calculated and thus both the heading and vertical distance calculated by the unit 116 can be used in the trajectory calculation or destination determination of the same unit. )) It would have been an advantageous addition to the system disclosed by Kumano-Floor to include based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as this limitation allows for additional vehicle metrics between itself and risk deemed area to be considered allowing for a safer destination to be considered as it is based on real time data of vehicular travel.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include based on a heading value and the maximum vertical movement distance maintained to reach each of the candidate destinations as more real time data of the vehicle allow for a more data driven approach to be considered in risk mitigation.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kumano- Floor- Kessler as previously mapped and in further view of Fausten (DE102023200871A1)
Regarding Claim 11 Kumano teaches An autonomous driving system for controlling an autonomous vehicle, (Pg. 19 – Col. 4 – line 5 – “The vehicle system 1 includes…” & See Also Pg. 19 – Col. 3 – lines 48-49 – “The vehicle control device of the embodiment is applied to, for example, an automatic traveling vehicle” & See Also Pg. 18 – [Col. 2 – lines 47-51] – “A ninth aspect is a program is provided to execute a computer mounted on a vehicle to: recognize an object present around the vehicle; generate one or a plurality of 50 target trajectories along which the vehicle is to travel on the basis of the recognized object;” & See Also Pg. 20 – [Col. 6 – lines 24 - 26] – “are realized by executing a program (software) using a hardware processor such as a central processing unit (CPU)…. a flash memory” (equates to An autonomous driving system for controlling an autonomous vehicle as the second quote demonstrates automatic driving via a control device of the cited art ) ) comprising a system comprising at least one processor and memory for generating a destination that generates the destination of the autonomous vehicle for an emergency response,( Pg. 1 – Abstract – “A vehicle control method includes recognizing an object, generating a target trajectory of a vehicle, and automatically controlling driving of the vehicle on the basis of the target trajectory, calculating a region between a first virtual line, which passes through a reference point using the vehicle as a reference and a first point present in the vicinity of an outer edge of the object, and a second virtual line, which passes through the reference point and a second point present in the vicinity of the outer edge of the object, as a region through which the vehicle should avoid to travel” (equates to comprising a system for generating a destination that generates the destination of the autonomous vehicle for an emergency response as the emergency response is denoted by the object in the road and the trajectory generated has an associated destination. )) the system for generating a destination receives from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle, ((Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.” (equates to receive from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle as the quote shows the front side or forward direction of the vehicle being sensed via a camera and the forward perception information is generated via a sensor fusion result including the camera data as seen from the second quote.))) generates forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle(Pg. 19 – Col. 1 – lines 25-27 – “M. For example, when a side in front of the host vehicle Mis imaged, the camera 10 is attached to an upper section of a front windshield” & See Also Pg. 19 – Col. 4 – lines 53-57 - “The object recognition device 16 recognizes a position, a type, a speed, or the like, of the object by performing sensor fusion processing with respect to the detection result by some or all of the camera 10, the radar device 12, and the LIDAR 14.” (equates to generates forward perception information based on the data collected from the at least one sensor mounted on the autonomous vehicle as the quote shows the front side or forward direction of the vehicle being sensed via a camera and the forward perception information is generated via a sensor fusion result including the camera data as seen from the second quote.)), sets a destination generation area based on the forward perception information, (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to setting a destination generation area based on the forward perception information as the figures and combination of quotes show how the previously mapped forward perception information is used to generate a risk area and by inputting a risk area in a deep neural network one can extrapolate the trajectory needed for a vehicle is mitigate any obstacle detected and thus seen by fig. 8 and the trajectory generated a destination are ais generated to be able to move the vehicle through the risk area. )) generates a candidate destination in the destination generation area based on the forward perception information (Pg. 7 – Fig. 8 & See Also Pg. 8 – Fig. 9 & See Also Pg. 9 – Fig. 10 & See Also Pg. 21 – Col. 8 – lines 40-43 – “The risk region calculating part 144 calculates a risk region potentially distributed or present around the object recognized by the recognition part 130 (hereinafter, referred to as a risk region RA).” & See Also Pg. 21 – col. 7 – lines 2-4 – “input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The object recognized by the recognition part 130 includes,” & See Also Pg. 23 – Col. 12 – lines 25-27 – “trajectory generating part 146 inputs a vector or a tensor representing the risk region RA to each of the plurality of DNN models MDL” & See Also Pg. 23 – col. 12 – lines 36-37 – “is a view showing an example of the target trajectory TR output from a certain DNN model MDLl” (equates to generating a candidate destination in the destination generation area based on the forward perception information and a current heading range of the autonomous vehicle; as the figure 10 and the last quote show how a trajectory is generated via a risk area assessment and thus a candidate destination is generated based on the forward perception. )) and when there are the plurality of candidate destinations, (Pg. 13 – Fig. 14 & See Also Pg. 25 – Col. 16 – lines 1-3 – “That is, as shown, the total four target trajectories TR referred to as TRl, TR2, TR3 and TR4 are generated.” (equates to and when the candidate destination is provided as a plurality of candidate destinations as the quote and figure shows trajectories being generated that have different candidate destinations.)) selects one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. (Pg. 4 – fig. 3 & See Also Pg. 6 – Fig. 7 & See Also Pg. 13 – Fig. 14 & See Also Pg. 22 – Col. 10 – lines 10 – 18 – “showing a variation in the risk potential p in the X direction at a certain coordinate y4. The coordinate y4 is intermediate coordinates between yl and y2, and the preceding vehicle ml is present at the coordinate y4. For this reason, the risk potential p is highest at the coordinates 15 (x3, y4), the risk potential p at the coordinates (x2, y4) farther from the preceding vehicle ml than the coordinates (x3, y4) is lower than the risk potential at the coordinates (x3, y4),” & See Also Pg. 23 – Col. 12 – lines 19-22 – “generates one or a plurality of target trajectories TR on the basis of the output result of the DNN models MDLl to which the risk region RA is input.” & See Also Pg. 10 – Fig. 11 – s110 – “SELECT OPTIMAL TARGET TRAJECTORY FROM REMAINING TARGET TRAJECTORIES” (equates to selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations. As the figures shows the maximum vertical distance the vehicle would travel within a destination area and then assigns risk value to each of the locations. The trajectory that is then selected is based on the maximum vertical distance the vehicle travels within the destination area and its associated risk value.)) wherein the autonomous vehicle maneuvers to the selected destination. (Pg. 23 – [Col. 12 – lines 44 – 49 ] – “Returning to the description of FIG. 2, the second controller 160 controls the traveling driving power output device 200, the brake device 210, and the steering device 220 such that the host vehicle M passes through the target trajectory TR generated by the target trajectory generating part 146 on time as scheduled.” (equates to wherein the autonomous vehicle maneuvers to the selected destination as the quote shows a control of the autonomous vehicle based along a given trajectory wherein the trajectory necessarily comprises a selected destination.))
Yet Kumano fails to teach generate a maximum deceleration value and a current heading range of the autonomous vehicle; the maximum deceleration value and a current heading range of the autonomous vehicle. wherein, when a failure or abnormal situation of the autonomous vehicle is detected, the autonomous driving system takes over a system state to a fallback state, and when the system state is taken over to the fallback state,
Floor teaches and a current heading range of the autonomous vehicle (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” & See Also Pg. 15 – [0024] – “For autonomous vehicles, navigating the autonomous vehicle in confined spaces may be a complex task” (equates to and a current heading range of the autonomous vehicle as the quote shows a range of heading values to which the vehicle is constrained to travelling within and the second quote showing the autonomous driving capabilities of the vehicle)) information and a current heading range of the autonomous vehicle; (Pg. 24 – [0093] – “constrains a vehicle heading to within a predetermined range of headings with respect to a given point on the reference path 704” )
Yet both Kumano- Floor fail to teach generating a maximum deceleration value; the maximum deceleration value. wherein, when a failure or abnormal situation of the autonomous vehicle is detected, the autonomous driving system takes over a system state to a fallback state, and when the system state is taken over to the fallback state,
Kessler teaches generating a maximum deceleration value (Pg. 11 – [Col. 1 – lines 36 – 44 & 55- 57] – “A method of decelerating a plurality of vehicles along a roadway may include, at a first vehicle, receiving, from an adjacent downstream vehicle, a first braking initiation signal and a first deceleration value indicating a deceleration rate of the adjacent downstream vehicle, determining a first distance to the adjacent downstream vehicle, and determining, based at least in part on the first distance, a second deceleration value configured to prevent the first vehicle from colliding with the adjacent downstream vehicle… The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding” (equates to generating a maximum deceleration value. As the quote shows a range of decelerations generated to ensure the vehicle does not collide with the detected surrounding vehicles wherein the end of the quote shows how a band of the range of generated values includes a maximum generated deceleration value. ) ) the maximum deceleration value. (Pg. 11 – col. 1 – lines 55-57 – “The upper deceleration value may correspond to a maximum deceleration value that the first vehicle can undergo without skidding””)
Yet all Kumano-Floor-Kessler fail to teach wherein, when a failure or abnormal situation of the autonomous vehicle is detected, the autonomous driving system takes over a system state to a fallback state, and when the system state is taken over to the fallback state,
Fausten teaches wherein, when a failure or abnormal situation of the autonomous vehicle is detected, (Pg. 2 – [0006] – “Detecting at least one safety-critical state based on monitoring the functional chain, whereby in the safety-critical state there may be an impairment of the functional chain” & See Also Pg. 2 – [0006] – “Monitoring a functional chain which provides the safety-relevant driving function” (equates to when a failure or abnormal situation of the autonomous vehicle is detected as the quote shows a detection of a function chain wherein the functional chain is described as a safety relevant driving function and maps to the failure or abnormal situation of the vehicle.)) the autonomous driving system takes over a system state to a fallback state, (Pg. 2 – [0006] – “Initiating the fallback function based on the detection and the determined residual capacity.” ) and when the system state is taken over to the fallback state, (Pg. 3 – [0008] – “if it monitors the state of the vehicle, the communication and the external system and initiates the fallback function upon detection” ) It would have been an advantageous addition to the system disclosed by Kumano-Floor to include wherein, when a failure or abnormal situation of the autonomous vehicle is detected, the autonomous driving system takes over a system state to a fallback state, and when the system state is taken over to the fallback state, as these limitations allow for a specifically mode to be activated when a risk is detected and thus ensure the process later described only occurs when the mode is initiated.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein, when a failure or abnormal situation of the autonomous vehicle is detected, the autonomous driving system takes over a system state to a fallback state, and when the system state is taken over to the fallback state as this allows for a reduced amount of processing power to be had as the system’s configuration later described is then only activated when the fallback state is initiated.
Response to Arguments
Response to 35 U.S.C. § 101 rejection of claims 1-11 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered and are persuasive.
Applicant argues on pages, “With respect to the 101 rejection, independent claims 1, 6 and 11 have been amended to more clearly recite the invention. Specifically, claim 1 has been amended to recite "A computer- implemented method of generating a destination for an emergency response of an autonomous vehicle, comprising at least one processor and memory, the method comprising: receiving from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle; generating forward perception information based on the data collected from [[a]] the at least one sensor mounted on the autonomous vehicle; generating a maximum deceleration value and a current heading range of the autonomous vehicle; setting a destination generation area based on the forward perception information; generating a candidate destination in the destination generation area based on the forward perception information=and the maximum deceleration value and the [[a]] current heading range of the autonomous vehicle; and when the candidate destination is provided as a plurality of candidate destinations, selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations, wherein the autonomous vehicle maneuvers to the selected destination. claimed subject matter is inseparably tied to specific hardware components (computer components, physical sensors and maneuvering an autonomous vehicles) that transform real- world sensor measurements into data that is used to safely maneuver an autonomous vehicle to a safe location in the case of an emergency. The claims therefore fall squarely within the technological-improvement category recognized in Enfish and McRO, rather than any enumerated abstract-idea category. Indeed, the claims do not recite simply an abstract idea without significantly more. Instead, they specify:
" Receiving detected data from at least one sensor on an autonomous vehicle regarding traffic conditions around the autonomous vehicle
" Generating a maximum deceleration value and a current heading range of the autonomous vehicle
" Generating a candidate destination based on forward perception information, the maximum deceleration value and the current heading range
" Maneuvering the autonomous vehicle to the selected destination. Thus, it is respectfully submitted that amended independent claims 1, 6 and 11 recite significantly more than an abstract idea and thus contain patentable subject matter. ". Independent claims 6 and 11 have been amended in a similar manner.
The Office Action characterizes the claims as an abstract idea without significantly more. Respectfully, this characterization oversimplifies and misconstrues the claims. The amended” –In response to point A the Examiner agrees with the arguments set forth by the Applicant and removes the 35 U.S.C. § 101 rejection to claims 1-7 as the inclusion of the claim limitation, “Maneuvering the autonomous vehicle to the selected destination” adds an element of control that cannot be performed by the human mind and therefor is now considered to be patentable subject matter rather than a mental process with a transaction of data.
Response to 35 U.S.C. § 103 rejection of claims 1-11 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on pages 2-4 , “Claims 1, 3, 4, 6, 8 and 9 stand rejected under 35 U.S.C. 103 as being unpatentable over Kumano et al. (US 11,667,281) in view of Floor (US 2023/0339505). Claims 2 and 7 stand rejected under 35 U.S.C. 103 as being unpatentable over Kumano et al. and Floor in view of Park (KR 102841665B). Claims 5 and 10 stand rejected under 35 U.S.C. 103 as being unpatentable over Kumano et al. and Floor in view of Sharma Banjade (US 2022/0388505). Finally, claim 11 stands rejected under 35 U.S.C. 103 as being unpatentable over Kumano et al. and Floor in view of Fausten (DE 10202320087A1). Applicant will argue the inapplicability of these rejections to the amended claims.
With respect to the 103 rejections, independent claims 1, 6 and 11 have been amended to more clearly recite the invention. Specifically, claim 1 has been amended to recite, among other features "receiving from at least one physical sensor on the autonomous vehicle data regarding traffic conditions around the autonomous vehicle; generating forward perception information based on the data collected from [[a]] the at least one sensor mounted on the autonomous vehicle; generating a maximum deceleration value and a current heading range of the autonomous vehicle; setting a destination generation area based on the forward perception information; generating a candidate destination in the destination generation area based on the forward perception informations and the maximum deceleration value and the [[a]] current heading range of the autonomous vehicle; and when the candidate destination is provided as a plurality of candidate destinations, selecting one destination from among the candidate destinations based on a maximum vertical movement distance of each of the candidate destinations, wherein the autonomous vehicle maneuvers to the selected destination". Independent claims 6 and 11 have been amended in a similar manner.
According to amended independent claims 1, 6 and 11, the invention recites generating a maximum deceleration value and a current heading range of the autonomous vehicle. In addition, the invention generates a candidate destination in the destination generation area based on 1). the forward perception information, 2). the maximum deceleration value and 3). the current heading range. In the Office Action, it is asserted that Kumano et al. and Floor teach the generation of the candidate destination. However, it is respectfully submitted that Kumano et al. and Floor fail to disclose that the maximum deceleration value is used in the generation of the candidate destination. Furthermore, it is respectfully submitted that Park, Sharma Banjade and Fausten fail
to overcome the deficiencies of Kumano et al. and Floor cited above. Thus, the cited prior art fails to disclose all the limitations of amended independent claims 1, 6 and 11.
In view of the above, it is respectfully submitted that amended independent claims 1, 6 and 11 and dependent claims 2-5 and 7-10 are in condition for allowance for at least the reasons set forth above. “ – In response to Point B arguments set forth by the applicant have been made moot on new grounds of rejection as supplied above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
12/19/25
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