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
This action is reply to the Application Number 18/382,756 filed on 09/11/2025
Claims 1 – 10 are currently pending and have been examined. Claims 1, 6 and 7 have been amended. Claims 8 – 10 are new
This action is made FINAL
Examiner’s Note
Examiner states the previous prior art of Asakura et al. (JP 2017007572 A) was incorrectly stated as Shinji et al. (JP 2015126558). All cited paragraphs and motivation to combine for the 35 U.S.C. 103 prior art rejections in the previous office action were only using Asakura, hence only the identified reference was incorrect. Applicant stated this error in the REMARKS filed 09/11/2025. The current office action is updated to show this change along with the submitted PTO-892 Notice of References Cited.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 10 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.
Claim 1 states: “A vehicle controller comprising:
a memory configured to store a first map and a second map each representing information on a road, the first and second maps being updated at different timings; and
a processor configured to:
detect, in a travel direction of a vehicle, an inconsistent section where information on a road represented in the first map is inconsistent with information on the road represented in the second map, the road being traveled by the vehicle,
generate a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map,
generate the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section,[[ and]]
determine whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section,
upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves, and
control the vehicle so that the vehicle travels along the planned trajectory.” , however it is indefinite on how a planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section. When the generated planned trajectory is created regarding the inconsistent section based on a map, how is it possible that the planned trajectory is switched between the inconsistent section and the sections in front and behind of the inconsistent section? The generated planned trajectory is claimed to be based on a map with a shorter elapsed time, thus the planned trajectory is generated without the need of the switching to sections in front and behind of the inconsistent section. Due to these reasons the claim limitation will be interpreted as the map which is not used to create the planned trajectory due to the inconsistent section, is used to identify known sections of the roadway in front and behind of the inconsistent section. Claims 6 and 7 state the same indefiniteness and therefore rejected under the same pretenses.
Dependent claims 2 – 5 and 8 are rejected as being dependent upon claim 1. Dependent claim 9 is rejected as being dependent upon claim 6. Dependent claim 10 is rejected as being dependent upon claim 7.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 2 and 4 – 10 are rejected under 35 U.S.C. 103 as being unpatentable over Asakura et al. (JP 2017007572) in view De Castro et al. (US 10809734 B2) and Shashua et al. (US 11378958 B2).
Regarding claim 1, Asakura teaches a vehicle controller comprising: a memory (Asakura: Paragraph 0030: “The vehicle control device 100 includes a recognition unit 110, a navigation unit 120, an action planning unit 130, a determination unit 140, a travel control unit 150, and a control switching unit 160. Among these functional units, the recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 are, for example, software functional units that function when a processor such as a CPU ( Central Processing Unit ) included in the vehicle control device 100 executes a program stored in a program memory. The recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 may be hardware functional units such as LSIs ( Large Scale Integration ) or ASICs ( Application Specific Integrated Circuit ). Further, the navigation unit 120 may be realized by a processor separate from other functional units of the vehicle control device 100.”)
configured to store a first map and a second map each representing information on a road, the first and second maps being updated at different timings; and (Asakura: Paragraph 0002: “Conventionally, in an automatic travel guiding device in which a navigation control unit and an automatic travel control unit are combined, there has been proposed an automatic travel guiding device that determines a position on a road on which an own vehicle is traveling from a sensed road situation in a traveling direction of the own vehicle, and corrects a current position of the own vehicle so that the current position of the own vehicle comes to a place of corresponding road map information”; Paragraph 0006: “A second aspect of the present disclosure is the vehicle control device of the first aspect, wherein the determination section determines that the automated driving can be implemented in cases in which the information freshness of the first map and the information freshness of the second map match, and determines that the automated driving cannot be implemented in cases in which the information freshness of the first map and the information freshness of the second map do not match.”; Paragraph 0035: “The navigation-side storage unit 125 is realized by, for example, a readable and writable nonvolatile storage device such as an HDD ( Hard Disk Drive ), a flash memory, or an EEPROM ( Electrically Erasable Programmable Read Only Memory ). The navigation-side storage unit 125 stores a navigation map 126.”; Paragraph 0039: “The action plan-side storage unit 135 stores a high-precision map 136 (second map), a road lane correspondence table, and a version correspondence table 139.”; Paragraph 0084: “The server side route search unit 224 derives a traveling route along which the host vehicle M can arrive at the destination by analyzing the navigation map (first map) 227,”,
Supplemental Note: both maps are interpreted at different times as both are compared for freshness, thus a map updated prior to another would have a lower freshness)
a processor configured to: (Asakura: Paragraph 0030: “The vehicle control device 100 includes a recognition unit 110, a navigation unit 120, an action planning unit 130, a determination unit 140, a travel control unit 150, and a control switching unit 160. Among these functional units, the recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 are, for example, software functional units that function when a processor such as a CPU ( Central Processing Unit ) included in the vehicle control device 100 executes a program stored in a program memory. The recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 may be hardware functional units such as LSIs ( Large Scale Integration ) or ASICs ( Application Specific Integrated Circuit ). Further, the navigation unit 120 may be realized by a processor separate from other functional units of the vehicle control device 100.”)
detect, in a travel direction of a vehicle, an inconsistent section where information on a road represented in the first map is inconsistent with information on the road represented in the second map, the road being traveled by the vehicle, (Asakura: Paragraph 0005: “The invention according to claim 1 is a vehicle control device (100) including an action plan generation unit (130) that generates an action plan of automated driving that performs at least a part of drive control, braking control, or steering control on the basis of a traveling route of a vehicle determined on the basis of a first map (126) and a second map (136) including more detailed information than the first map, and a determination unit (140) that compares information freshness of the first map with information freshness of the second map and determines whether or not the automated driving can be performed on the basis of a comparison result.”; Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”)
determine whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section, upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections)
… control the vehicle so that the vehicle travels along the planned trajectory. (Asakura: Paragraph 0060: “coincide with each other, the determination unit 140 sets the mesh region including the travel route determined in the step S102 as a permitted region for autonomous driving (step S110).”)
In sum, Asakura teaches a vehicle controller comprising: a memory configured to store a first map and a second map each representing information on a road, the first and second maps being updated at different timings; and a processor configured to: detect, in a travel direction of a vehicle, an inconsistent section where information on a road represented in the first map is inconsistent with information on the road represented in the second map, the road being traveled by the vehicle, determine whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section, upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, control the vehicle so that the vehicle travels along the planned trajectory. Asakura however does not teach to generate a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map, generate the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section whereas De Castro does.
De Castro teaches generate a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map,
generate the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section, and (De Castro: Col. 1, line 63 – Col. 2, line 19: “Adjusting the content of the cost map may include adding a weight to a cost of a route among the one or more routes, with a greater weight being indicative of a less desirable route. The one or more routes may include at least two routes that have different costs. The computing system may be configured to select one of the at least two routes having a least cost for the autonomous device. The computing system may be configured to select one of the at least two routes for the autonomous device based on a cost of the one of the at least two routes. The cost may be based at least in part on one or more of the following: a length of the one of the at least two routes, an inclination of the one of the at least two routes, a material associated with the one of the at least two routes, or a width of the one of the at least two routes. Greater costs may be assigned to longer routes. A route among the one or more routes may include a segment along a path between the first location and the second location. The segment may contain an entirety of the path between two locations on the path. A route among the one or more routes may include a zone along a path between the first location and the second location. The zone may contain less than an entirety of the path between two locations on the path.”; Col. 10, lines 29 – 39: “This cost map is available to each robot and is usable by each robot to implement route planning—for example, to plan a best route from a current position to a destination by selecting edges or routes from the cost map having the least cost. This cost associated with a particular route through the cost map is thus affected by both the initial parameters used to define the cost map and by dynamic adjustments to the cost map generated using input from this static and dynamic detectors, such as those in the vision system.”; Col. 4, lines 11 – 13: “FIG. 6 is a flowchart showing an example process that is part of a route planning process and that includes adjusting a cost map.”,
Supplemental Note: the system is able to navigate to a destination with the path with the on the cost map. An inconsistent section is interpreted as a section of the route with a less desirable cost. The two maps are interpreted as the cost map being the claimed first map and the sensors/detectors are able to detect it’s environment and placements of obstacles as the second map)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. De Castro teaches the ability to navigate per a cost map in within an environment and update the cost map per the detected obstacles from sensors and detectors to change the weights of the routes based on that observation. These costs are used to determine the route with the lowest cost. One with knowledge in the art would find it obvious to try to implement this function as taught by De Castro with the vehicle system of Asakura. For example, the cost map not showing an obstacle in the travel route while the detectors have detected an object as taught by De Castro is interpreted as a travel route on the high-precision map and navigational map of Asakura being compared to determine their freshness. One with knowledge in the art would find these as simple substitution functions as both are determining a route from one location to another while comparing two sets of environmental data. Asakura in view of De Castro is able compare two environmental data sources for route planning and able to determine a path with the lowest cost, thus to one with knowledge in the art, increasing the efficiency of the vehicle system of Asakura. Asakura in view of De Castro however still do not teach connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves whereas Shashua does.
Shashua teaches connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves, and (Shashua: Col. 3, lines 40 – 53: “In some embodiments, an autonomous vehicle may include a body; and a non-transitory computer-readable medium that may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer. The autonomous vehicle may include a processor configured to execute data included in the sparse map for providing autonomous vehicle navigation along the road segment.”: Col. 76, lines 19 – 39: “The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment may be represented by a curve in three dimensional space, which may be a spline connecting three dimensional polynomials. The reconstructed trajectory curve may be determined from analysis of a video stream or a plurality of images captured by a camera installed on the vehicle. In some embodiments, a location is identified in each frame or image that is a few meters ahead of the current position of the vehicle. This location is where the vehicle is expected to travel to in a predetermined time period. This operation may be repeated frame by frame, and at the same time, the vehicle may compute the camera's ego motion (rotation and translation). At each frame or image, a short range model for the desired path is generated by the vehicle in a reference frame that is attached to the camera. The short range models may be stitched together to obtain a three dimensional model of the road in some coordinate frame, which may be an arbitrary or predetermined coordinate frame. The three dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of suitable orders.”,
Supplemental Note: the vehicles in the system are able to generate their own trajectory along a sparse map by known landmarks and images of the camera. The inconsistent sections between known landmarks are generated)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of Shashua with a reasonable expectation of success. Shashua teaches the ability to generate a trajectory of an inconsistent roadway based on the landmarks of the sparse map and the vehicle itself gathering roadway data. One with knowledge in the art would find it obvious to try to implement this function of Shashua with the vehicle system of Asakura to improve the ability to gather accurate data about the roadways. For example, if the freshness of the maps used by Asakura are still showing inconsistent data, the system of Shashua can be implemented to improve the maps with generated trajectories per the landmarks and the vehicle data. This gathers an improved representation of the roadway which even with the updated freshness of the map data to still be inconsistent.
Regarding claim 2, Asakura, as modified, teaches wherein the information on the road represented in the first map is more accurate than the information on the road represented in the second map, (Asakura: Paragraph 0040: “The high-precision map 136 is a map including more detailed information than the navigation map 126. The high-precision map 136 is referred to when the action plan generation unit 132 generates an action plan. The high-precision map 136 includes a lane layer and a target object layer. In addition, version information indicating freshness of information is associated with the high-precision map 136 (see FIG. 8 described later). Each time the high-precision map 136 is updated, the version information of the high-precision map 136 is stored in the action plan-side storage unit 135 as information indicating the update status.”)
and the second map is updated (Asakura: Paragraph 0033: “The route search unit 122 derives a traveling route from the current position of the host vehicle M to a destination input by a user such as a driver while referring to a navigation map (first map) 126 stored in the navigation-side storage unit 125.”; Paragraph 0035: “The navigation map 126 is composed of road layers. The road layer includes a road node table 127, a road link table 128, a POI ( Point Of Interest ; point information (coordinate point) of a target such as various facilities), information on the POI, a link cost, and the like. The various facilities of the POI are, for example, a signal, a sign, a building, a signboard, and the like. Further, the information on the POI is the content of a sign, the name of a building, or the like. The building and the signboard are targets that do not affect the automated driving. The link cost is information indicating the shortest travel route from a certain point to another certain point. Further, the navigation map 126 is associated with version information indicating information freshness (see FIG. 8 described later). Each time the navigation map 126 is updated, the version information of the navigation map 126 is stored in the navigation-side storage unit 125 as information indicating the update status. In the present embodiment, the coordinate point is, for example, latitude and longitude.”)
In sum, Asakura teaches wherein the information on the road represented in the first map is more accurate than the information on the road represented in the second map and a second map. Asakura however does not teach the second map is updated more frequently than the first map whereas De Castro does.
De Castro teaches the second map (taught by Asakura) is updated more frequently than the first map. (De Castro: Col. 4, lines 33 – 42: “An example system includes one or more autonomous devices having one or more receivers for receiving route planning updates via a control system. The control system may control the autonomous devices in order to enable the autonomous devices to choose a route through a dynamically changing environment. These route planning updates may be obtained from distributed cameras or other detectors that provide information about local visual elements that may be used to update a cost map dynamically.”; Col. 13, lines 49 – 61: “In some implementations, the detectors include one or more processing devices to execute computer vision processes trained to detect a set of object classes (known to exist in the current location), their movement direction, their movement speed, and their observation frequency, and to report this information to the control system. The control system is configured to use this information to make the decisions regarding route planning. In some implementations, the control system dynamically adjusts cost maps that are used to decide which route a robot or robots are to take. By changing the cost of a specific route in the cost map, a longer but unobstructed path can become more favorable.”,
Supplemental Note: the control system plans a route using a cost map which is updated with the detectors and sensors that gather information to update the cost map, thus more frequently updated that the cost map)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. As discussed in claim 1, De Castro teaches the ability to navigate per a cost map in within an environment and update the cost map per the detected obstacles from sensors and detectors to change the weights of the routes based on that observation. These costs are used to determine the route with the lowest cost. One with knowledge in the art would find it obvious to try to implement this function as taught by De Castro with the vehicle system of Asakura. For example, the cost map not showing an obstacle in the travel route while the detectors have detected an object as taught by De Castro is interpreted as a travel route on the high-precision map and navigational map of Asakura being compared to determine their freshness. One with knowledge in the art would find these as simple substitution functions as both are determining a route from one location to another while comparing two sets of environmental data. Furthermore, the second data is gathered more frequently as taught by De Castro by detectors in the environment, thus increasing the accuracy of the environment and aid in improving the current route based on this determination. For these reasons, one with knowledge in the art would find it obvious to try to combine this function of De Castro with the vehicle system of Asakura.
Regarding claim 4, Asakura, as modified, does not teach wherein the processor generates the planned trajectory from a point that is a predetermined offset distance closer to the current position of the vehicle than a start point of the inconsistent section closest to the current position of the vehicle in the inconsistent section, based on the map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section whereas De Castro does.
De Castro teaches wherein the processor generates the planned trajectory from a point that is a predetermined offset distance closer to the current position of the vehicle than a start point of the inconsistent section closest to the current position of the vehicle in the inconsistent section, based on the map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section. (De Castro: Col. 1, line 63 – Col. 2, line 19: “Adjusting the content of the cost map may include adding a weight to a cost of a route among the one or more routes, with a greater weight being indicative of a less desirable route. The one or more routes may include at least two routes that have different costs. The computing system may be configured to select one of the at least two routes having a least cost for the autonomous device. The computing system may be configured to select one of the at least two routes for the autonomous device based on a cost of the one of the at least two routes. The cost may be based at least in part on one or more of the following: a length of the one of the at least two routes, an inclination of the one of the at least two routes, a material associated with the one of the at least two routes, or a width of the one of the at least two routes. Greater costs may be assigned to longer routes. A route among the one or more routes may include a segment along a path between the first location and the second location. The segment may contain an entirety of the path between two locations on the path. A route among the one or more routes may include a zone along a path between the first location and the second location. The zone may contain less than an entirety of the path between two locations on the path.”; Col. 4, lines 33 – 42: “An example system includes one or more autonomous devices having one or more receivers for receiving route planning updates via a control system. The control system may control the autonomous devices in order to enable the autonomous devices to choose a route through a dynamically changing environment. These route planning updates may be obtained from distributed cameras or other detectors that provide information about local visual elements that may be used to update a cost map dynamically.”; Col. 10, lines 29 – 39: “This cost map is available to each robot and is usable by each robot to implement route planning—for example, to plan a best route from a current position to a destination by selecting edges or routes from the cost map having the least cost. This cost associated with a particular route through the cost map is thus affected by both the initial parameters used to define the cost map and by dynamic adjustments to the cost map generated using input from this static and dynamic detectors, such as those in the vision system.”; Col. 4, lines 11 – 13: “FIG. 6 is a flowchart showing an example process that is part of a route planning process and that includes adjusting a cost map.”,
Supplemental Note: the system is able to navigate to a destination with the path with the on the cost map. An inconsistent section is interpreted as a section of the route with a less desirable cost. The two maps are interpreted as the cost map being the claimed first map and the sensors/detectors are able to detect it’s environment and placements of obstacles as the second map. The claimed “predetermined offset distance closer to the current position of the vehicle than a start point of the inconsistent section closest to the current position of the vehicle in the inconsistent section” is the vehicle not going on a path that has the obstacle (inconsistent section) thus increasing the value of that route in the cost map, therefore the planned trajectory from the starting point is of a path with the lowest cost)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. As discussed in claim 1, De Castro teaches the ability to navigate per a cost map in within an environment and update the cost map per the detected obstacles from sensors and detectors to change the weights of the routes based on that observation. These costs are used to determine the route with the lowest cost. One with knowledge in the art would find it obvious to try to implement this function as taught by De Castro with the vehicle system of Asakura. For example, the cost map not showing an obstacle in the travel route while the detectors have detected an object as taught by De Castro is interpreted as a travel route on the high-precision map and navigational map of Asakura being compared to determine their freshness. One with knowledge in the art would find these as simple substitution functions as both are determining a route from one location to another while comparing two sets of environmental data. Asakura in view of De Castro is able compare two environmental data sources for route planning and able to determine a path with the lowest cost, thus to one with knowledge in the art, increasing the efficiency of the vehicle system of Asakura.
Regarding claim 5, Asakura, as modified, does not teach wherein when two inconsistent sections separated by a distance less than a predetermined distance threshold are detected, the processor determines a continuous section including the two inconsistent sections as a single inconsistent section whereas De Castro does.
De Castro teaches wherein when two inconsistent sections separated by a distance less than a predetermined distance threshold are detected, the processor determines a continuous section including the two inconsistent sections as a single inconsistent section. (De Castro: Col. 5, lines 34 – 47: “The autonomous device may also include one or more detectors for continuously detecting and calculating distances between the devices and static or dynamic objects in a vicinity of the device. This is done in order to avoid collision and to guide the device safely around or between detected objects along a route. While the autonomous device is moving along a route, the on-board computing system may continuously receive input from the detectors. If an obstacle is blocking the trajectory of the autonomous device, the on-board computing system is configured to plan a path around the obstacle. If an obstacle is predicted to block the trajectory of the autonomous device, the on-board computing system is configured to plan a path around the obstacle. These operations are referred to as local planning.”; Col. 7, lines 34 – 46: “FIG. 4 shows an example of a cost map 40. In cost map 40, there are no objects in the path of a robot. Accordingly, costs—in this example, weights—associated with segments of routes are all labeled “1”. In this example, each segment is a straight line connecting two additional segments. In some examples, greater costs are indicative of less desirable segments. In the example of FIG. 4, the cost of a route may be determined by adding the weights of segments along a route. The greater the cost, the less desirable the route may be. Although weights are described herein in the context of objects, weights may be added to routes based on other factors, such as their length, their difficulty to traverse, their width, and so forth.”; Col. 7, lines 47 – 52: “FIG. 5 shows an example of a cost map 41 having a weight on a segment 42 of route 43 adjusted. In this example, the weight is increased to “10”. This may indicate the presence of an object in that route. The increase in cost along route 43 containing segment 42 may make that route less desirable and therefore less likely to be selected by a control system when planning robot movement between “start” and “goal”.”,
Supplemental Note: the system of De Castro views a specific vicinity in which it is able to plan a route from one point to another. The system then determines areas of a higher cost, such as areas as objects and selects the route with the lowest cost. The “predetermined distance threshold” is interpreted as the vicinity the objects are placed in as they all get this higher cost, thus the system determines a route with the lowest cost to travel on while the other paths are all treated as routes with higher costs, interpreted as a single inconsistent section)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. De Castro teaches the ability to increase the cost of a route impacted by an object within a vicinity wherein the cost of the route increases based on at least an object along the route. The system determines these sections as higher cost sections, thus interpreted as treating these sections as the same inconsistent section while the lowest cost route is determined to be one it takes. This function in combination with the system of Asakura allows for a vehicle to determine sections with higher cost (areas where the first and second map are compared and determined inconsistent) with sections of lower costs (areas where the first and second map are compared and match), thus being able to filter out areas with higher costs when routing and determine a route with the lowest cost. This allows for better vehicle route determination as a multiple inconsistencies or obstructions can be in the path of multiple routes, the ability to select the one with the lowest cost, the most desirable route as taught by De Castro, increases the efficiency of Asakura’s vehicle system. Due to these reasons, one with knowledge in the art would find it obvious to try to combine the teachings of Asakura with De Castro.
Regarding claim 6, Asakura teaches a method for vehicle control, comprising: (Asakura: Paragraph 0030: “The vehicle control device 100 includes a recognition unit 110, a navigation unit 120, an action planning unit 130, a determination unit 140, a travel control unit 150, and a control switching unit 160. Among these functional units, the recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 are, for example, software functional units that function when a processor such as a CPU ( Central Processing Unit ) included in the vehicle control device 100 executes a program stored in a program memory. The recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 may be hardware functional units such as LSIs ( Large Scale Integration ) or ASICs ( Application Specific Integrated Circuit ). Further, the navigation unit 120 may be realized by a processor separate from other functional units of the vehicle control device 100.”)
detecting, in a travel direction of a vehicle, an inconsistent section where information on a road represented in a first map is inconsistent with information on the road represented in a second map, the road being traveled by the vehicle, (Asakura: Paragraph 0005: “The invention according to claim 1 is a vehicle control device (100) including an action plan generation unit (130) that generates an action plan of automated driving that performs at least a part of drive control, braking control, or steering control on the basis of a traveling route of a vehicle determined on the basis of a first map (126) and a second map (136) including more detailed information than the first map, and a determination unit (140) that compares information freshness of the first map with information freshness of the second map and determines whether or not the automated driving can be performed on the basis of a comparison result.”; Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”)
the first and second maps being updated at different timings; (Asakura: Paragraph 0002: “Conventionally, in an automatic travel guiding device in which a navigation control unit and an automatic travel control unit are combined, there has been proposed an automatic travel guiding device that determines a position on a road on which an own vehicle is traveling from a sensed road situation in a traveling direction of the own vehicle, and corrects a current position of the own vehicle so that the current position of the own vehicle comes to a place of corresponding road map information”; Paragraph 0006: “A second aspect of the present disclosure is the vehicle control device of the first aspect, wherein the determination section determines that the automated driving can be implemented in cases in which the information freshness of the first map and the information freshness of the second map match, and determines that the automated driving cannot be implemented in cases in which the information freshness of the first map and the information freshness of the second map do not match.”; Paragraph 0035: “The navigation-side storage unit 125 is realized by, for example, a readable and writable nonvolatile storage device such as an HDD ( Hard Disk Drive ), a flash memory, or an EEPROM ( Electrically Erasable Programmable Read Only Memory ). The navigation-side storage unit 125 stores a navigation map 126.”; Paragraph 0039: “The action plan-side storage unit 135 stores a high-precision map 136 (second map), a road lane correspondence table, and a version correspondence table 139.”; Paragraph 0084: “The server side route search unit 224 derives a traveling route along which the host vehicle M can arrive at the destination by analyzing the navigation map (first map) 227,”,
Supplemental Note: both maps are interpreted at different times as both are compared for freshness, thus a map updated prior to another would have a lower freshness)
determining whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section; upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections)
… controlling the vehicle so that the vehicle travels along the planned trajectory. (Asakura: Paragraph 0060: “coincide with each other, the determination unit 140 sets the mesh region including the travel route determined in the step S102 as a permitted region for autonomous driving (step S110).”)
In sum, Asakura teaches a method for vehicle control, comprising: detecting, in a travel direction of a vehicle, an inconsistent section where information on a road represented in a first map is inconsistent with information on the road represented in a second map, the road being traveled by the vehicle, the first and second maps being updated at different timings; determining whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section; upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched and controlling the vehicle so that the vehicle travels along the planned trajectory. Asakura however does not teach generating a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map; generating the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section whereas De Castro does.
De Castro teaches generating a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map;
generating the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section; and (De Castro: Col. 1, line 63 – Col. 2, line 19: “Adjusting the content of the cost map may include adding a weight to a cost of a route among the one or more routes, with a greater weight being indicative of a less desirable route. The one or more routes may include at least two routes that have different costs. The computing system may be configured to select one of the at least two routes having a least cost for the autonomous device. The computing system may be configured to select one of the at least two routes for the autonomous device based on a cost of the one of the at least two routes. The cost may be based at least in part on one or more of the following: a length of the one of the at least two routes, an inclination of the one of the at least two routes, a material associated with the one of the at least two routes, or a width of the one of the at least two routes. Greater costs may be assigned to longer routes. A route among the one or more routes may include a segment along a path between the first location and the second location. The segment may contain an entirety of the path between two locations on the path. A route among the one or more routes may include a zone along a path between the first location and the second location. The zone may contain less than an entirety of the path between two locations on the path.”; Col. 10, lines 29 – 39: “This cost map is available to each robot and is usable by each robot to implement route planning—for example, to plan a best route from a current position to a destination by selecting edges or routes from the cost map having the least cost. This cost associated with a particular route through the cost map is thus affected by both the initial parameters used to define the cost map and by dynamic adjustments to the cost map generated using input from this static and dynamic detectors, such as those in the vision system.”; Col. 4, lines 11 – 13: “FIG. 6 is a flowchart showing an example process that is part of a route planning process and that includes adjusting a cost map.”,
Supplemental Note: the system is able to navigate to a destination with the path with the on the cost map. An inconsistent section is interpreted as a section of the route with a less desirable cost. The two maps are interpreted as the cost map being the claimed first map and the sensors/detectors are able to detect it’s environment and placements of obstacles as the second map)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. Please refer to the rejection of claim 1 as both state the same functional language and therefore rejected under the same pretenses. Asakura in view of De Castro however still do not teach connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves whereas Shashua does.
Shashua teaches connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves; and (Shashua: Col. 3, lines 40 – 53: “In some embodiments, an autonomous vehicle may include a body; and a non-transitory computer-readable medium that may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer. The autonomous vehicle may include a processor configured to execute data included in the sparse map for providing autonomous vehicle navigation along the road segment.”: Col. 76, lines 19 – 39: “The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment may be represented by a curve in three dimensional space, which may be a spline connecting three dimensional polynomials. The reconstructed trajectory curve may be determined from analysis of a video stream or a plurality of images captured by a camera installed on the vehicle. In some embodiments, a location is identified in each frame or image that is a few meters ahead of the current position of the vehicle. This location is where the vehicle is expected to travel to in a predetermined time period. This operation may be repeated frame by frame, and at the same time, the vehicle may compute the camera's ego motion (rotation and translation). At each frame or image, a short range model for the desired path is generated by the vehicle in a reference frame that is attached to the camera. The short range models may be stitched together to obtain a three dimensional model of the road in some coordinate frame, which may be an arbitrary or predetermined coordinate frame. The three dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of suitable orders.”,
Supplemental Note: the vehicles in the system are able to generate their own trajectory along a sparse map by known landmarks and images of the camera. The inconsistent sections between known landmarks are generated)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of Shashua with a reasonable expectation of success. Please refer to the rejection of claim 1 as both state the same functional language and therefore rejected under the same pretenses.
Regarding claim 7, Asakura teaches a non-transitory recording medium that stores a computer program for vehicle control, the computer program causing a processor mounted on a vehicle to execute a process comprising: (Asakura: Paragraph 0030: “The vehicle control device 100 includes a recognition unit 110, a navigation unit 120, an action planning unit 130, a determination unit 140, a travel control unit 150, and a control switching unit 160. Among these functional units, the recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 are, for example, software functional units that function when a processor such as a CPU ( Central Processing Unit ) included in the vehicle control device 100 executes a program stored in a program memory. The recognition unit 110, the navigation unit 120, the action planning unit 130, the determination unit 140, the travel control unit 150, and the control switching unit 160 may be hardware functional units such as LSIs ( Large Scale Integration ) or ASICs ( Application Specific Integrated Circuit ). Further, the navigation unit 120 may be realized by a processor separate from other functional units of the vehicle control device 100.”)
detecting, in a travel direction of the vehicle, an inconsistent section where information on a road represented in a first map is inconsistent with information on the road represented in a second map, the road being traveled by the vehicle, (Asakura: Paragraph 0005: “The invention according to claim 1 is a vehicle control device (100) including an action plan generation unit (130) that generates an action plan of automated driving that performs at least a part of drive control, braking control, or steering control on the basis of a traveling route of a vehicle determined on the basis of a first map (126) and a second map (136) including more detailed information than the first map, and a determination unit (140) that compares information freshness of the first map with information freshness of the second map and determines whether or not the automated driving can be performed on the basis of a comparison result.”; Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”)
the first and second maps being updated at different timings; (Asakura: Paragraph 0002: “Conventionally, in an automatic travel guiding device in which a navigation control unit and an automatic travel control unit are combined, there has been proposed an automatic travel guiding device that determines a position on a road on which an own vehicle is traveling from a sensed road situation in a traveling direction of the own vehicle, and corrects a current position of the own vehicle so that the current position of the own vehicle comes to a place of corresponding road map information”; Paragraph 0006: “A second aspect of the present disclosure is the vehicle control device of the first aspect, wherein the determination section determines that the automated driving can be implemented in cases in which the information freshness of the first map and the information freshness of the second map match, and determines that the automated driving cannot be implemented in cases in which the information freshness of the first map and the information freshness of the second map do not match.”; Paragraph 0035: “The navigation-side storage unit 125 is realized by, for example, a readable and writable nonvolatile storage device such as an HDD ( Hard Disk Drive ), a flash memory, or an EEPROM ( Electrically Erasable Programmable Read Only Memory ). The navigation-side storage unit 125 stores a navigation map 126.”; Paragraph 0039: “The action plan-side storage unit 135 stores a high-precision map 136 (second map), a road lane correspondence table, and a version correspondence table 139.”; Paragraph 0084: “The server side route search unit 224 derives a traveling route along which the host vehicle M can arrive at the destination by analyzing the navigation map (first map) 227,”,
Supplemental Note: both maps are interpreted at different times as both are compared for freshness, thus a map updated prior to another would have a lower freshness)
determining whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section; upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections)
… controlling the vehicle so that the vehicle travels along the planned trajectory. (Asakura: Paragraph 0060: “coincide with each other, the determination unit 140 sets the mesh region including the travel route determined in the step S102 as a permitted region for autonomous driving (step S110).”)
In sum, Asakura teaches a non-transitory recording medium that stores a computer program for vehicle control, the computer program causing a processor mounted on a vehicle to execute a process comprising: detecting, in a travel direction of the vehicle, an inconsistent section where information on a road represented in a first map is inconsistent with information on the road represented in a second map, the road being traveled by the vehicle, the first and second maps being updated at different timings; determining whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section; upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched and controlling the vehicle so that the vehicle travels along the planned trajectory. Asakura however does not teach generating a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map; generating the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section whereas De Castro does.
De Castro teaches generating a planned trajectory to be traveled by the vehicle regarding a section from a current position of the vehicle to a predetermined distance away in the travel direction of the vehicle except the inconsistent section, based on the first map;
generating the planned trajectory regarding the inconsistent section, based on a map, of the first and second maps, with a shorter elapsed time since the timing of the last update of information on the road in the inconsistent section; and (De Castro: Col. 1, line 63 – Col. 2, line 19: “Adjusting the content of the cost map may include adding a weight to a cost of a route among the one or more routes, with a greater weight being indicative of a less desirable route. The one or more routes may include at least two routes that have different costs. The computing system may be configured to select one of the at least two routes having a least cost for the autonomous device. The computing system may be configured to select one of the at least two routes for the autonomous device based on a cost of the one of the at least two routes. The cost may be based at least in part on one or more of the following: a length of the one of the at least two routes, an inclination of the one of the at least two routes, a material associated with the one of the at least two routes, or a width of the one of the at least two routes. Greater costs may be assigned to longer routes. A route among the one or more routes may include a segment along a path between the first location and the second location. The segment may contain an entirety of the path between two locations on the path. A route among the one or more routes may include a zone along a path between the first location and the second location. The zone may contain less than an entirety of the path between two locations on the path.”; Col. 10, lines 29 – 39: “This cost map is available to each robot and is usable by each robot to implement route planning—for example, to plan a best route from a current position to a destination by selecting edges or routes from the cost map having the least cost. This cost associated with a particular route through the cost map is thus affected by both the initial parameters used to define the cost map and by dynamic adjustments to the cost map generated using input from this static and dynamic detectors, such as those in the vision system.”; Col. 4, lines 11 – 13: “FIG. 6 is a flowchart showing an example process that is part of a route planning process and that includes adjusting a cost map.”,
Supplemental Note: the system is able to navigate to a destination with the path with the on the cost map. An inconsistent section is interpreted as a section of the route with a less desirable cost. The two maps are interpreted as the cost map being the claimed first map and the sensors/detectors are able to detect it’s environment and placements of obstacles as the second map)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of De Castro with a reasonable expectation of success. Please refer to the rejection of claim 1 as both state the same functional language and therefore rejected under the same pretenses. Asakura in view of De Castro however still do not teach connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves whereas Shashua does.
Shashua teaches connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves; and (Shashua: Col. 3, lines 40 – 53: “In some embodiments, an autonomous vehicle may include a body; and a non-transitory computer-readable medium that may include a sparse map for autonomous vehicle navigation along a road segment. The sparse map may include a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer. The autonomous vehicle may include a processor configured to execute data included in the sparse map for providing autonomous vehicle navigation along the road segment.”: Col. 76, lines 19 – 39: “The geometry of a reconstructed trajectory (and also a target trajectory) along a road segment may be represented by a curve in three dimensional space, which may be a spline connecting three dimensional polynomials. The reconstructed trajectory curve may be determined from analysis of a video stream or a plurality of images captured by a camera installed on the vehicle. In some embodiments, a location is identified in each frame or image that is a few meters ahead of the current position of the vehicle. This location is where the vehicle is expected to travel to in a predetermined time period. This operation may be repeated frame by frame, and at the same time, the vehicle may compute the camera's ego motion (rotation and translation). At each frame or image, a short range model for the desired path is generated by the vehicle in a reference frame that is attached to the camera. The short range models may be stitched together to obtain a three dimensional model of the road in some coordinate frame, which may be an arbitrary or predetermined coordinate frame. The three dimensional model of the road may then be fitted by a spline, which may include or connect one or more polynomials of suitable orders.”,
Supplemental Note: the vehicles in the system are able to generate their own trajectory along a sparse map by known landmarks and images of the camera. The inconsistent sections between known landmarks are generated)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of Shashua with a reasonable expectation of success. Please refer to the rejection of claim 1 as both state the same functional language and therefore rejected under the same pretenses.
Regarding claim 8, Asakura, as modified, teaches wherein the processor detects a road section in which a distance between a position of a predetermined feature represented in the first map and a position of the predetermined feature represented in the second map is equal to or greater than a predetermined threshold as the inconsistent section. (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections. The determination unit differentiates the compared roadway section per a reference value wherein the freshness of the map is identified)
Regarding claim 9, Asakura, as modified, teaches further comprising detecting a road section in which a distance between a position of a predetermined feature represented in the first map and a position of the predetermined feature represented in the second map is equal to or greater than a predetermined threshold as the inconsistent section. (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections. The determination unit differentiates the compared roadway section per a reference value wherein the freshness of the map is identified)
Regarding claim 10, Asakura, as modified, teaches further comprising detecting a road section in which a distance between a position of a predetermined feature represented in the first map and a position of the predetermined feature represented in the second map is equal to or greater than a predetermined threshold as the inconsistent section. (Asakura: Paragraph 0061: “When the information freshness of the navigation map 126 corresponding to the mesh region selected in step S106 does not match the information freshness of the high-precision map 136 corresponding to the mesh region selected in step S106, the determination unit 140 determines whether or not the information freshness related to the items other than the information related to the target that does not affect the autonomous driving matches between the mesh region of the navigation map 126 corresponding to the traveling route determined in step S102 and the mesh region of the high-precision map 136 (step S112).”; Paragraph 0069: “The determination unit 140 may compare the road shape R with the road shape R1 and determine whether or not the information freshness of the navigation map 126 matches the information freshness of the high-precision map 136 on the basis of the comparison result. In this case, for example, in a case where the road shape R and the road shape R1 are superimposed on each other, the determination unit 140 performs the determination depending on whether or not a difference in the direction and the size of the road shape is within a reference value. When the difference in the direction and the size of the road shape is within the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 matches the information freshness of the corresponding road in the high-precision map 136. When the difference in the direction and the size of the road shape exceeds the reference value, the determination unit 140 determines that the information freshness of the corresponding road in the navigation map 126 does not match the information freshness of the corresponding road in the high-precision map 136.”
Supplemental Note: the traveling route is created through evaluating the freshness of the map. The navigation map is evaluated by the high-precision map along different sections such that an inconsistent section can be in between two matching sections. The determination unit differentiates the compared roadway section per a reference value wherein the freshness of the map is identified)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Asakura et al. (JP 2017007572) in view De Castro et al. (US 10809734 B2) and Shashua et al. (US 11378958 B2), as applied to claim 1 above, and further in view of Ostafew et al. (US 20210237769 A1).
Regarding claim 3, Asakura, as modified, does not teach wherein the processor is further configured to connect the planned trajectory generated regarding the inconsistent section and the planned trajectories generated regarding sections in front of and behind the inconsistent section with predetermined curves whereas Ostafew does.
Ostafew teaches wherein the processor is further configured to connect the planned trajectory generated regarding the inconsistent section and the planned trajectories generated regarding sections in front of and behind the inconsistent section with predetermined curves (Ostafew: Paragraph 0011: “FIG. 3 is a diagram of situations of predictable responses according to implementations of this disclosure.”; Paragraph 0031: “As further described below, implementations of a trajectory planner according to this disclosure can generate a smooth trajectory for an autonomous vehicle (AV), from a source location to a destination location, by, for example, receiving HD map data, teleoperation data, and other input data; stitching (e.g., fusing, connecting, etc.) the input data longitudinally to determine a speed profile for a path from the source location to the destination location (e.g., the speed profile specifying how fast the AV can be driven along different segments of the path from the source location to the destination location); and, at discrete time points (e.g., every few milliseconds), having the trajectory planner process constraints related to static and dynamic objects, which are observed based on sensor data of the AV, to generate a smooth trajectory for the AV for the next time window (e.g., a look-ahead time of 6 seconds).”; Paragraph 0067: “FIG. 3 is a diagram of situations 300 of predictable responses according to implementations of this disclosure. The situations 300 include situations 310-360 in which responses of an autonomous vehicle (AV) 302 can be predicted and a trajectory planned”; Paragraph 0070: “In the situation 310, the AV 302 detects (i.e., by the tracking component) a parked car 304 (i.e., a static object) at the side of the road. The AV 302 (i.e., the trajectory planner of the AV 302) can plan a path (i.e., a trajectory), as further described below, that navigates the AV 302 around the parked car 304, as shown by a trajectory 306.”,
Supplemental Note: as seen for Fig. 3, the vehicle is able to determine a predetermined trajectory in avoiding an inconsistent section, such as a vehicle parked on the side of a road, with curves)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Asakura with the teachings of Ostafew with a reasonable expectation of success. Ostafew teaches an example of a vehicle parked along the side of road causing the host vehicle to perform predetermined maneuvers to avoid the vehicle. One with knowledge in the art would find it obvious to try to implement this function of Ostafew with the vehicle system of Asakura. For example, when comparing the navigational map and the high-precision map, the vehicle system can determine areas of which one of the maps states a parked vehicle along the roadway and implement this predetermined maneuver to avoid the vehicle. This combined with autonomous vehicles able to determine their environment in real-time to perform safe vehicle maneuvers as, for example, if the parked vehicle along a highway is not determined, the autonomous vehicle traveling at an high speed would not have that knowledge and make more sudden vehicle maneuver than if the parked vehicle was already known so the autonomous vehicle may limit its speed or be prepared for the vehicle maneuver in that section of the highway. This combination increase the efficiency of the autonomous vehicle as taught by Asakura in how it handles areas of inconsistency.
Response to Arguments
Applicant’s arguments, see section Rejections under 35 U.S.C. 103 of the REMARKS, filed 09/11/2025, with respect to the 35 U.S.C. 103 prior art rejection of claims 1 – 2 and 4 – 7 have been fully considered but are not fully persuasive.
Applicant states per the telephone interview, the amended claim limitations of claims 1, 6 and 7 regarding to “determine whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section, [and] upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched, connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves” overcomes the previous prior art rejection of Asakura in view of De Castro. Regarding the claim limitation of “connect the planned trajectory regarding the inconsistent section and the planned trajectories regarding the sections in front and behind the inconsistent section with predetermined curves,”, examiner agrees is not taught by Asakura in view of De Castro as stated in the interview, however through further search and consideration, the prior art of Shashua does. However the claim limitations of “determine whether the map used for generating the planned trajectory regarding the inconsistent section is switched between the inconsistent section and sections in front and behind the inconsistent section, upon determination that the map used for generating the planned trajectory regarding the inconsistent section is switched,” is taught by Asakura. Asakura teaches the ability to evaluate freshness by evaluating sections of a roadway from a navigation map with a high-precision map (Paragraph 0061). The evaluation consists of superimposing the two areas within the two maps to determine if the comparison result is within a reference value. The comparison result is based upon a difference in direction and size of the road shape (Paragraph 0069). Furthermore, please refer to section Claim Rejections - 35 USC § 112 for the indefiniteness rejection of claims 1, 6 and 7.
Applicant states regarding dependent claim 2 that Asakura in view of De Castro fail to teach updating a frequency of updates for any maps. Examiner respectfully disagrees. An additional citation of De Castro is added to claim 2 in the 35 U.S.C. 103 prior art rejection to state how the cost maps can be updated with an increased observation frequency which is used for route planning (Col. 13, lines 49 – 61). The claimed second map is interpreted as the cost map as taught by De Castro as it is the most updated map and the observation frequency of which the cost map is updated dynamically changes.
Examiner states that the newly added claims of 8 – 10 are also as independent claims 1, 6 and 7 are still rejected per the arguments stated above and in view of Asakura.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/DONALD J WALLACE/Primary Examiner, Art Unit 3665