DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-8, 10-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 20200216094 A1; hereinafter Zhu) in view of Oh et al. (US 20220341739 A1; hereinafter Oh).
Regarding claim 1, Zhu teaches a system comprising: a perception module comprising a plurality of sensors configured to operate in an autonomous mode (see at least, [0031] the autonomous vehicle driving control architecture 100 includes a perception system 102 that includes a number of sensors that perceives the environment around the autonomous vehicle) and to sense an obstacle in an environment around an autonomous vehicle (see at least, [0048] the input information received by motion planner 330 further includes obstacle information provided by sensors 311); an obstacle identification module (see at least, Fig 3, sensor data input interface-304), and a path planning module (see at least, Fig 3, trajectory planner-320), the computing device configured to receive current configuration and goal configuration from at least one of a user interface, an external source, and an internal source (see at least, [0044] The input information received by trajectory planner 320 includes a start point, a current position, a destination, navigation information, and environment information);
the obstacle identification module configured to detect obstacles around the autonomous vehicle (see at least, [0035] sensors 311 collect current environment information related to the unexpected obstacle and provide the collected environment information to computing device 300…may include the size of the obstacle, the moving direction of the obstacle, and the speed of the obstacle) the obstacle identification module determining position, velocity, and attitude of the obstacles (see at least, [0048] the input information received by motion planner 330 further includes obstacle information provided by sensors 311…includes detailed position information, heading value, speed value); and
the path planning module comprising a path planning algorithm and a dynamic step-size generator (see at least, [0044] The trajectory generation algorithm includes a Dynamic Programming (DP) method that is used by trajectory planner 320 to generate multiple possible paths according to the input information), the path planning module configured to: determine a path comprising a sequence of nodes of dynamic step-size for the autonomous vehicle to travel from a current configuration to a goal configuration (see at least, [0046] Trajectory planner 320 may communicate with controller 340
multiple times when autonomous vehicle 310 moves from the start point to the destination…The current position of autonomous vehicle 310 is indicated by a coarse position value provided by sensors 311…indicates a position located in a segment constructed by two consecutive waypoints in a map…trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints), the sequence of nodes comprising a current node and a successor node (see at least, [0044] Each path generated by trajectory planner 320 includes a sequence of waypoints. Each waypoint has a position value that is expressed by p(x, y), where the symbol x in p(x, y) indicates a value on the horizontal axis of the map, and the symbol y in p(x, y) indicates a value on the vertical axis of the map. A distance between two neighboring waypoints is about 50 meters to 150 meters), generate, at the current node, a plurality of sub paths to obtain a local path for the autonomous vehicle to reach to the successor node (see at least, [0049] motion planner
330 calculates the path information based on a current position of autonomous vehicle 310 and the next waypoint received…includes a plurality of poses, which enables autonomous vehicle 310 to move from the position indicated by the current position value of the autonomous vehicle 310 to the next
waypoint received step by step), wherein the local path is selected from a plurality of collision-free and kinematically-feasible candidate sub paths selected from the plurality of sub paths (see at least, [0046] The coarse position value indicates a position located in a segment constructed by two consecutive
waypoints in a map…the trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints, e.g., a static obstacle, and each of the multiple possible paths starts with a waypoint close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy), and wherein length of the local path is the least of the plurality of collision-free and kinematically-feasible candidate sub paths (see at least [0046] each of the multiple possible paths starts with a waypoint
close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy), calculate a maneuver for the autonomous vehicle to track the local path (see at least, [0031] The calculated coarse maneuver selections, motion planning
constraints, and the calculated next short-range waypoint data are provided to the motion planner 108 along with object data and road constraint data from the perception system 102 to calculate the controls for the autonomous vehicle, including the desired vehicle speed and direction), the path planning module controlling the autonomous vehicle based on the maneuver, control the autonomous vehicle based on the calculated maneuver (see at least, [0031] The calculated controls 110 are used to control the appropriate actuators of the autonomous vehicle in a conventional manner).
Zhu does not explicitly teach a computing device comprising an occupancy grid map prediction module, the occupancy grid map prediction module configured to predict and generate a set of occupancy grid maps based on sensor data collected from the plurality of sensors. However, Oh teaches these limitations.
Oh teaches a computing device comprising an occupancy grid map prediction module (see at least, [0084] The controller 120 may generate a map by scoring the plurality of cells, based on the information on the road and the information on the demand amount….may be a grid map), the occupancy grid map prediction module configured to predict and generate a set of occupancy grid maps based on sensor data collected from the plurality of sensors (see at least, Fig 1; information obtaining device-110; [0086] the controller 120 may always generate at least one of the first grid map, the second grid map, or the third grid map).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include a computing device comprising an occupancy grid map prediction module, the occupancy grid map prediction module configured to predict and generate a set of occupancy grid maps based on sensor data collected from the plurality of sensors as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 2, the combination of Zhu and Oh teaches the system of claim 1. Zhu further teaches wherein the plurality of sensors comprising a camera, a location sensor, a radar sensor, a light detection and ranging (LiDAR) sensor, an ultrasonic sensor, and an audio sensor (see at least, [0034] Sensors 311 may include…cameras…radar/lidar units, microphones, laser units).
Regarding claim 3, the combination of Zhu and Oh teaches the system of claim 1. Oh further teaches wherein the occupancy grid map prediction module generates the occupancy grid map based on vehicle data (see at least, [0085] The controller 120 may generate...a second grid map based on the demand amount for the autonomous driving vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include a the occupancy grid map prediction module generates the occupancy grid map based on vehicle data as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 4, the combination of Zhu and Oh teaches the system of claim 1. Oh further teaches wherein the occupancy grid map prediction module generates the occupancy grid map based on track data (see at least, [0085] the controller 120 may determine the accessibility of the road, based on at least one of information on the road width of the road, a number of lanes on the road, a number of crossroads on the road, or a number of routes, which allow movement to a nearby area, of the road, and may generate a third grid map, based on the accessibility of the road).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include the occupancy grid map prediction module generates the occupancy grid map based on track data as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 5, the combination of Zhu and Oh teaches the system of claim 1. Oh further teaches wherein the occupancy grid map prediction module generates the occupancy grid map based on obstacle data (see at least, [0085] The controller 120 may generate a first grid map based on the real-time traffic volume of the road).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include the occupancy grid map prediction module generates the occupancy grid map based on obstacle data as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 6, the combination of Zhu and Oh teaches the system of claim 1. Zhu further teaches wherein the obstacle identification module uses a multi-target tracking scheme to generate a consistent map of static and dynamic obstacles near the autonomous vehicle (see at least, [0048] the input information received by motion planner 330 further includes obstacle information provided by sensors 311. The obstacle may be a static obstacle or a moving obstacle).
Regarding claim 7, the combination of Zhu and Oh teaches the system of claim 1. Zhu further teaches wherein the path planning module generates a plurality of sub paths considering drivable ground (see at least, [0044] The trajectory generation algorithm includes a Dynamic Programming (DP) method that is used by trajectory planner 320 to generate multiple possible paths according to the input information. Each path generated by trajectory planner 320 includes a sequence of waypoints).
Regarding claim 8, the combination of Zhu and Oh teaches the system of claim 1. Zhu further teaches wherein the path planning module generates a plurality of sub paths considering obstacles (see at least, [0031] The calculated coarse maneuver selections, motion planning constraints, and the calculated next short-range waypoint data are provided to the motion planner 108 along with object data and road constraint data from the perception system).
Regarding claim 10, the combination of Zhu and Oh teaches the system of claim 1. Oh further teaches wherein the path planning module generates a plurality of sub paths considering user-defined preferences (see at least, [0138] the server 200 may receive a request for the use of an autonomous driving vehicle from a departure point “S1” to a destination “S2” by a user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include the path planning module generates a plurality of sub paths considering user-defined preferences as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 11, Zhu teaches a method comprising: receiving, at a computing device, sensor data from a plurality of sensors of an autonomous vehicle (see at least, [0031] the autonomous vehicle driving control architecture 100 includes a perception system 102 that includes a number of sensors that perceives the environment around the autonomous vehicle), pertaining to an environment of a current configuration (see at least, [0044] The input information…includes…a current position…and environment information); receiving, at the computing device, a goal configuration (see at least, [0044] The input information received by trajectory planner 320 includes…a destination); generating, by the computing device, a path comprising a sequence of nodes of dynamic step-size (see at least, [0046]
Trajectory planner 320 may communicate with controller 340 multiple times when autonomous vehicle 310 moves from the start point to the destination…The current position of autonomous vehicle 310 is indicated by a coarse position value provided by sensors 311…indicates a position located in a segment constructed by two consecutive waypoints in a map…trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints), wherein a successor node is generated at a current node using a path planning module comprising a dynamic step-size generator (see at least, [0044] The trajectory generation algorithm
includes a Dynamic Programming (DP) method that is used by trajectory planner 320 to generate multiple possible paths according to the input information); generating, at the current node, a plurality of sub paths to obtain a local path for the autonomous vehicle to reach the successor node (see at least, [0049] motion planner 330 calculates the path information based on a current position of autonomous
vehicle 310 and the next waypoint received…includes a plurality of poses, which enables autonomous
vehicle 310 to move from the position indicated by the current position value of the autonomous
vehicle 310 to the next waypoint received step by step), wherein the local path is selected from a plurality of collision-free and kinematically-feasible candidate sub paths selected from the plurality of sub paths (see at least, [0046] The coarse position value indicates a position located in a segment constructed by two consecutive waypoints in a map…the trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints, e.g., a static obstacle, and each of the multiple possible paths starts with a waypoint close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy), and wherein length of the local path is the least of the plurality of collision-free and kinematically-feasible candidate sub paths (see at least [0046] each of the multiple possible paths starts with a waypoint close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy); calculating, by the computing device, a maneuver for the autonomous vehicle to track the local path (see at least, [0031] The calculated coarse maneuver selections, motion planning constraints, and the calculated next short-range waypoint data are provided to the motion planner 108 along with object data and road constraint data from the perception system 102 to calculate the controls for the autonomous vehicle, including the desired vehicle speed and direction); and controlling the autonomous vehicle based on the calculated maneuver (see at least, [0031] The calculated controls 110 are used to control the appropriate actuators of the autonomous vehicle in a conventional manner).
Zhu does not explicitly teach processing, by the computing device, the sensor data, the current configuration, and the goal configuration to generate an occupancy grid map of the environment; updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data. However, Oh teaches these limitations.
Oh teaches processing, by the computing device, the sensor data, the current configuration, and the goal configuration (see at least, [0130] the information obtaining device 210 may obtain…at least one of a departure point, a destination) to generate an occupancy grid map of the environment (see at least, [0131] The route generator 220 may generate information on a grid map…based on information obtained from the information obtaining device 210); updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data (see at least, [0097] the controller 1 may generate a final grid map, based on scores and weights for each situation, which are applied to cells in the first grid map, the second grid map, and the third grid map).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include processing, by the computing device, the sensor data, the current configuration, and the goal configuration; updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 12, the combination of Zhu and Oh method of claim 11. Zhu further teaches wherein the autonomous vehicle is capable of sensing and navigating the environment (see at least, [0031]
the autonomous vehicle driving control architecture 100 includes a perception system 102 that includes a number of sensors that perceives the environment around the autonomous vehicle and provides control inputs to the respective functional units of the autonomous vehicle driving control architecture 100).
Regarding claim 13, the combination of Zhu and Oh teaches the method of claim 11. Zhu further teaches wherein the autonomous vehicle is in at least one of a driving mode, a reversing mode, and a parking mode (see at least, [0031] the controls for the autonomous vehicle, including the desired vehicle speed and direction. The calculated controls 110 are used to control the appropriate actuators of the autonomous vehicle).
Regarding claim 14, the combination of Zhu and Oh teaches the method of claim 11. Zhu further teaches wherein the plurality of sensors is configured to operate in an autonomous mode to sense an obstacle in the environment (see at least, [0048] the input information received by motion planner 330 further includes obstacle information provided by sensors 311).
Regarding claim 16, the combination of Zhu and Oh teaches the method of claim11, Oh further teaches wherein the occupancy grid map is generated dynamically in real-time (see at least, [0014] the controller may be configured to generate at least one of a first grid map based on the real-time traffic volume of the road).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include the occupancy grid map is generated dynamically in real-time as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 17, the combination of Zhu and Oh teaches the method of claim 11. Oh further teaches further comprising receiving, at the computing device, data pertaining to real-time traffic conditions from a data source (see at least, [0066] the sensor 11 may obtain at least one of information on a traffic volume of a road, information on the construction of the road, or information on a section (accident section) of the road, in which an accident occurs, from at least one of a road-provided camera or a government office).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include receiving, at the computing device, data pertaining to real-time traffic conditions from a data source as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 18, the combination of Zhu and Oh teaches the method of claim11. Oh further teaches further comprising estimating, by the computing device, a density of a plurality of obstacles in the environment (see at least, [0089] the controller 120 may divide a map “M” into cells “C”. The controller 120 may score the cell “C”….based on the traffic volume…may assign a lower score to the cell “C” with respect to a smaller traffic volume, and may assign a higher score to the cell “C” with respect to a larger traffic volume).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include estimating, by the computing device, a density of a plurality of obstacles in the environment as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Regarding claim 19, the combination of Zhu and Oh teaches the method of claim 18. Zhu further teaches further comprising estimating, by the computing device, a searching step-size based on density of the plurality of obstacles (see at least, [0044] The environment information includes traffic statistical data and static obstacle data. The trajectory generation algorithm includes a Dynamic Programming (DP) method that is used by trajectory planner 320 to generate multiple possible paths according to the input information. Each path generated by trajectory planner 320 includes a sequence of waypoints).
Regarding claim 20, Zhu teaches a non-transitory computer-readable medium including instructions stored thereon that when processed by at least one processor causes a system associated with an autonomous vehicle to perform operations (see at least, [0011] a non-transitory computer-readable
media storing computer instructions for modifying operation of an autonomous vehicle) comprising: receiving, at a computing device, sensor data from a plurality of sensors of the autonomous vehicle (see at least, [0031] the autonomous vehicle driving control architecture 100 includes a perception system 102 that includes a number of sensors that perceives the environment around the autonomous vehicle), pertaining to an environment of a current configuration (see at least, [0044] The input information…includes…a current position…and environment information); receiving, at the computing device, a goal configuration (see at least, [0044] The input information received by trajectory planner 320 includes…a destination); generating, by the computing device, a path comprising a sequence of nodes of dynamic step-size (see at least, [0046] Trajectory planner 320 may communicate with controller 340 multiple times when autonomous vehicle 310 moves from the start point to the destination…The current position of autonomous vehicle 310 is indicated by a coarse position value provided by sensors 311…indicates a position located in a segment constructed by two consecutive
waypoints in a map…trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints), wherein a successor node is generated at a current node using a path planning module comprising a dynamic step-size generator (see at least, [0044] The trajectory generation algorithm includes a Dynamic Programming (DP) method that is used by trajectory planner 320 to generate multiple possible paths according to the input information); generating, at the current node, a plurality of sub paths to obtain a local path for the autonomous vehicle to reach the successor node (see at least, [0049] motion planner 330 calculates the path
information based on a current position of autonomous vehicle 310 and the next waypoint received
…includes a plurality of poses, which enables autonomous vehicle 310 to move from the position indicated by the current position value of the autonomous vehicle 310 to the next waypoint received
step by step), wherein the local path is selected from a plurality of collision-free and kinematically-feasible candidate sub paths selected from the plurality of sub paths (see at least, [0046] The coarse position value indicates a position located in a segment constructed by two consecutive waypoints in a map…the trajectory planner 320 may calculate multiple possible paths for each coarse position value received based on other input constraints, e.g., a static obstacle, and each of the multiple possible paths starts with a waypoint close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy), and wherein length of the local path is the least of the plurality of collision-free and kinematically-feasible candidate sub paths (see at least [0046] each of the multiple possible paths starts with a waypoint close to the current position and ends at the destination…selects a path from the multiple possible paths according to the preset policy); calculating, by the computing device, a maneuver for the autonomous vehicle to track the local path (see at least, [0031] The calculated coarse maneuver selections, motion planning constraints, and the calculated next short-range waypoint data are provided to the motion planner 108 along with object data and road constraint data from the perception system 102 to calculate the controls for the autonomous vehicle, including the desired vehicle speed and direction); and controlling the autonomous vehicle based on the calculated maneuver (see at least, [0031] The calculated controls 110 are used to control the appropriate actuators of the autonomous vehicle in a conventional manner).
Zhu does not explicitly teach processing, by the computing device, the sensor data, the current configuration, and the goal configuration to generate an occupancy grid map of the environment; updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data. However, Oh teaches these limitations.
Oh teaches processing, by the computing device, the sensor data, the current configuration, and the goal configuration (see at least, [0130] the information obtaining device 210 may obtain…at least one of a departure point, a destination) to generate an occupancy grid map of the environment (see at least, [0131] The route generator 220 may generate information on a grid map…based on information obtained from the information obtaining device 210); updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data (see at least, [0097] the controller 1 may generate a final grid map, based on scores and weights for each situation, which are applied to cells in the first grid map, the second grid map, and the third grid map).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhu to include processing, by the computing device, the sensor data, the current configuration, and the goal configuration; updating, at the computing device, within the occupancy grid map, the current configuration and the goal configuration based on the sensor data as taught by Oh in order to generate the driving route of an autonomous driving vehicle efficiently by totally considering a complex road situation varying in real time (Oh, [0004]).
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 20200216094 A1; hereinafter Zhu) in view of OH et al. (US 20220341739 A1; hereinafter OH) in further view of Biala et al. (US 20220017115 A1; hereinafter Biala).
Regarding claim 9, the combination of Zhu and Oh teaches the system of claim 1. The combination does not explicitly teach wherein the path planning module generates a plurality of sub paths considering traffic regulations. However, Biala teaches this limitation.
Biala teaches the path planning module generates a plurality of sub paths considering traffic regulations (see at least, [0145] The navigation controller 1020 may include a motion planning engine (MPE) 686
and a path follower engine (PFE) 1146 each of which includes machine learning algorithms for planning the motion of the vehicle based on various parameters of a to be followed path along a planned route from an origination location to a destination location of global coordinate system. The parameter may include…motor vehicle operation laws of a jurisdiction…i.e., speed limits).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zhu and Oh to include the path planning module generates a plurality of sub paths considering traffic regulations as taught by Biala in order to avoid a collision at an intersection or as a vehicle travels along an imminent path (Biala, [0145]).
Regarding claim 15, the combination of Zhu and Oh teaches the method of claim 11. The combination does not explicitly teach further comprising processing the sensor data using digital image processing techniques, wherein the digital image processing techniques comprise image filtering, segmentation, enhancement, and edge detection. However, Biala teaches this limitation.
Biala teaches processing the sensor data using digital image processing techniques (see at least, [0054] The vehicle CVS 115 may include one or more cameras for capturing digital images of various features of the environment in which the vehicle 105 is traveling, along with a processor and software for processing images), wherein the digital image processing techniques comprise image filtering (see at least, [0094] The signal processing algorithms 630 may include…debayer filtering algorithms 634 such that raw images become red, green, blue (RGB) images), segmentation (see at least, [0094] processing channels 620 may include a segmentation module (SM) 625), enhancement (see at least, [0094] the signal processing algorithms 630 may also employ tonal mapping algorithms 632 to approximate an appearance of images such as for generating processed image data representative of high dynamic range images), and edge detection (see at least, [0098] Feature extraction algorithms may include…edge detection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Zhu and Oh to include processing the sensor data using digital image processing techniques, wherein the digital image processing techniques comprise image filtering, segmentation, enhancement, and edge detection as taught by Biala in order
process images and identify objects of interest in the images, to define a vehicle's vision range (Biala, [0054]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Watanabe et al. (US 20220207883 A1) discloses the path planning module configured to: determine a path comprising a sequence of nodes of dynamic step-size for the autonomous vehicle to travel from a current configuration to a goal configuration (for example, [0089] the action planning unit generates action plan candidates by, for example, an A* algorithm (A star search algorithm) of dividing an environment into grids and optimizing the arrival determination and the weight of the route to generate the best path).
Doria et al. (US 20190279049 A1) discloses a computing device comprising an occupancy grid map prediction module (for example, [0033] The mobile device...and/or the server identifies one or more voxel occupancy grids associated with a region of a roadway…an occupancy grid is a
volumetric grid representing a 3D space. The volumetric grid includes a plurality of voxels that
each represents a portion of the 3D space).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F.
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, Jelani Smith can be reached at 571-270-3969. 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.
/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
Prosecution Insights