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 the Claims
The claims 1-13 are currently pending and have been examined. Applicant amended claims 1-5, 8, 12 and 13.
Response to Arguments/Amendments
The amendment filed April 20, 2026 has been entered. Claims 1-13 are currently pending in the Application. Applicant's amendments and arguments regarding the 35 U.S.C. 101 mental process rejection have been fully considered and they are persuasive. As such, the rejection under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments with respect to the limitation “local target state generation circuitry calculating a local target state as a local target state reachable in all of the plurality of target state candidates” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments with respect to the limitation “target state candidate generation circuitry selecting a plurality of target position candidates from a plurality of travelable positions” have been fully considered but they are not persuasive. Applicant argues that Mochida only discloses a singular target position and a temporary target position, and therefore does not teach “selecting a plurality of target position candidates from a plurality of travelable positions.” The Examiner respectfully disagrees. Applicant’s arguments do not fully consider the findings set forth in the rejection. Specifically, the rejection relies on Mochida’s overall route-planning process, including the parking route generation unit, the first and second route generation units, the temporary target position, and the target position, which are generated based on surrounding environment information and vehicle condition information (See paragraph [0044].). The Examiner maintains that, under broadest reasonable interpretation, these teachings reasonably correspond to selecting a plurality of target position candidates from a plurality of travelable positions. Applicant’s argument focuses on whether Mochida discloses a plurality of target destinations. However, the claim does not require the plurality of target position candidates to be target destinations. Accordingly, Mochida does teach the limitation argued.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mochida (US 20190300054 A1) in view of Di Cairano (US 20190391580 A1).
Regarding Claim 1, Mochida teaches A passage point generation apparatus generating a passage point which a moving body should reach, comprising: target state candidate generation circuitry selecting a plurality of target position candidates from a plurality of travelable positions and calculating a plurality of target state candidates including the plurality of target position candidates which have been selected based on surrounding environment information of the moving body and a state amount of the moving body (See at least paragraph [0035], “As illustrated in FIG. 3, the vehicle controller includes a vehicle ECU (electronic control unit) 10 that is mounted on the subject vehicle. The vehicle controller further includes a camera 1, a sonar 2, and a vehicle condition detection sensor 3 that are connected to input side of the vehicle ECU 10. The vehicle controller further includes a steering device 21 connected to output side of the vehicle ECU 10” and paragraph [0044], “The parking route generation unit 13 is a functional block to generate the parking route to park the subject vehicle at the target position without interfering with the obstacles, based on the surroundings information transmitted from the camera 1 and the sonar 2 and the vehicle condition transmitted from the vehicle condition detection sensor 3. The parking route generation unit 13 specifically includes a first route generation unit 131 and a second route generation unit 132. The first route generation unit 131 generates the first route that is a parking route to park the subject vehicle from the current position to the temporary target position, by use of the recognition result transmitted from the temporary target position recognition unit 12. FIG. 7 is a diagram to explain operation to generate the first route. FIG. 7 illustrates the parking route on which the vehicle moves left forward once and then moves right rearward. The first route preferably includes a parking route that causes the subject vehicle to turn at a minimum turning radius.”), wherein the moving body is controlled to reach the local target state (See at least paragraph [0049], “When the routine illustrated in FIG. 9 is started, the target position recognition unit 11 first recognizes the target position where the subject vehicle is to be parked, based on the surroundings information transmitted from the camera 1 and the sonar 2 (step S100). Next, the temporary target position recognition unit 12 recognizes the temporary target position by use of the surroundings information transmitted from the camera 1 and the sonar 2 and the recognition result transmitted from the target position recognition unit 11 (step S102). Next, the first route generation unit 131 of the parking route generation unit 13 generates the first route to park the subject vehicle from the current position to the temporary target position, by use of the recognition result transmitted from the temporary target position recognition unit 12 (step S104). Next, the automatic steering unit 14 automatically operates the steering device 21 such that the subject vehicle follows the first route (step S106).”).
Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches and local target state generation circuitry calculating a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and outputting the local target state as the passage point (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable and selecting the intermediate goal from the computed region, corresponding to calculating a local target state that preserves reachability to the plurality of target state candidates and using the intermediate goal as the next passage point.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].).
Regarding Claim 2, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida teaches wherein the target state candidate generation circuitry generates a set of state amounts in which each of the target states adjacent to each other is within a predetermined range (See at least paragraph [0045], “Further, the second route generation unit 132 generates the second route that is a corrected parking route to park the subject vehicle from the middle of the first route to the target position, by use of the recognition result transmitted from the target position recognition unit 11. FIG. 8 is a diagram to explain operation to generate the second route. FIG. 8 illustrates the parking route on which the vehicle moves rearward from a predetermined designated position in the first route to the target position. The predetermined designated position is a position that allows for generation of the achievable second route. In the example illustrated in FIG. 8, a position where a distance in the +Y direction from the coordinate origin to the center position of the rear wheel axle of the subject vehicle becomes a predetermined distance (e.g., 5000 mm to 6000 mm) or more is set as the designated position”, and paragraph [0046], “Note that the designated position is preferably a position allowing for recognition of the target position with high precision. In other words, the target position can be updated to a more precise position by use of the latest surroundings information obtained while the subject vehicle follows the first route. Therefore, the designated position is set to, for example, a position where the target position can be recognized with high precision in the update operation of the target position. This enables the second route generation unit 132 to generate the second route toward the precise target position.” The system limits target state quantities based on vehicle dynamics and predetermined distance thresholds such that adjacent target states are within a predetermined range.).
Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches so that the moving body can reach all of the plurality of target state candidates when the moving body reaches the local target state (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable, corresponding to the moving body reaching all of the plurality of target state candidates upon reaching the local target state.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; so that the moving body can reach all of the plurality of target state candidates when the moving body reaches the local target state, as taught by Di Cairano (See paragraphs [0087], [0091].), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].).
Regarding Claim 5, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida teaches further comprising global target state generation circuitry generating a more global target state than each target state of the plurality of target state candidates and outputting the target state as a global target state (See at least paragraph [0044], “The parking route generation unit 13 is a functional block to generate the parking route to park the subject vehicle at the target position without interfering with the obstacles, based on the surroundings information transmitted from the camera 1 and the sonar 2 and the vehicle condition transmitted from the vehicle condition detection sensor 3. The parking route generation unit 13 specifically includes a first route generation unit 131 and a second route generation unit 132. The first route generation unit 131 generates the first route that is a parking route to park the subject vehicle from the current position to the temporary target position, by use of the recognition result transmitted from the temporary target position recognition unit 12. FIG. 7 is a diagram to explain operation to generate the first route. FIG. 7 illustrates the parking route on which the vehicle moves left forward once and then moves right rearward. The first route preferably includes a parking route that causes the subject vehicle to turn at a minimum turning radius”, paragraph [0045], “Further, the second route generation unit 132 generates the second route that is a corrected parking route to park the subject vehicle from the middle of the first route to the target position, by use of the recognition result transmitted from the target position recognition unit 11. FIG. 8 is a diagram to explain operation to generate the second route. FIG. 8 illustrates the parking route on which the vehicle moves rearward from a predetermined designated position in the first route to the target position. The predetermined designated position is a position that allows for generation of the achievable second route. In the example illustrated in FIG. 8, a position where a distance in the +Y direction from the coordinate origin to the center position of the rear wheel axle of the subject vehicle becomes a predetermined distance (e.g., 5000 mm to 6000 mm) or more is set as the designated position” and paragraph [0046], “Note that the designated position is preferably a position allowing for recognition of the target position with high precision. In other words, the target position can be updated to a more precise position by use of the latest surroundings information obtained while the subject vehicle follows the first route. Therefore, the designated position is set to, for example, a position where the target position can be recognized with high precision in the update operation of the target position. This enables the second route generation unit 132 to generate the second route toward the precise target position.” The system generates a final target position as a global target state and selects immediate target states for guiding the vehicle to the final target position.).
Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches wherein the target state candidate generation circuitry performs calculation so that all of the plurality of target state candidates can reach the global target state (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable, corresponding to calculating the target state candidates so that all of the plurality of target state candidates can reach the global target state.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; wherein the target state candidate generation circuitry performs calculation so that all of the plurality of target state candidates can reach the global target state, as taught by Di Cairano (See paragraphs [0087], [0091].), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].).
Claim(s) 3, 4, and 7-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mochida (US 20190300054 A1) in view of Di Cairano (US 20190391580 A1) and Wang (US 9969386 B1).
Regarding Claim 3, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches all of the plurality of target state candidates (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable, corresponding to all of the plurality of target state candidates.).
Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the target state candidate generation circuitry performs calculation so that plurality of target state candidates is within a reachable region which the moving body can dynamically reach (See at least Col. 13 lines 1-30, “The method samples 550 a point in the state space of the parking lot to produce a sampled state. The sampled state is rejected 555 if all states corresponding to the nodes of the geometrical graph are within 560 a non-reachable area of the sampled state. Otherwise, the method determines 570 a nearest node of the geometric graph having a state nearest to the sampled state and adds 580 a node for the sampled state to the geometric graph and connecting the added node with the nearest node via an edge if the edge is collision free.” The system performs calculations such that the plurality of target state candidates are within a region dynamically reachable by the moving body, because sampled states located in non-reachable areas are rejected.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; all of the plurality of target state candidates, as taught by Di Cairano (See paragraphs [0087], [0091].), and perform calculation so that the plurality of target state candidates is within a reachable region which the moving body can dynamically reach, as taught by Wang (See Col. 13 lines 1-30.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 4, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches so that the moving body can reach all of the plurality of target state candidates (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable, corresponding to the moving body reaching all of the plurality of target state candidates.).
Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the target state candidate generation circuitry performs calculation so that the moving body can reach plurality of target state candidates by traveling in consideration of ride quality of the moving body (See at least Col. 4 lines 1-20, “One embodiment uses a biased sampling to guide state sampling for constructing kinematic graphs. Additionally, or alternatively, one embodiment uses an approximate reachable set into the sampling step to improve the quality of sampled states. By taking the approximate reachable set into account, sampled states which are difficult or costly to connect are rejected. This treatment can reduce computation time wasted in collision detection due to inefficient samples, and also provide a set of waypoints which enable fast construction of the kinematic graph.” The system calculates the plurality of target states while improving the quality of travel by rejecting states that are difficult or costly to connect, which corresponds to consideration of ride quality.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; so that the moving body can reach all of the plurality of target state candidates, as taught by Di Cairano (See paragraphs [0087], [0091].), and perform calculation so that the moving body can reach the plurality of target state candidates by traveling in consideration of ride quality of the moving body, as taught by Wang (See Col. 4 lines 1-20.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 7, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the target state candidate generation circuitry includes: virtual trajectory generation circuitry generating a plurality of virtual trajectories passing through a plurality of target states (See at least Col. 13 lines 30-45, “The geometric graph includes nodes and edges representing geometric connections between the nodes such that the initial node is connected with the target node. Given the geometric graph, there can be many geometric paths from the initial state to the target state. It is advantageous to select a geometric path from the geometric graph that reduces a certain cost function. For example, one embodiment selects the geometric path by performing two steps: a) determining a cost for each node, where the node cost represents the minimal cost from the state specified by the node to the target state; b) starting with the initial node, selecting a set of nodes according to the node cost, where the cost of the initial node reaches minimum only if the vehicle passes through the set of nodes.”); virtual trajectory evaluation circuitry evaluating the plurality of virtual trajectories based on a dynamics limitation of the moving body (See at least Col. 13 lines 1-30, “The method samples 550 a point in the state space of the parking lot to produce a sampled state. The sampled state is rejected 555 if all states corresponding to the nodes of the geometrical graph are within 560 a non-reachable area of the sampled state. Otherwise, the method determines 570 a nearest node of the geometric graph having a state nearest to the sampled state and adds 580 a node for the sampled state to the geometric graph and connecting the added node with the nearest node via an edge if the edge is collision free.”); and target state candidate calculation circuitry calculating the plurality of target states reachable along a virtual trajectory within the limitation as the plurality of target state candidates (See at least Col. 12 lines 50-65, “Additionally, or alternatively, some embodiments perform the sampling using a reachability criterion. According to the reachability criterion, the sample in the state space is preserved only if that sample is reachable from the already constructed graph. In one embodiment, to avoid usage of the dynamic of the vehicle to test reachability, the reachability is defined as an absence of non-reachability, and nonreachability is a predetermined area near the sides of the vehicle.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and utilize virtual trajectory generation circuitry generating a plurality of virtual trajectories passing through a plurality of target states, evaluating the plurality of virtual trajectories based on a dynamics limitation of the moving body, and target state candidate calculation circuitry calculating the plurality of target states reachable along a virtual trajectory within the limitation as the plurality of target state candidates, as taught by Wang (See Col. 12 lines 50-65, Col. 13 lines 1-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 8, Mochida, Di Cairano, and Wang teach The passage point generation apparatus according to claim 7, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the local target state generation circuitry sets an optional position on a virtual trajectory reaching a target state candidate located closest to a center in the plurality of target state candidates calculated in the target state candidate calculation circuitry in the plurality of virtual trajectories to the local target state (See at least Col. 13 lines 30-45, “The geometric graph includes nodes and edges representing geometric connections between the nodes such that the initial node is connected with the target node. Given the geometric graph, there can be many geometric paths from the initial state to the target state. It is advantageous to select a geometric path from the geometric graph that reduces a certain cost function. For example, one embodiment selects the geometric path by performing two steps: a) determining a cost for each node, where the node cost represents the minimal cost from the state specified by the node to the target state; b) starting with the initial node, selecting a set of nodes according to the node cost, where the cost of the initial node reaches minimum only if the vehicle passes through the set of nodes.” The system evaluates multiple virtual trajectories and associated target state candidates to identify a representative target state candidate closest to a center of candidates based on cost evaluation, and sets a position along the virtual trajectory reaching that candidate as the local target state.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and set an optional position on a virtual trajectory reaching a target state candidate located closest to a center in the plurality of target state candidates calculated in the target state candidate calculation circuitry in the plurality of virtual trajectories to the local target state, as taught by Wang (See Col. 13 lines 30-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 9, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the local target state generation circuitry limits a change of the local target state immediately before the moving body reaches the local target state (See at least Col. 13 lines 1-45, “The method repeats the sampling, the rejecting, the determining, and the adding until the initial node is connected to the target node. For example, in one embodiment, construction of the geometric graph stops as long as the initial and target geometric trees are connected. In another embodiment, construction of the geometric graph stops until certain number of sampled states are added to both the initial and target geometric trees.” The system limits changes to the located target state by stopping updates to the planned path when the target becomes reachable, corresponding to limiting changes to the local target state immediately before the moving body reaches the local target state.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and limit a change of the local target state immediately before the moving body reaches the local target state, as taught by Wang (See Col. 13 lines 1-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 10, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the local target state generation circuitry performs weighting on the plurality of target state candidates based on a predetermined evaluation index, and calculates the local target state based on a value of a weight (See at least Col. 13 lines 30-45, “The geometric graph includes nodes and edges representing geometric connections between the nodes such that the initial node is connected with the target node. Given the geometric graph, there can be many geometric paths from the initial state to the target state. It is advantageous to select a geometric path from the geometric graph that reduces a certain cost function. For example, one embodiment selects the geometric path by performing two steps: a) determining a cost for each node, where the node cost represents the minimal cost from the state specified by the node to the target state; b) starting with the initial node, selecting a set of nodes according to the node cost, where the cost of the initial node reaches minimum only if the vehicle passes through the set of nodes.” The system evaluates target state candidates using cost as an evaluation index and selects candidates based on the minimum cost, corresponding to performing weighting based on a predetermined evaluation index.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and perform weighting on the plurality of target state candidates based on a predetermined evaluation index, and calculate the local target state based on a value of a weight, as taught by Wang (See Col. 13 lines 30-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 11, Mochida, Di Cairano, and Wang teach The passage point generation apparatus according to claim 7, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the target state candidate generation circuitry provides scores to the plurality of virtual trajectories based on a predetermined evaluation index, compares a score provided to each virtual trajectory and a threshold value, and sets a point where the moving body travels along the virtual trajectory having a score equal to or larger than the threshold value or smaller than the threshold value to the plurality of target state candidates (See at least Col. 4 lines 1-20, “One embodiment uses a biased sampling to guide state sampling for constructing kinematic graphs. Additionally, or alternatively, one embodiment uses an approximate reachable set into the sampling step to improve the quality of sampled states. By taking the approximate reachable set into account, sampled states which are difficult or costly to connect are rejected. This treatment can reduce computation time wasted in collision detection due to inefficient samples, and also provide a set of waypoints which enable fast construction of the kinematic graph” and Col. 13 lines 30-45, “The geometric graph includes nodes and edges representing geometric connections between the nodes such that the initial node is connected with the target node. Given the geometric graph, there can be many geometric paths from the initial state to the target state. It is advantageous to select a geometric path from the geometric graph that reduces a certain cost function. For example, one embodiment selects the geometric path by performing two steps: a) determining a cost for each node, where the node cost represents the minimal cost from the state specified by the node to the target state; b) starting with the initial node, selecting a set of nodes according to the node cost, where the cost of the initial node reaches minimum only if the vehicle passes through the set of nodes.” The system assigns cost values as scores to virtual trajectories, rejects trajectories and states that are costly or difficult to connect, and selects nodes among remaining trajectories as target state candidates.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and utilize virtual trajectory generation circuitry generating a plurality of virtual trajectories passing through a plurality of target states, evaluating the plurality of virtual trajectories based on a dynamics limitation of the moving body, target state candidate calculation circuitry calculating the plurality of target states reachable along a virtual trajectory within the limitation as the plurality of target state candidates and providing scores to the plurality of virtual trajectories based on a predetermined evaluation index, compares a score provided to each virtual trajectory and a threshold value, and sets a point where the moving body travels along the virtual trajectory having a score equal to or larger than the threshold value or smaller than the threshold value to the plurality of target state candidates, as taught by Wang (See Col. 4 lines 1-20, Col. 12 lines 50-65, Col. 13 lines 1-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Regarding Claim 12, Mochida, Di Cairano, and Wang teach The passage point generation apparatus according to claim 7, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Wang, in the same field of endeavor, teaches wherein the local target state generation circuitry performs weighting on a virtual trajectory reaching the plurality of target state candidates calculated in the target state candidate calculation circuitry in the plurality of virtual trajectories, and sets a state where a weighted average of an optional position on the virtual trajectory and a weight is obtained to the local target state (See at least Col. 13 lines 30-45, “The geometric graph includes nodes and edges representing geometric connections between the nodes such that the initial node is connected with the target node. Given the geometric graph, there can be many geometric paths from the initial state to the target state. It is advantageous to select a geometric path from the geometric graph that reduces a certain cost function. For example, one embodiment selects the geometric path by performing two steps: a) determining a cost for each node, where the node cost represents the minimal cost from the state specified by the node to the target state; b) starting with the initial node, selecting a set of nodes according to the node cost, where the cost of the initial node reaches minimum only if the vehicle passes through the set of nodes” and Col. 14 lines 25-45, “In another embodiment, the best geometric path is determined by three steps: a) trimming the geometric graph by removing all leaf nodes except the initial and target nodes, where a leaf node has only one neighbor node; b) determining the minimal cost of each node by performing the value iteration over the geometric graph after trimming; c) starting with the initial node, selecting a set of nodes according to the geometric graph after trimming. This embodiment is based on realization that the best geometric path does not contain any leaf node, and thus performing value iteration over the geometric graph after trimming gives the same minimal cost of all non-leaf nodes. This embodiment can significantly reduce computation load of determining the minimal cost of the node, and the best geometric path. A set of waypoints can be extracted from the set of nodes defining the best geometric graph.” The system determines a representative position along a virtual trajectory based on cost-based weighting through iterative evaluation, corresponding to setting a local target based on weighting of nodes along the virtual trajectory.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Wang such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point, as taught by Di Cairano (See paragraphs [0087], [0091].), and utilize virtual trajectory generation circuitry generating a plurality of virtual trajectories passing through a plurality of target states, evaluating the plurality of virtual trajectories based on a dynamics limitation of the moving body, target state candidate calculation circuitry calculating the plurality of target states reachable along a virtual trajectory within the limitation as the plurality of target state candidates, and performing weighting on a virtual trajectory reaching the plurality of target state candidates calculated in the target state candidate calculation circuitry in in the plurality of virtual trajectories, and sets a state where a weighted average of an optional position on the virtual trajectory and a weight is obtained to the local target state, as taught by Wang (See Col. 12 lines 50-65, Col. 13 lines 1-45, Col. 14 lines 25-45.), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as predicting safe paths to avoid obstacles and optimizing vehicle operation criteria, as taught by Wang (See Col. 1 lines 1-20.).
Claim(s) 6 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mochida (US 20190300054 A1) in view of Di Cairano (US 20190391580 A1) and Jin (US 20190311616 A1).
Regarding Claim 6, Mochida and Di Cairano teach The passage point generation apparatus according to claim 5, as set forth in the obviousness rejection above. Mochida and Di Cairano do not explicitly disclose, however, Jin, in the same field of endeavor, teaches wherein each of the plurality of target state candidates is a target state for the moving body to pass through a gate of a tollgate, and the global target state includes at least a state amount of a position to which the moving body proceeds after the moving body passes through the gate of the tollgate (See at least paragraph [0181], “710—Pre-Routing Services for Different Toll Method in Toll Plaza: CAVH vehicles with different toll-devices or toll-plan are pre-routed to a different lane/path before they reach a toll-gate to avoid congestion in a toll plaza” and paragraph [0182], “711—Pre-Routing Services for Different Exit Direction in Departure Stage: CAVH vehicles are pre-routed to a different lane/path to get a smooth access to a specific exit ramp/link according to their destination and preference.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Jin such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; wherein the target state candidate generation circuitry performs calculation so that all of the plurality of target state candidates can reach the global target state, as taught by Di Cairano (See paragraphs [0087], [0091].), and utilize each of the plurality of target state candidates is a target state for the moving body to pass through a gate of a tollgate, and the global target state includes at least a state amount of a position to which the moving body proceeds after the moving body passes through the gate of the tollgate, as taught by Jin (See Paragraph [0181], [0182].), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as providing control and guidance signals to vehicles to execute autonomous driving through different road segments and nodes, as taught by Jin (See Paragraph [0003].).
Regarding Claim 13, Mochida and Di Cairano teach The passage point generation apparatus according to claim 1, as set forth in the obviousness rejection above. Mochida does not explicitly disclose, however, Di Cairano, in the same field of endeavor, teaches and the local target state generation circuitry calculates the local target state so that the moving body can reach all of the plurality of target state candidates (See at least paragraph [0087], “The need to achieve the next intermediate specific goal has a similar effect. First, a region of the vehicle state space is associated to it being the region of vehicle states from which the next intermediate specific goal can be achieved according to the first vehicle model stored in the first section 211 of the memory. Then, for any graph node that is allowed to transition to the graph node corresponding to the next intermediate specific goal, the region of vehicle states that allow to achieve the intermediate goal and also enter the region of states from which the next specific goal is achieved, according to the first vehicle model stored in the first section 211 of the memory, is computed. The process can be repeated iteratively by obtaining for any graph node that is successor of the graph node associated to the current intermediate goal a region of vehicle states such that transitioning to the goal associated with the successor node allows for a sequence of intermediate goals leading to accomplishment of the next intermediate specific goal” and paragraph [0091], “By considering only the region of states from which the goal can be accomplished, and the next specific goal can be accomplished, and the collision or dangerous interactions with the traffic are avoided, one obtains the region of states from which currently one can transition to the goal. If the current vehicle state is contained in such region, then the goal can be selected as intermediate goal for the next period.” The system computes a region of vehicle states from which an intermediate goal and subsequent goals remain achievable, corresponding to the moving body reaching all of the plurality of target state candidates upon reaching the local target state.).
Mochida and Di Cairano do not explicitly disclose, however, Jin, in the same field of endeavor, teaches wherein the passage point generation apparatus is mounted on a control apparatus admissive- controlling the moving body by communication with the moving body, in consideration of communication delay of admissive control (See at least paragraph [0165]-[0168], “610—Communication from RSU to OBU or other device: data flow including CAVH control/guidance signals from RSU to OBU; 611—Communication between RSUs: data flow including control/guidance signals from one RSU to other RSUs; 612—Communication from OBU to RSU: data flow including control/guidance signals from OBU to RSU; 613—Communication between TCU/TCC/CAVH Cloud and RSU: data flow including control/guidance signals from TCU/TCC/CAVH Cloud to RSU and necessary data from RSU to TCU” and paragraph [0185], “714—CAVH V2I Link in Lower Deck/Tunnel: CAVH V2I link in this area is not guaranteed. Detection may be unavailable in some segments and V2I communication may have high packet-loss/delay.” The system generates guidance or control information for the moving body via communication and accounts for communication delay, corresponding to the passage point generation apparatus being mounted on a control apparatus admissive—controlling the moving body by communication with the moving body and considering communication delay of admissive control.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of Mochida with the teachings of Di Cairano and Jin such that the vehicle system of Mochida is further configured to utilize local target state generation circuitry to calculate a local target state as a local target state reachable in all of the plurality of target state candidates in an intermediate point of a trajectory toward a surrounding area of the plurality of target state candidates and output the local target state as the passage point; and the local target state generation circuitry calculates the local target state so that the moving body can reach all of the plurality of target state candidates, as taught by Di Cairano (See paragraphs [0087], [0091].), and utilize a mounted control apparatus admissive- controlling the moving body by communication with the moving body, in consideration of communication delay of admissive control., as taught by Jin (See Paragraph [0165]-[0168], [0185].), with a reasonable expectation of success. The motivation for doing so would be to ensure that each intermediate goal remains achievable such that a sequence of intermediate goals leads to the achievement of the overall driving objectives, as taught by Di Cairano (See paragraph [0004].), as well as providing control and guidance signals to vehicles to execute autonomous driving through different road segments and nodes, as taught by Jin (See Paragraph [0003].).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEWEL ASHLEY KUNTZ whose telephone number is (571)270-5542. The examiner can normally be reached M-F 8:30am-5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEWEL A KUNTZ/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
Prosecution Insights