DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-19 are pending in this application.
Claims 1 is currently amended.
No claims are newly presented.
No claims are cancelled.
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Sep. 17, 2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 recites “ . . . includes one or more accessible areas from a perspective of the UAV; . . .;” however it is unclear if “accessible areas” means ‘accessible to the UAV” within the context of permissible airspace or if “accessible areas” means ‘accessible to the connected ground vehicle” within the context of traffic or roadway obstructions. While the recited limitations are provided the broadest reasonable interpretation in light of the specification, the scope of the claim is rendered indefinite. For the purposes of the prior art rejection below this term has been interpreted as accessible by the land vehicle using the elevated perspective of the UAV while flying because such an interpretation would appear more consistent with a later limitation claiming “. . . wherein the determined one or more accessible areas are dynamically adjusted based on the traffic rules and the motion information of the ground objects . . ..“
Claims 1 further recites “. . . wherein the land vehicle processor is configured to release the UAV when the land vehicle is approaching an intersection and the UAV is configured to fly toward the intersection, leading the land vehicle, wherein when the UAV is close to the intersection, the at least one UAV LiDAR sensor and the at least one UAV camera of the UAV are further configured to collect the first ground traffic information comprising positions of the crosswalks, the land dividing lanes, the curbsides, and other vehicles, . . . wherein the land vehicle processor is further configured to release the UAV to a position of following the land vehicle or leading the land vehicle.“ It is unclear if the UAV being (1) dispatched to the next intersection and / or (2) dispatched to the follow / lead the ground vehicle is claimed in the alternative. Further, it is unclear that if the limitations are not in the alternative, how the UAV accomplishes both missions simultaneously. While the recited limitations are provided the broadest reasonable interpretation in light of the specification, the scope of the claim is rendered indefinite. For the purposes of the prior art rejection below this term has been interpreted as independent options being conducted in the alternative.“
Claims 2-19 are rejected due to dependency on a previously rejected claim. Correction or clarification is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 7, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20210132612 A1) in view of Kim et al. (US 20200160735 A1) in view of Yagi (US 20190009704 A1) (the combination of these references referred to as combination Wang hereinafter). As regards the individual claims:
With respect to claim 1, Wang teaches an autonomous driving system, comprising:
an unmanned aerial vehicle (UAV) in air, wherein the UAV includes: at least one UAV camera configured to collect visual images of ground objects, (Wang: ¶ 024; images captured by a drone, or other unmanned aerial vehicle (UAV), which can capture overhead images directly, without any transformation or the associated inaccuracies.) at least one UAV LiDAR sensor configured to determine distance information between the ground objects and the UAV, a UAV processor configured to convert first raw sensor information to first ground traffic information (Wang: ¶ 035; a LiDAR sensor can be configured to collect point cloud data representing a 360-degree view of the ambient environment of the vehicle)in a three-dimensional (3D) coordinate system, (Wang: ¶ 065; 3D representation can be divided into voxels) wherein the first raw sensor information includes the visual images of the ground objects and the distance information between the ground objects and the UAV, (Wang: ¶ 021; sensors provide accurate, longer distance measurements and positioning data for the vehicle.) a UAV communication module configured to transmit the first ground traffic information, (Wang: ¶ 025; processing of environmental data by collecting data using an aerial vehicle and transmitting the data to a ground vehicle for use in object detection)wherein the first ground traffic information includes one or more accessible areas from a perspective of the UAV; (Wang: ¶ 074; construct a localization mapping for itself to plan a route.) and a land vehicle communicatively connected to the UAV in the air, (Wang: ¶ 025; using an aerial vehicle and transmitting the data to a ground vehicle)wherein the land vehicle includes: one or more vehicle sensors configured to collect second raw sensor information surrounding the land vehicle, (Wang: ¶ 078; ground vehicle, in accordance with various embodiments. As discussed above, a ground vehicle 111 may include one or more scanning sensors 132, such as a LiDAR sensor. The ground vehicle's scanning sensors may have a sensing range 602, 604 in which it can detect its environment) wherein the second raw sensor information comprises visual images of surrounding objects and distance from the surrounding objects to the land vehicle, (Wang: ¶ 076; car may use cameras, LiDAR, inertial measurement units, gyroscopes, and other sensors to generate the high precision map. The car can transmit the resulting high precision map to a drone flying near the car.)a land vehicle processor configured to convert the second raw sensor information to second ground traffic information in a two-dimensional (2D) coordinate system, (Wang: ¶ 057; image transform manager can implement perspective transformation techniques to convert the front-view scanning data collected by the ground vehicle to a birds-eye view) and a land vehicle communication module configured to receive the first ground traffic information from the UAV, wherein the second ground traffic information includes one or more accessible areas from a perspective of the land vehicle, (Wang: ¶ 089; can include a data manager 914 that receives scanning data from aerial mapping manager 900 via communication system 120A, and from onboard scanning sensors coupled to ground vehicle 111 via scanning sensor interface) . . . and the visual images included in the first raw sensor information, (Wang: ¶ 077; ground vehicle can combine its own map with that of the UAV to generate a combined map) . . . wherein when the UAV is close to the intersection, the at least one UAV LiDAR sensor and the at least one UAV camera of the UAV are further configured to collect the first ground traffic information comprising positions of the crosswalks, the land dividing lanes, the curbsides, (Wang: Figs. 2B-2C; [showing lane markings, curbs, dividers) (Wang: ¶ 070; one or more navigation objects include a driving lane) and other vehicles, (Wang: ¶ 073; include detecting a traffic condition affecting the ground vehicle) wherein the land vehicle processor is further configured to convert the first ground traffic information in the three-dimensional (3D) coordinate system to the two- dimensional (2D) coordinate system, (Wang: ¶ 057; image transform manager can implement perspective transformation techniques to convert the front-view scanning data collected by the ground vehicle to a birds-eye view) . . . wherein the land vehicle processor is further configured to generate one or more land vehicle planning results from the first ground traffic information and the second ground traffic information, directing the land vehicle to the destination, wherein the land vehicle planning results comprise one or more instructions, and wherein the land vehicle processor is further configured to generate decisions for the land vehicle based on a world model generated by combining the first ground traffic information and the second ground traffic information. (Wang: ¶ 070; a navigation control command can be executed based on one or more navigation objects identified in the overlapping portion of the first scanning data and the second scanning data) (Wang: ¶ 077; ground vehicle can combine its own map with that of the UAV to generate a combined map)
Wang does not explicitly teach: wherein each of the one or more accessible areas, from the perspective of the UAV, is determined based on traffic rules and motion information of the ground objects, wherein the motion information includes acceleration information of the ground objects and one or more moving objects surrounding the land vehicle, wherein the determined one or more accessible areas are dynamically adjusted based on the traffic rules and the motion information of the ground objects, wherein the ground objects are located on a route between the land vehicle and a destination, wherein the land vehicle processor is configured to determine a direction associated with the ground objects and the surrounding objects based on the visual images included in the second raw sensor information . . . to a position of following the land vehicle or leading the land vehicle, . . . ; Kim does teach:
wherein each of the one or more accessible areas, from the perspective of the UAV (Kim: 042; accident partially or fully blocking the road and the land vehicle, is determined based on traffic rules (Kim: 015; legal speed limits therefor may be used to calculate a travel path)and motion information of the ground objects, wherein the motion information includes acceleration information (Kim: 040; sensor data may include . . . conditions ahead . . . conditions may include free-flowing traffic, slow-moving and/or congested traffic, a congested or a free-flowing intersection) (Kim: 049; route can be changed to shorten a response time of the emergency vehicle to the location of the emergency by altering the route and changing traffic conditions along the altered route) of the ground objects (Kim: 042; sensor data may indicate the presence of a partial or full traffic obstruction along the calculated route. The traffic obstruction may be, for example, a car accident partially or fully blocking the road, a fallen tree)and one or more moving objects surrounding the land vehicle, (Kim: 045; traffic conditions such as the number of cars on the roads, the speed in which the cars are traveling on the roads)wherein the determined one or more accessible areas are dynamically adjusted based on the traffic rules and the motion information of the ground objects, (Kim: 0949; recalculating of the route includes determining how the calculated route can be changed to shorten a response time of the emergency vehicle to the location of the emergency by altering the route and changing traffic conditions along the altered route)wherein the ground objects are located on a route between the land vehicle and a destination, (Kim: 042; accident partially or fully blocking the road)wherein the land vehicle processor is configured to determine a direction associated with the ground objects and the surrounding objects based on the visual images included in the second raw sensor information (Kim: 045; traffic conditions such as the number of cars on the roads, the speed in which the cars are traveling on the roads) . . . to a position of following the land vehicle or leading the land vehicle, (Kim: ¶ 004; UAV is automatically piloted ahead of the emergency vehicle) . . .
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kim with the teachings of Wang because doing so would predictably result “the travel time to the location of the emergency [being] reduced” when encountering traffic. (Kim : ¶ 018).
Wang does not explicitly teach: . . . wherein the land vehicle processor is configured to release the UAV when the land vehicle is approaching an intersection and the UAV is configured to fly toward the intersection, leading the land vehicle, . . . wherein the land vehicle processor is further configured to release the UAV.; Yagi does teach:
. . . wherein the land vehicle processor is configured to release the UAV when the land vehicle is approaching an intersection and the UAV is configured to fly toward the intersection, leading the land vehicle, (Yagi: ¶ 030; when the vehicle 101 approaches the intersection, the flight state of the unmanned flying object 10 may be controlled by the control unit. . . unmanned flying object may be normally stored in the vehicle 101, and may take off from the vehicle 101 only when necessary) . . . wherein the land vehicle processor is further configured to release the UAV (Yagi: ¶ 030; flying object may be normally stored in the vehicle 101, and may take off from the vehicle 101 only when necessary)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Yagi with the teachings of Wang because doing so would predictably result in the improvement that it becomes possible to “project the vehicle information to an area that is a blind spot from the vehicle. “ (Yagi: ¶ 004).
Regarding claim 7, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
wherein the second ground traffic information is formatted in a coordinate system with a position of the land vehicle as a coordinate origin. (Wang: ¶ 079; Using its scanning sensors 132, the ground vehicle can obtain a position of the aerial vehicle so long as it is operating within range of the ground vehicle's sensors. The position may be relative to the ground vehicle or may be an absolute position in the world coordinate system or other coordinate system)
Regarding claim 16, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
wherein the land vehicle processor is further configured to combine the first ground traffic information and the second ground traffic information to generate a world model and generate coordinated planning results based on the world model. (Wang: ¶ 065; the scanning data may include point cloud data. The point cloud data may be a three-dimensional representation of the target environment (e.g., the roadway environment). This 3D representation can be divided into voxels (e.g., 3D pixels). Each point in the point cloud of the mapping data is associated with a position in the scanner reference frame that is determined relative to the scanning sensor. The positioning data of the movable object, produced by the positioning sensor, may then be used to convert this position in the scanner reference frame to the output reference frame in a world coordinate system.) (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
Claims 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Kim in view of Yagi as applied to claim 1 above, and further in view of Radetzki (US 20210016735 A1).
Regarding claim 2, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang does not explicitly teach:
wherein the first ground traffic information is formatted in a coordinate system with a position of the UAV as a coordinate origin; however, Radetzki does teach:
wherein the first ground traffic information is formatted in a coordinate system with a position of the UAV as a coordinate origin. (Radetzki: ¶ 079; a position of an object (for example of the object) is an absolute position or a relative position (for example relative to the unmanned vehicle). Such a position can be represented, for example, in the form of a position specification, which refers, for example, to an arbitrarily specified absolute or relative coordinate system. A position of an object relative to the unmanned vehicle can be determined, for example, based on a sequence of multiple images captured by an optical sensor (for example a camera) of the unmanned vehicle.).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Wang with the teachings of Radetzki with a reasonable expectation of success because the use of a known technique to improve similar methods in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Wang's and Radetzki's base methods are similar vehicle control systems based on object recognition; however, the combined device would be improved by considering a coordinate systems originating at the UAV’s position because doing so would improve speed of processing.
Regarding claim 3, as detailed above, combination Wang in view of Radetzki teaches the invention as detailed with respect to claim 2. Wang further teaches:
wherein the first ground traffic information includes at least one of: a position of the land vehicle in the coordinate system; (Wang: ¶ 083; ground vehicle can convert the coordinate system of the image-based real-time map to match the coordinate system of the point cloud data collected by the ground vehicle. The ground vehicle can then return a combined map of the matched overlapping portion to the aerial vehicle to be used for navigation.) (Wang: ¶ 092; The first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle. In some embodiments, receiving, by an aerial vehicle, a first real-time map from a ground vehicle, wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.) first position information that indicates locations of one or more still objects on the ground in the coordinate system with the position of the UAV as the coordinate origin; (Wang: ¶ 049; reference object 202 can be identified in the vehicle scanning data. The reference object can be associated with a coordinate in the scanning data (e.g., an image coordinate system based on pixels). For example, the example front-view shown in FIG. 2B is a projection of the roadway environment 200 onto a two-dimensional image plane. Using the intrinsic parameters (e.g., focal length, lens parameters, etc.) and extrinsic parameters (e.g., position, orientation, etc.) of the scanning sensor of the ground vehicle, a projection matrix can be used to convert from the image coordinate system of then two-dimensional image plane to the world coordinate system of the three-dimensional roadway environment) (Wang: ¶ 079; Using its scanning sensors 132, the ground vehicle can obtain a position of the aerial vehicle so long as it is operating within range of the ground vehicle's sensors. The position may be relative to the ground vehicle or may be an absolute position in the world coordinate system or other coordinate system) (Wang: Fig. 2B; [showing tree as a still object])
And Kim further teaches: first motion information that indicates velocities of one or more moving objects on the ground; (Kim: 045; traffic conditions such as the number of cars on the roads, the speed in which the cars are traveling on the roads) first predicted trajectories of the one or more moving objects on the ground; (Kim: ¶ 074; computer learning may be used to predict future actions of drivers and/or pedestrians given a current state of traffic conditions on a given road, intersection, or other travel path..) first status information that indicates statuses of one or more traffic signals (Kim: ¶ 037; one or more traffic lights along the calculated route may be extended and the red light time may be reduced to alleviate traffic congestions)
Regarding claim 4, as detailed above, combination Wang in view of Radetzki teaches the invention as detailed with respect to claim 3. Wang further teaches:
wherein the UAV processor is further configured to determine UAV planning results (Wang: ¶ 076; drone can combine its map with the high precision map received from the car to obtain a high precision map it can use to navigate through its environment)
And Kim further teaches: including a path of the land vehicle to the destination based on the first ground traffic information. (Kim: ¶ 044; Operation S27 may include recalculating the route between the received present location of the emergency vehicle and the received location of the emergency site using the area map data and the obtained sensor data from the UAV) (Kim: ¶ 043; operation S25 the sensor data obtained from the UAV may be indicative of conditions ahead of the emergency vehicle along other routes (e.g., not the route which the emergency vehicle is currently taking).)
Regarding claim 5, as detailed above, combination Wang in view of Radetzki teaches the invention as detailed with respect to claim 3. Wang further teaches:
wherein the UAV processor is further configured to determine UAV planning results including one or more instructions in response to at least a portion of the first motion information that indicates the velocities or accelerations of the one or more moving objects. (Wang: ¶ 076; drone can also generate a map based on its onboard sensors as it flies nearby. However, this is a lower precision map, due to the types of sensors carried by the drone, and the computing resources available to the drone. However, the drone can combine its map with the high precision map received from the car to obtain a high precision map it can use to navigate through its environment (for example to avoid object, generate routes, etc. Embodiments take advantage of the higher quality sensors and computers available in a car or other ground vehicle to improve the maps available to a drone or other UAV.)
Regarding claim 6, as detailed above, combination Wang in view of Radetzki teaches the invention as detailed with respect to claim 3. Wang further teaches:
wherein the UAV processor is further configured to determine UAV planning results including one or more parameters of the land vehicle based on the first ground traffic information, and wherein the one or parameters include at least one of velocity, acceleration, and direction. (Wang: ¶ 076; drone can also generate a map based on its onboard sensors as it flies nearby. However, this is a lower precision map, due to the types of sensors carried by the drone, and the computing resources available to the drone. However, the drone can combine its map with the high precision map received from the car to obtain a high precision map it can use to navigate through its environment (for example to avoid object, generate routes, etc. Embodiments take advantage of the higher quality sensors and computers available in a car or other ground vehicle to improve the maps available to a drone or other UAV.)
Claims 8-11 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Kim in view of Yagi as applied to claims 7 and 1 respectively above and in further view of Lee (US 20210134156 A1). As regards the individual claims:
Regarding claim 8, as detailed above, combination Wang teaches the invention as detailed with respect to claim 7. Wang further teaches:
wherein the second ground traffic information further includes: second position information that indicates locations of one or more still objects surrounding the land vehicle (Wang: ¶ 035; a LiDAR sensor can be configured to collect point cloud data representing a 360-degree view of the ambient environment of the vehicle. Similarly, a high definition imaging sensor can collect image data (e.g., still images and video) of the environment around the ground vehicle.. . .(e.g., identify roadway objects (such as lane markings, other vehicles, trees and other objects present in the roadway environment)) in the coordinate system with the position of the land vehicle as the coordinate origin; (Wang: ¶ 079; Using its scanning sensors 132, the ground vehicle can obtain a position of the aerial vehicle so long as it is operating within range of the ground vehicle's sensors. The position may be relative to the ground vehicle or may be an absolute position in the world coordinate system or other coordinate system)
To the extent Wang is silent about or does not teach: second motion information that indicates velocities of the one or more moving objects surrounding the land vehicle; second predicted trajectories of the one or more moving objects surrounding the land vehicle; and second status information that indicates statuses of one or more traffic signals; Lee teaches:
second motion information that indicates velocities of the one or more moving objects surrounding the land vehicle; (Lee: ¶ 148; object detection device 210 can generate information about objects outside the vehicle 10. Information about an object can include at least one of information on presence or absence of the object, positional information of the object, information on a distance between the vehicle 10 and the object, and information on a relative speed of the vehicle 10 with respect to the object.) second predicted trajectories of the one or more moving objects surrounding the land vehicle; (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.) and second status information that indicates statuses of one or more traffic signals; (Lee: ¶ 330; monitoring information includes object information, traffic facility information, meta information, and the like. The object information may include information of vehicles and pedestrians adjacent to the vehicle 10, and may include information of surrounding buildings and facilities. The traffic facility information may include lane information, traffic light information, traffic facility information)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the trajectory prediction teachings of Lee with the teachings of Wang because doing so would result in the predicable benefit of better predicting dangerous collision risk (Lee: ¶ 008-014) and mitigating ahead of time.
Regarding claim 9, as detailed above, combination Wang in view of Lee teaches the invention as detailed with respect to claim 8. Wang further teaches:
wherein the land vehicle processor is further configured to determine the land vehicle planning results including a path of the land vehicle to the destination based on the second ground traffic information. (Wang: ¶ 058; When roadway objects are identified in the scanning data received from the aerial vehicle, the positions of the objects can be mapped to their corresponding positions in the scanning data captured by the ground vehicle. In some embodiments, navigation commands can be generated based on the object identified in the scanning data. For example, when a lane is detected in the scanning data, a polynomial curve can be calculated that fits the detected lane in the scanning data. The polynomial curve can be used to determine a trajectory for the ground vehicle to follow to stay within the lane. This can be provided to navigation controller 328 which can generate movement commands for the ground vehicle.)
Regarding claim 10, as detailed above, combination Wang in view of Lee teaches the invention as detailed with respect to claim 8. Wang further teaches:
wherein the land vehicle processor is further configured to determine the land vehicle planning results including one or more instructions (Wang: ¶ 044; the roadway environment 200 can include the ground vehicle driving on a roadway and an aerial vehicle flying above the roadway. As discussed, each vehicle can include one or more scanning sensors to collect scanning data from the roadway environment. One use of the scanning data is to perform various autonomous driving and/or assisted driving functions, such as lane detection.)
And Lee teaches: in response to at least a portion of the second motion information that indicates the velocities or accelerations of the one or more moving objects surrounding the land vehicle. (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.)
Regarding claim 11, as detailed above, combination Wang in view of Lee teaches the invention as detailed with respect to claim 8. Wang further teaches:
wherein the land vehicle processor is further configured to determine the land vehicle planning results including one or more parameters of the land vehicle based on the second ground traffic information, (Wang: ¶ 044; the roadway environment 200 can include the ground vehicle driving on a roadway and an aerial vehicle flying above the roadway. As discussed, each vehicle can include one or more scanning sensors to collect scanning data from the roadway environment. One use of the scanning data is to perform various autonomous driving and/or assisted driving functions, such as lane detection.)
And Lee teaches: and wherein the one or parameters include at least one of velocity, acceleration, and direction. (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.)
Regarding claim 17, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
wherein the land vehicle processor is further configured to: convert the first ground traffic information from a first coordinate system with a position of the UAV as a coordinate origin to a second coordinate system with a position of the land vehicle as the coordinate origin, (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.) (Wang: ¶ 079; Using its scanning sensors 132, the ground vehicle can obtain a position of the aerial vehicle so long as it is operating within range of the ground vehicle's sensors. The position may be relative to the ground vehicle or may be an absolute position in the world coordinate system or other coordinate system) convert first coordinates of one or more still objects, one or more moving objects, (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.). . . determine third coordinates of the one or more still objects, the one or more moving objects, the one or more traffic signals, and the one or more accessible areas in a world model based on the second coordinates and the second ground traffic information. (Wang: ¶ 065; the scanning data may include point cloud data. The point cloud data may be a three-dimensional representation of the target environment (e.g., the roadway environment). This 3D representation can be divided into voxels (e.g., 3D pixels). Each point in the point cloud of the mapping data is associated with a position in the scanner reference frame that is determined relative to the scanning sensor. The positioning data of the movable object, produced by the positioning sensor, may then be used to convert this position in the scanner reference frame to the output reference frame in a world coordinate system.) (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
To the extent Wang is silent regarding or does not teach: one or more traffic signals, and the one or more accessible areas, and predicted trajectories of the one or more moving objects identified in the first coordinate system to second coordinates in the second coordinate system; and; Lee does teach:
one or more traffic signals, and the one or more accessible areas, and predicted trajectories of the one or more moving objects identified in the first coordinate system to second coordinates in the second coordinate system; and (Lee: ¶ 330; monitoring information includes object information, traffic facility information, meta information, and the like. The object information may include information of vehicles and pedestrians adjacent to the vehicle 10, and may include information of surrounding buildings and facilities. The traffic facility information may include lane information, traffic light information, traffic facility information,) (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the trajectory prediction teachings of Lee with the teachings of Wang because doing so would result in the predicable benefit of better predicting dangerous collision risk (Lee: ¶ 008-014) and mitigating ahead of time.
Regarding claim 18, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
wherein the land vehicle processor is further configured to: convert first semantic segments of one or more still objects, one or more moving objects, one or more traffic signals, and the one or more accessible areas identified in a first coordinate system to second semantic segment in a second coordinate system; and (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.) determine third semantic segments of the one or more still objects, the one or more moving objects, (Wang: ¶ 021; a self-driving vehicle can analyze its surrounding environment based on data gathered by one or more sensors mounted on the vehicle, including, e.g., visual sensors, LiDAR sensors, millimeter wave radar sensors, ultrasound sensors, etc. The sensor data can be analyzed using image processing tools, machine learning techniques, etc. to determine depth information and semantic information, to assist the vehicle in identifying surrounding people and objects) . . . identified in a world model based on the second semantic segment and the second ground traffic information. (Wang: ¶ 065; the scanning data may include point cloud data. The point cloud data may be a three-dimensional representation of the target environment (e.g., the roadway environment). This 3D representation can be divided into voxels (e.g., 3D pixels). Each point in the point cloud of the mapping data is associated with a position in the scanner reference frame that is determined relative to the scanning sensor. The positioning data of the movable object, produced by the positioning sensor, may then be used to convert this position in the scanner reference frame to the output reference frame in a world coordinate system.)
To the extent Wang is silent regarding or does not teach: the one or more traffic signals, and the one or more accessible areas and predicted trajectories of the one or more moving objects; Lee does teach:
the one or more traffic signals, and the one or more accessible areas and predicted trajectories of the one or more moving objects (Lee: ¶ 330; monitoring information includes object information, traffic facility information, meta information, and the like. The object information may include information of vehicles and pedestrians adjacent to the vehicle 10, and may include information of surrounding buildings and facilities. The traffic facility information may include lane information, traffic light information, traffic facility information,) (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the trajectory prediction teachings of Lee with the teachings of Wang because doing so would result in the predicable benefit of better predicting dangerous collision risk (Lee: ¶ 008-014) and mitigating ahead of time.
Regarding claim 19, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
wherein the land vehicle processor is further configured to: convert first point clouds of one or more still objects, one or more moving objects, one or more traffic signals, and the one or more accessible areas identified in a first coordinate system to second point clouds in a second coordinate system; and determine third point clouds of the one or more still objects, the one or more moving objects, the one or more traffic signals, and the one or more accessible areas (Wang: ¶ 035; a LiDAR sensor can be configured to collect point cloud data representing a 360-degree view of the ambient environment of the vehicle . . . sensor 132 may collect scanning data related to the roadway environment in which the ground vehicle is operating (e.g., identify roadway objects (such as lane markings, other vehicles, trees and other objects present in the roadway environment), driving conditions (such as weather conditions), traffic information (including information related to nearby vehicles),) (Wang: ¶ 065; the point cloud data obtained by the aerial vehicle can be used to augment the point cloud data obtained by the ground vehicle) (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.) (Wang: ¶ 083; real-time maps may be merged by the ground vehicle, by identifying features in the image-based real-time map to features in the point cloud-based real-time map. . . . The ground vehicle can convert the coordinate system of the image-based real-time map to match the coordinate system of the point cloud data collected by the ground vehicle.) . . . in a world model based on the second point clouds and the second ground traffic information. (Wang: ¶ 065; the scanning data may include point cloud data. The point cloud data may be a three-dimensional representation of the target environment (e.g., the roadway environment). This 3D representation can be divided into voxels (e.g., 3D pixels). Each point in the point cloud of the mapping data is associated with a position in the scanner reference frame that is determined relative to the scanning sensor. The positioning data of the movable object, produced by the positioning sensor, may then be used to convert this position in the scanner reference frame to the output reference frame in a world coordinate system.) (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
To the extent Wang is silent regarding or does not teach: and predicted trajectories of the one or more moving objects identified; Lee does teach:
and predicted trajectories of the one or more moving objects identified (Lee: ¶ 010; The predicting the dangerous situation may include predicting a moving line of an adjacent vehicle and a pedestrian corresponding to the object, and predicting a collision risk between the pedestrian and the adjacent vehicle based on the predicted moving line of the pedestrian and the predicted moving line the adjacent vehicle.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the trajectory prediction teachings of Lee with the teachings of Wang because doing so would result in the predicable benefit of better predicting dangerous collision risk (Lee: ¶ 008-014) and mitigating ahead of time.
Claims 12-15 is rejected as being unpatentable over combination Wang as applied to claim 1, and further in view of Guney et al. (US 20230230471 A1).
Regarding claim 12, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang teaches:
. . .based on UAV planning results and land vehicle planning results. (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
To the extent Wang is silent regarding or does not teach: wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output . . .; Guney does teach:
wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output . . . (Guney: ¶ 025; learning-based module 126 uses this data, and applies learning-based algorithms to predict the changes in the traffic in a manner that detects whether there is a presence of traffic congestion at some portion of the roadway. The learning-based module 126 can be implemented in accordance with one of several known learning-based techniques, such as machine-learning (ML), artificial intelligence (AI), neural network, deep learning, and the like.) (Guney: ¶ 004; cooperative traffic congestion detection methods and systems are implemented that enhance the accuracy of detecting traffic congestion for enhanced routing and maneuvering vehicles along a travel route. In an embodiment, a vehicle is configured to receive data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, and vehicle movement, traffic, and the like, within the vicinity along the route.) (Guney: ¶ 003; systems can be crucial in solving such problems, and can allow for drivers and/or vehicles to make the right adjustments to make congestion easy to manage and reduce injuries.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Guney with the teachings of Wang because doing so would result in the predicable benefit of “improve[d] accuracy of traffic detection by leveraging vehicle connectivity, consensus, and the availability of vehicles with advanced sensors.” (Guney]: ¶ 039).
Regarding claim 13, as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
. . . UAV planning results, and land vehicle planning results. (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
To the extent Wang is silent regarding or does not teach: wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output based on the first ground traffic information, the second ground traffic information . . . ; Guney does teach:
wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output based on the first ground traffic information, the second ground traffic information, . . . (Guney: ¶ 025; learning-based module 126 uses this data, and applies learning-based algorithms to predict the changes in the traffic in a manner that detects whether there is a presence of traffic congestion at some portion of the roadway. The learning-based module 126 can be implemented in accordance with one of several known learning-based techniques, such as machine-learning (ML), artificial intelligence (AI), neural network, deep learning, and the like.) (Guney: ¶ 004; cooperative traffic congestion detection methods and systems are implemented that enhance the accuracy of detecting traffic congestion for enhanced routing and maneuvering vehicles along a travel route. In an embodiment, a vehicle is configured to receive data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, and vehicle movement, traffic, and the like, within the vicinity along the route.) (Guney: ¶ 003; systems can be crucial in solving such problems, and can allow for drivers and/or vehicles to make the right adjustments to make congestion easy to manage and reduce injuries.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Guney with the teachings of Wang because doing so would result in the predicable benefit of “improve[d] accuracy of traffic detection by leveraging vehicle connectivity, consensus, and the availability of vehicles with advanced sensors.” (Guney]: ¶ 039).
Regarding claim 14 as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang further teaches:
. . .based on the first ground traffic information and the second ground traffic information. (Wang: ¶ 092; wherein the first real-time map is based on first scanning data collected using a first scanning sensor coupled to the ground vehicle, can include transmitting the second real-time map to the ground vehicle, the ground vehicle configured to convert coordinates in the second real-time map to a coordinate system to match the first real-time map, determine an overlapping portion of the first real-time map and the second real-time map in the coordinate system, and transmit with the overlapping portion to the aerial vehicle.)
To the extent Wang is silent regarding or does not teach: wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output; Guney does teach:
wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output (Guney: ¶ 025; learning-based module 126 uses this data, and applies learning-based algorithms to predict the changes in the traffic in a manner that detects whether there is a presence of traffic congestion at some portion of the roadway. The learning-based module 126 can be implemented in accordance with one of several known learning-based techniques, such as machine-learning (ML), artificial intelligence (AI), neural network, deep learning, and the like.) (Guney: ¶ 004; cooperative traffic congestion detection methods and systems are implemented that enhance the accuracy of detecting traffic congestion for enhanced routing and maneuvering vehicles along a travel route. In an embodiment, a vehicle is configured to receive data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, and vehicle movement, traffic, and the like, within the vicinity along the route.) (Guney: ¶ 003; systems can be crucial in solving such problems, and can allow for drivers and/or vehicles to make the right adjustments to make congestion easy to manage and reduce injuries.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Guney with the teachings of Wang because doing so would result in the predicable benefit of “improve[d] accuracy of traffic detection by leveraging vehicle connectivity, consensus, and the availability of vehicles with advanced sensors.” (Guney]: ¶ 039).
Regarding claim 15 as detailed above, combination Wang teaches the invention as detailed with respect to claim 1. Wang does not explicitly teach:
wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output based on the first raw sensor information and the second raw sensor information; however, Guney does teach:
wherein the land vehicle processor is further configured to execute a neural network configured to generate coordinated planning results as output based on the first raw sensor information and the second raw sensor information. (Guney: ¶ 025; learning-based module 126 uses this data, and applies learning-based algorithms to predict the changes in the traffic in a manner that detects whether there is a presence of traffic congestion at some portion of the roadway. The learning-based module 126 can be implemented in accordance with one of several known learning-based techniques, such as machine-learning (ML), artificial intelligence (AI), neural network, deep learning, and the like.) (Guney: ¶ 004; cooperative traffic congestion detection methods and systems are implemented that enhance the accuracy of detecting traffic congestion for enhanced routing and maneuvering vehicles along a travel route. In an embodiment, a vehicle is configured to receive data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, and vehicle movement, traffic, and the like, within the vicinity along the route.) (Guney: ¶ 003; systems can be crucial in solving such problems, and can allow for drivers and/or vehicles to make the right adjustments to make congestion easy to manage and reduce injuries.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Guney with the teachings of Wang because doing so would result in the predicable benefit of “improve[d] accuracy of traffic detection by leveraging vehicle connectivity, consensus, and the availability of vehicles with advanced sensors.” (Guney]: ¶ 039).
Response to Arguments
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive. Applicant argues that
Kim does not disclose "acceleration" (and thus does not support "motion information includes acceleration.") Instead, the passages Kim relies on only describe traffic conditions such as (i) congestion/free-flowing traffic, (ii) obstructions, (iii) number of cars, and (iv) speed (i.e., "the speed in which the cars are traveling"). Kim's examples likewise discuss speeds such as "15 mile-per-hour-moving-traffic" and "30 miles per hour," not acceleration parameters. Accordingly, Kim fails to teach or suggest determining or using "acceleration information" of ground objects, as required by the claim language. Furthermore, Kim's "recalculating" is not "based on motion information including acceleration." To the extent the Office Action characterizes Kim's route recalculation as being "based on motion information including acceleration," that characterization is not supported by Kim's disclosure. Kim expressly ties "recalculating" to: using "area map data" and "sensor data obtained from the UAV" indicative of traffic conditions/obstructions, and determining how to change the route "by altering the route and changing traffic conditions." None of these passages describes acceleration-derived motion models, nor do they describe recalculation based on "acceleration information" of moving objects. Therefore, even if Wang is combined with Kim, the combination still fails to teach or render obvious the claimed requirement that the "motion information includes acceleration information ..." and that the relevant determination/adjustment is made based on such motion information. (Applicant’s Arguments filed May 26, 2026, pgs. 18-19).
Examiner respectfully disagrees and notes that the claimed limitation contains broad language “acceleration information” which is interpreted to mean any information related to the acceleration. In particular Kim teaches that “sensor data may include . . . conditions ahead . . . conditions may include free-flowing traffic, slow-moving and/or congested traffic, a congested or a free-flowing intersection” and that analysis of that traffic can consider a “pole partially or fully blocking the road, a dead animal lying on the road and partially or fully blocking the road” and that such data can be used when a “calculated route can be changed to shorten a response time of the emergency vehicle to the location of the emergency by altering the route and changing traffic conditions along the altered route. The recalculating of the route in operation S27 may further include determining how the calculated route can be changed to shorten the response time of the emergency vehicle. In this case, the traffic alert transmission may be performed for the route that corresponds to the shortened response time.” Kim: ¶ 040-049, etc. In other words Kim is explicitly teaching evaluating if traffic has slowed or stopped on some particular routes previously assigned as “fastest” and redirecting the emergency vehicle based on that change on that change in sensor-measured velocity. Sensor-measured velocity is a acceleration information. Kim could not re-route based on new sensor-derived information if it did not consider the change in acceleration (e.g. the change in measured velocity between the original routing and the later routing) because total predicted travel time would not change. Therefore, Wang in view of Kim does teach "’motion information includes acceleration information ...’" and that the relevant determination/adjustment is made based on such motion information (Applicant’s Arguments filed May 26, 2026, pgs. 18-19.) and Applicant’s arguments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Alvarez et al. (US 20200026289 A1) which discloses an autonomous driving environment where vehicles may be provided with varying levels of autonomous driving capabilities facilitated through in-vehicle computing systems with logic implemented in hardware, firmware, and/or software to enable respective autonomous driving stacks. Such autonomous driving stacks may allow vehicles to self-control or provide driver assistance to detect roadways, navigate from one point to another, detect other vehicles and road actors (e.g., pedestrians (e.g., 135), bicyclists, etc.), detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions, weather conditions, etc.), and adjust control and guidance of the vehicle accordingly.
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/C.P./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
