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 .
Response to Amendment
This action is in response to amendments and remarks filed on 05/08/2026. Claim(s) 1-2, 4-5, 7, 9, 16-20, 22, 24, and 31-32 have been amended. Claim(s) 15 and 30 have been cancelled. Claim(s) 1-14, 16-29, and 31-32 are pending examination. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 102 and 103 rejections of claims 1-32 is improper. Applicant asserts that the newly amended claim limitations to 1, 16, 31, and 32 would render the previous rejection improper. In particular applicant asserts that Fuchs and Thibaux do not teach, “reallocating nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories.” Accordingly the claims should be allowable.
Applicant’s arguments with respect to claim(s) 1-14, 16-29, and 31-32 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.
Regarding applicant’s argument A, the examiner finds it moot. In light of applicant’s amendments to the independent claims and further consideration the examiner would point towards newly cited art of Singh, “Comparison of multi-modal optimization algorithms based on evolutionary algorithms.” Singh teaches a search algorithm that can determine whether or not a node is being properly used. In the event that a searched event’s solution is of no further use to the searching, the node can be reallocated and at a new point in the search graph. This “clearing” method allows for a search of possible optimal solutions to a problem to be carried out and not waste nodes/computation that is too close to a solution but is no longer of use to the final proposed solution. This proposed solution to a clearing problem/searching allows all optimal solutions to be found to a problem with multiple variables. In light of Fuchs and Singh the examiner would maintain the rejection of the independent claims. Please see the section below titled “Claim Rejections – 35 USC 103,” for further detailed mapping and explanation.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 05/08/2026, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 11-18, and 26-32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fuchs (US PG Pub 2021/0146922) in view of Singh ("Comparison of multi-modal optimization algorithms based on evolutionary algorithms").
Regarding claim 1, Fuchs teaches a method of wireless communication performed by a first vehicle-to-everything (V2X)-capable vehicle, ([0051 teaches a vehicle capable of performing a V2X communication between itself and another vehicle) comprising:
receiving, from a second V2X-capable vehicle, one or more V2X messages indicating an intended driving maneuver of the second V2X-capable vehicle; ([0050], [0058] and [0067] teach the ego vehicle receiving from a second vehicle an intended trajectory for the second vehicle)
determining non-viable driving trajectories of a plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; ([0096]-[0098] teach determining viable trajectories and non-viable trajectories in the coupling of the ego vehicle trajectory and the second vehicle trajectory)
determining a viable driving trajectory for the first V2X-capable vehicle from the remaining driving trajectories of the plurality of potential driving trajectories; ([0008]-[0010], [0022], [0050], [0063], and [0067] teach the ego vehicle determining a viable trajectory for itself based in part from the trajectory of the second vehicle. The system performs analysis to determine a collision free trajectory which would be analogous to a viable trajectory. This includes removing trajectories based on their cost being too high and narrowing the search) and
transmitting at least the viable driving trajectory to one or more other V2X-capable vehicles, roadside infrastructure, or any combination thereof; ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.) and
performing a driving maneuver according to the viable driving trajectory. ([0051] teaches the vehicle executing the trajectory)
Fuchs does not teach reallocating nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories.
However, Singh teaches “reallocating nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories;” (Pages 1308-1309, “3. Proposed Modified Clearing Approach” teaches a clearing, search, algorithm that can determine an optimal solution for a given problem. It achieves this by searching for a given solution in a first run, after it has determined possible and not possible solution it reallocates some of its search nodes, denoted as “individuals” and/or “slots.” ) The act of “to refocus a node budget for determining viable driving trajectories;” is considered intended use and has no patentable weight.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh; and have a reasonable expectation of success. Both are related to searching for an optimal solution to a problem with multiple possible solutions. As Singh teaches in its Abstract and Conclusion, a clearing or searching, algorithm is tasked with finding the optimal solution for a problem with multiple variables and multiple possible solutions. However, sometimes solutions that do not solve the problem as a whole will take up an individual’s budget. These individuals are analogous to the nodes. After reallocating the nodes it allows the system to search for better solutions. On Page 1308, section 3, Singh further teaches, “the reallocation of bad solutions allows the method to explore the search space for better solutions.” This shows that the use of node reallocation would allow for the optimal trajectory to be found.
Regarding claim 2, Fuchs teaches the method of claim 1, further comprising: removing the non-viable driving trajectories from the plurality of potential driving trajectories to determine the remaining driving trajectories. ([0096]-[0098] teaches determining the viable trajectories and removing the trajectories from the set of possible trajectories)
Regarding claim 3, Fuchs teaches the method of claim 2, further comprising: transmitting the set of remaining driving trajectories to the one or more other V2X-capable vehicles, the roadside infrastructure, or the combination thereof. ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.)
Regarding claim 11, Fuchs teaches the method of claim 1, wherein the one or more V2X messages are one or more maneuver sharing and coordination message (MSCM) requests. ([0074]-[0075] teaches the system using a cooperative awareness message, this is analogous to the MSCM message)
Regarding claim 12, Fuchs teaches the method of claim 11, further comprising: transmitting an MSCM response to the second V2X-capable vehicle acknowledging the one or more MSCM requests. ([0050], and [0074]-[0075] teach the vehicle sending an acknowledgment message to the other vehicle)
Regarding claim 13, Fuchs teaches the method of claim 1, wherein the intended driving maneuver comprises: a lane change, a merge onto a road on which the first V2X-capable vehicle is travelling, a hard braking event, a turn onto another road than the road on which the first V2X-capable vehicle is travelling, or a turn off of the road on which the first V2X-capable vehicle is travelling. (Figs. 1 and 2 and [0052]-[0053] teach a combination of the intended driving maneuvers)
Regarding claim 14, Fuchs teaches the method of claim 1, further comprising: receiving one or more driving trajectories from the one or more other V2X-capable vehicles, the roadside infrastructure, or the combination thereof, wherein the viable driving trajectory is determined further based on the one or more driving trajectories. ([0050], [0058] and [0067] teach the ego vehicle receiving from another vehicle an intended trajectory for the other vehicle)
Regarding claim 16, Fuchs teaches a first vehicle-to-everything (V2X)-capable vehicle, ([0051 teaches a vehicle capable of performing a V2X communication between itself and another vehicle) comprising:
one or more memories, ([0038]-[0039] teaches the system having one or more memories)
one or more transceivers; ([0038] teaches the system having one or more communication interfaces to send and receive data, this would be analogous to the transceiver) and
one or more processors communicatively coupled to the one or more memories and the one or more transceivers, ([0038]-[0039] teach the system having one or more processors couples to the remaining components in order to execute stored code) the one or more processors, either alone or in combination, configured to:
receive, from a second V2X-capable vehicle, one or more V2X messages indicating an intended driving maneuver of the second V2X-capable vehicle; ([0050], [0058] and [0067] teach the ego vehicle receiving from a second vehicle an intended trajectory for the second vehicle)
determine non-viable driving trajectories of a plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; ([0096]-[0098] teach determining viable trajectories and non-viable trajectories in the coupling of the ego vehicle trajectory and the second vehicle trajectory)
determine a viable driving trajectory for the first V2X-capable vehicle from the remaining driving trajectories of the plurality of potential driving trajectories; ([0008]-[0010], [0022], [0050], [0063], and [0067] teach the ego vehicle determining a viable trajectory for itself based in part from the trajectory of the second vehicle. The system performs analysis to determine a collision free trajectory which would be analogous to a viable trajectory. This includes removing trajectories based on their cost being too high and narrowing the search)
transmit, via the one or more transceivers, at least the viable driving trajectory to one or more other V2X-capable vehicles, roadside infrastructure, or any combination thereof; ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.) and
perform a driving maneuver according to the viable driving trajectory. ([0051] teaches the vehicle executing the trajectory)
Fuchs does not teach reallocate nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories.
However, Singh teaches “reallocate nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories;” (Pages 1308-1309, “3. Proposed Modified Clearing Approach” teaches a clearing, search, algorithm that can determine an optimal solution for a given problem. It achieves this by searching for a given solution in a first run, after it has determined possible and not possible solution it reallocates some of its search nodes, denoted as “individuals” and/or “slots.” ) The act of “to refocus a node budget for determining viable driving trajectories;” is considered intended use and has no patentable weight.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh; and have a reasonable expectation of success. Both are related to searching for an optimal solution to a problem with multiple possible solutions. As Singh teaches in its Abstract and Conclusion, a clearing or searching, algorithm is tasked with finding the optimal solution for a problem with multiple variables and multiple possible solutions. However, sometimes solutions that do not solve the problem as a whole will take up an individual’s budget. These individuals are analogous to the nodes. After reallocating the nodes it allows the system to search for better solutions. On Page 1308, section 3, Singh further teaches, “the reallocation of bad solutions allows the method to explore the search space for better solutions.” This shows that the use of node reallocation would allow for the optimal trajectory to be found.
Regarding claim 17, Fuchs teaches the first V2X-capable vehicle of claim 16, wherein the one or more processors, either alone or in combination, are further configured to: remove the non-viable driving trajectories from the plurality of potential driving trajectories to determine the remaining driving trajectories of the plurality of potential driving trajectories. ([0096]-[0098] teaches determining the viable trajectories and removing the trajectories from the set of possible trajectories)
Regarding claim 18, Fuchs teaches the first V2X-capable vehicle of claim 17, wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, the remaining driving trajectories to one or more other V2X-capable vehicles, roadside infrastructure, or any combination thereof. ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.)
Regarding claim 26, Fuchs teaches the first V2X-capable vehicle of claim 16, wherein the one or more V2X messages are one or more maneuver sharing and coordination message (MSCM) requests ([0074]-[0075] teaches the system using a cooperative awareness message, this is analogous to the MSCM message)
Regarding claim 27, Fuchs teaches the first V2X-capable vehicle of claim 26, wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, an MSCM response to the second V2X-capable vehicle acknowledging the one or more MSCM requests. ([0050], and [0074]-[0075] teach the vehicle sending an acknowledgment message to the other vehicle)
Regarding claim 28, Fuchs teaches the first V2X-capable vehicle of claim 16, wherein the intended driving maneuver comprises: a lane change, a merge onto a road on which the first V2X-capable vehicle is travelling, a hard braking event, a turn onto another road than the road on which the first V2X-capable vehicle is travelling, or a turn off of the road on which the first V2X-capable vehicle is travelling. (Figs. 1 and 2 and [0052]-[0053] teach a combination of the intended driving maneuvers)
Regarding claim 29, Fuchs teaches the first V2X-capable vehicle of claim 16, wherein the one or more processors, either alone or in combination, are further configured to: receive, via the one or more transceivers, one or more driving trajectories from the one or more other V2X-capable vehicles, the roadside infrastructure, or any combination thereof, wherein the viable driving trajectory is determined further based on the one or more driving trajectories. ([0050], [0058] and [0067] teach the ego vehicle receiving from a second vehicle an intended trajectory for the second vehicle)
Regarding claim 31, Fuchs teaches a first vehicle-to-everything (V2X)-capable vehicle, ([0051 teaches a vehicle capable of performing a V2X communication between itself and another vehicle) comprising:
means for receiving, from a second V2X-capable vehicle, one or more V2X messages indicating an intended driving maneuver of the second V2X-capable vehicle; ([0050], [0058] and [0067] teach the ego vehicle receiving from a second vehicle an intended trajectory for the second vehicle)
means for determining non-viable driving trajectories of a plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; ([0096]-[0098] teach determining viable trajectories and non-viable trajectories in the coupling of the ego vehicle trajectory and the second vehicle trajectory)
means for determining a viable driving trajectory for the first V2X-capable vehicle from the remaining driving trajectories of the plurality of potential driving trajectories; ([0008]-[0010], [0022], [0050], [0063], and [0067] teach the ego vehicle determining a viable trajectory for itself based in part from the trajectory of the second vehicle. The system performs analysis to determine a collision free trajectory which would be analogous to a viable trajectory. This includes removing trajectories based on their cost being too high and narrowing the search) and
means for transmitting at least the viable driving trajectory to one or more other V2X-capable vehicles, roadside infrastructure, or any combination thereof; ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.) and
means for performing a driving maneuver according to the viable driving trajectory. ([0051] teaches the vehicle executing the trajectory)
Fuchs does not teach means for reallocating nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories.
However, Singh teaches “means for reallocating nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories;” (Pages 1308-1309, “3. Proposed Modified Clearing Approach” teaches a clearing, search, algorithm that can determine an optimal solution for a given problem. It achieves this by searching for a given solution in a first run, after it has determined possible and not possible solution it reallocates some of its search nodes, denoted as “individuals” and/or “slots.” ) The act of “to refocus a node budget for determining viable driving trajectories;” is considered intended use and has no patentable weight.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh; and have a reasonable expectation of success. Both are related to searching for an optimal solution to a problem with multiple possible solutions. As Singh teaches in its Abstract and Conclusion, a clearing or searching, algorithm is tasked with finding the optimal solution for a problem with multiple variables and multiple possible solutions. However, sometimes solutions that do not solve the problem as a whole will take up an individual’s budget. These individuals are analogous to the nodes. After reallocating the nodes it allows the system to search for better solutions. On Page 1308, section 3, Singh further teaches, “the reallocation of bad solutions allows the method to explore the search space for better solutions.” This shows that the use of node reallocation would allow for the optimal trajectory to be found.
Regarding claim 32, Fuchs teaches a non-transitory computer-readable medium storing computer-executable instructions that, when executed by a first vehicle-to-everything (V2X)-capable vehicle, ([0051 teaches a vehicle capable of performing a V2X communication between itself and another vehicle; [0038]-[0039] teaches a non-transitory computer readable medium that can store signals and programs to be executed by a vehicle) cause the first V2X-capable vehicle to:
receive, from a second V2X-capable vehicle, one or more V2X messages indicating an intended driving maneuver of the second V2X-capable vehicle; ([0050], [0058] and [0067] teach the ego vehicle receiving from a second vehicle an intended trajectory for the second vehicle)
determine non-viable driving trajectories of a plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; ([0096]-[0098] teach determining viable trajectories and non-viable trajectories in the coupling of the ego vehicle trajectory and the second vehicle trajectory)
determine a viable driving trajectory for the first V2X-capable vehicle from the remaining driving trajectories of the plurality of potential driving trajectories; ([0008]-[0010], [0022], [0050], [0063], and [0067] teach the ego vehicle determining a viable trajectory for itself based in part from the trajectory of the second vehicle. The system performs analysis to determine a collision free trajectory which would be analogous to a viable trajectory. This includes removing trajectories based on their cost being too high and narrowing the search) and
transmit at least the viable driving trajectory to one or more other V2X-capable vehicles, roadside infrastructure, or any combination thereof; ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0011], [0054]-[0055], [0067]-[0071], [0090]-[0092], and [0104] teach the use of “demand trajectory,” and “alternative trajectories.” These are trajectories that the ego vehicle would like to carry out and transmits to surrounding vehicles in order to advise them of their plan. As [0054]-[0055] teach “ In an embodiment, trajectories with an effort value 112 greater than the reference effort value 114 are denominated alternative trajectories 116. Trajectories 108 with an effort value less than the reference effort value 114 are referred to as demand trajectories 118. The device 102 sends a data packet 120 to the other vehicle 104. The data packet 120 contains information about trajectories 108 of the trajectory set 106. The respective effort values 112 are also transmitted via data packet 120.” This clearly teaches the system sending desired trajectories for the vehicle to carry out. These trajectories are then clearly communicated to surrounding vehicles.) and
perform a driving maneuver according to the viable driving trajectory. ([0051] teaches the vehicle executing the trajectory)
Fuchs does not teach reallocate nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories.
However, Singh teaches “reallocate nodes from the non-viable driving trajectories to remaining driving trajectories of the plurality of potential driving trajectories to refocus a node budget for determining viable driving trajectories;” (Pages 1308-1309, “3. Proposed Modified Clearing Approach” teaches a clearing, search, algorithm that can determine an optimal solution for a given problem. It achieves this by searching for a given solution in a first run, after it has determined possible and not possible solution it reallocates some of its search nodes, denoted as “individuals” and/or “slots.” ) The act of “to refocus a node budget for determining viable driving trajectories;” is considered intended use and has no patentable weight.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh; and have a reasonable expectation of success. Both are related to searching for an optimal solution to a problem with multiple possible solutions. As Singh teaches in its Abstract and Conclusion, a clearing or searching, algorithm is tasked with finding the optimal solution for a problem with multiple variables and multiple possible solutions. However, sometimes solutions that do not solve the problem as a whole will take up an individual’s budget. These individuals are analogous to the nodes. After reallocating the nodes it allows the system to search for better solutions. On Page 1308, section 3, Singh further teaches, “the reallocation of bad solutions allows the method to explore the search space for better solutions.” This shows that the use of node reallocation would allow for the optimal trajectory to be found.
Claim(s) 4-10 and 19-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fuchs and Singh in view of Thibaux (US PG Pub 2023/0094975).
Regarding claim 4, the combination of Fuchs and Singh teaches method of claim 1.
The combination of Fuchs and Singh does not teach each node represents a position on a potential driving trajectory through a macro action of one or more macro actions, and each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling.
However, Thibaux teaches “each node represents a position on a potential driving trajectory through a macro action of one or more macro actions,” (Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions) and “each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling.” ([0010] teaches the system having graphical representations of roads and traversing them. Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 19 is substantially similar and would be rejected for the same reason.
Regarding claim 5, the combination of Fuchs and Singh teaches the method of claim 1.
The combination of Fuchs and Singh does not teach wherein building a search tree of the plurality of potential driving trajectories, wherein each of the plurality of potential driving trajectories corresponds to a subtree of the search tree.
However, Thibaux teaches “building a search tree of the plurality of potential driving trajectories, wherein each of the plurality of potential driving trajectories corresponds to a subtree of the search tree.” ([0070], [0084]-[0091] teaches the creation of a search tree for determining the routing of the vehicle in the environment and to determine viable trajectories)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 20 is substantially similar and would be rejected for the same reason.
Regarding claim 6, the combination of Fuchs and Singh teaches the method of claim 5.
The combination of Fuchs and Singh does not teach wherein each subtree of the search tree comprises one or more macro actions, each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling and is associated with one or more nodes, and each node represents a position on a potential driving trajectory through the portion of the lane of the road represented by the corresponding macro action.
However, Thibaux teaches “wherein each subtree of the search tree comprises one or more macro actions,” ([0084]-[0085] teach the creation of subtrees that correspond to actions that a vehicle can perform in a drivable region) “each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling and is associated with one or more nodes,” ([0010] teaches the system having graphical representations of roads and traversing them. Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions) and “each node represents a position on a potential driving trajectory through the portion of the lane of the road represented by the corresponding macro action.” (Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 21 is substantially similar and would be rejected for the same reason.
Regarding claim 7, the combination of Fuchs and Singh teaches the method of claim 5.
The combination of Fuchs and Singh does not teach wherein determining the non-viable driving trajectories comprises: determining subtrees of the search tree corresponding to the non-viable driving trajectories of the plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; and removing the subtrees of the search tree corresponding to the non-viable driving trajectories from the plurality of potential driving trajectories, wherein the viable driving trajectory for the first V2X-capable vehicle corresponds to a remaining subtree of the search tree.
However, Thibaux teaches “wherein determining the non-viable driving trajectories comprises: determining subtrees of the search tree corresponding to the non-viable driving trajectories of the plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle;” ([0088] and [0112] teaches the system determining if the lane graph violates a rule established by the system) and removing the subtrees of the search tree corresponding to the non-viable driving trajectories from the plurality of potential driving trajectories, wherein the viable driving trajectory for the first V2X-capable vehicle corresponds to a remaining subtree of the search tree. ([0057], [0088], [0094] and [0111]-[0112] teaches the system pruning the sub tree graphs to determine the last remaining viable trajectory)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 22 is substantially similar and would be rejected for the same reason.
Regarding claim 8, Fuchs teaches the method of claim 7, further comprising: transmitting remaining subtrees of the search tree to the one or more other V2X-capable vehicles, the roadside infrastructure, or the combination thereof. ([0078] and [0099] teaches the system transmitting the trajectories from the ego vehicle to another vehicle this is furthered by [0074]-[0076] Fuchs teaches a series of trajectory identifiers; these categories can determine that the trajectory is sent to multiple other V2X-capable vehicles. [0079]-[0089] explicitly teaches a coordination system between several vehicles. This would mean that the trajectory is sent to a series of additional vehicles. [0105] further teaches a vehicle determining a viable trajectory, i.e. an emergency vehicle route on a highway. After the determination of the viable trajectory the vehicle sends the route to all necessary V2X vehicles.)
Claim 23 is substantially similar and would be rejected for the same reason.
Regarding claim 9, Fuchs teaches method of claim 5.
Fuchs does not teach determining subtrees of the search tree corresponding to non-viable driving trajectories of the plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; reallocating nodes from the non-viable driving trajectories to the remaining driving trajectories comprises reallocating nodes from the subtrees of the search tree corresponding to the non-viable driving trajectories to remaining subtrees of the search tree, the viable driving trajectory for the first V2X-capable vehicle corresponds to a remaining subtree of the search tree, each node represents a position on a potential driving trajectory through a macro action of one or more macro actions, and each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling.
However, Singh teaches “reallocating nodes from the non-viable driving trajectories to the remaining driving trajectories comprises reallocating nodes from the subtrees of the search tree corresponding to the non-viable driving trajectories to remaining subtrees of the search tree,” (Pages 1308-1309, “3. Proposed Modified Clearing Approach” teaches a clearing, search, algorithm that can determine an optimal solution for a given problem. It achieves this by searching for a given solution in a first run, after it has determined possible and not possible solution it reallocates some of its search nodes, denoted as “individuals” and/or “slots.” )
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh; and have a reasonable expectation of success. Both are related to searching for an optimal solution to a problem with multiple possible solutions. As Singh teaches in its Abstract and Conclusion, a clearing or searching, algorithm is tasked with finding the optimal solution for a problem with multiple variables and multiple possible solutions. However, sometimes solutions that do not solve the problem as a whole will take up an individual’s budget. These individuals are analogous to the nodes. After reallocating the nodes it allows the system to search for better solutions. On Page 1308, section 3, Singh further teaches, “the reallocation of bad solutions allows the method to explore the search space for better solutions.” This shows that the use of node reallocation would allow for the optimal trajectory to be found.
The combination of Fuchs and Singh does not teach determining subtrees of the search tree corresponding to non-viable driving trajectories of the plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle; the viable driving trajectory for the first V2X-capable vehicle corresponds to a remaining subtree of the search tree, each node represents a position on a potential driving trajectory through a macro action of one or more macro actions, and each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling.
However, Thibaux teaches “determining subtrees of the search tree corresponding to non-viable driving trajectories of the plurality of potential driving trajectories based, at least in part, on the intended driving maneuver of the second V2X-capable vehicle;” ([0088] and [0112] teaches the system determining if the lane graph violates a rule established by the system) and “the viable driving trajectory for the first V2X-capable vehicle corresponds to a remaining subtree of the search tree,” ([0057], [0088], [0094] and [0111]-[0112] teaches the system pruning the sub tree graphs to determine the last remaining viable trajectory) “each node represents a position on a potential driving trajectory through a macro action of one or more macro actions,” (Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions) and “each macro action represents a portion of a lane of a road on which the first V2X-capable vehicle is travelling.” ([0010] teaches the system having graphical representations of roads and traversing them. Fig. 2 and [0076] teach the system having entry and exit points along a vehicle trajectory and creating a path through those points. [0043] teaches that each node is a connection point between actions)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 24 is substantially similar and would be rejected for the same reason.
Regarding claim 10, the combination of Fuchs and Singh teaches method of claim 5.
The combination of Fuchs and Singh does not teach wherein the search tree comprises a Monte Carlo Tree Search.
However, Thibaux teaches “wherein the search tree comprises a Monte Carlo Tree Search.” ([0070] and [0084] teach the use of search tree to determine the viable driving trajectory.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Fuchs and Singh with Thibaux; and have a reasonable expectation of success. All relate to finding optimal solutions of problems, including trajectory planning of autonomous vehicles. As taught in [0006]-[0009] the system of Thibaux allows for the completion of driving tasks quickly and efficiently. The autonomous vehicle can determine the next steps of a driving system and navigate it using search trees and allocation of nodes to ensure that it is moving through the area as optimally as possible without wasted computational time.
Claim 25 is substantially similar and would be rejected for the same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kalra ("Enhancing clearing-based niching method using Delaunay Triangulation") teaches the interest in multi-modal optimization methods is increasing in the recent years since many of real-world optimization problems have multiple/many optima and decision makers prefer to find all of them. Multiple global/local peaks create difficulties for optimization algorithms. In this context, niching is well-known and widely used technique for finding multiple solutions in multi-modal optimization. One commonly used niching technique in evolutionary algorithms is the Clearing method. However, canonical clearing scheme reduces the exploration capacity of the evolutionary algorithms. In this paper, Delaunay Triangulation based Clearing (DT-Clearing) procedure is proposed to handle multi-modal optimizations more efficiently while preserving simplicity of canonical clearing approach. In DT-Clearing, cleared individuals are reallocated in the biggest empty spaces formed within the search space which are determined through Delaunay Triangulation. The reallocation of cleared individuals discourages wasting of the resources and allows better exploration of the landscape. The algorithm also uses an external memory, an archive of the explored niches, thus preventing the redundant visiting of the individuals, henceforth finding more solutions in lesser number of generations. The method is tested using multi-modal benchmark problems proposed for the IEEE CEC 2013, Special Session on Niching Methods for Multimodal Optimization. Our method obtains promising results in comparison with the canonical clearing and demonstrates to be a competitive niching algorithm.
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 NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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, Christian Chace can be reached at (571) 272-4190. 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.
/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights