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 RCE
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/17/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/17/2026.The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 7, 10, 11, 14, 15, 16, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable by Nishira (US20050256630) in view of Li (US20200307589) and Asakura (US20170313313).
Regarding claim 1, Nishira teaches a method comprising:
Nishira teaches Determining using speed profiles and in a one-dimensional lane representation, forward projections of at least one first position of at least one object corresponding to the candidate gap and at least one second position corresponding to the machine ([0053] discloses the appropriate speed range for the gap based on an appropriate lane change condition as to a positional relationship between the host vehicle and the other vehicle where the equation is based on a value which relates to longitudinal travel as shown in figure 4, i.e., in one dimension. [0055] disclosing determining a condition where the host vehicle would travel at a position ahead of the other vehicle by a headway time. [0056] disclosing predicting the change of state quantity in relationship and [0057] disclosing determining appropriate speed range that reaches the lane changeable condition within a predicted period in time, i.e., filtering set of speed profiles and only selecting a set that are the appropriate speed range. The cited paragraph disclose at least in view of one object that corresponds to the gap and in one dimension to determine its changed location and the changed location of the subject vehicle in view of the different speeds to select the best speed profile);
Evaluating the forward projections using one or more criteria to select a speed profile from the speed profiles for the candidate gap (at least [0053]-[0057] disclosing the critera has to be met corresponding to the relative distance between the subject vehicle and the one other vehicle in order to select the speed profile from amongst the set of speed profiles);
Using the speed profile to perform one or more control operations to maneuver the machine into the candidate gap ([0102] disclosing using the speed range selecting the appropriate speed from the speed profile to change lane).
Nashira does not teach identifying, based at least on sensor data obtained using at least one sensor of a machine in an environment, a candidate gap for the machine; graph having an axis representing a longitudinal space of a lane corresponding to the machine compressed with at least one lane corresponding to the one object defining the candidate gap. Evaluating projections within graph.
Li teaches identifying, based at least on sensor data obtained using at least one sensor of a machine in an environment, a candidate gap for the machine ([0025] disclosing the target gap created between vehicles based on the velocities, locations and accelerations of the two vehicles on the target lane change. [0030]-[0055] disclosing candidate gaps based on sensing other vehicles locations).
It would have been obvious to one of ordinary skill in the art to have combine the teaching of Li yielding predictable results in order to identify gaps based on relative speeds, locations and accelerations of other vehicles as taught by Li [0025, [0030]-[0055].
Asakura further teaches one dimensional lane graph an axis representing a longitudinal space of a lane corresponding to the machine compressed with at least one lane corresponding to the at least one object defining the candidate gap, respective forward projections along the axis ([0102] and figure 10 disclosing a one dimensional lane graph, which incorporates just the one dimension of a lane instead of two dimensions wherein all lanes are treated as one, i.e., compressed and just considering the longitudinal dimension).
Evaluating the respective forward projections within the one-dimensional lane graph ([0102] and figure 10 disclosing a one dimensional lane graph, which incorporates just the one dimension of a lane instead of two dimensions wherein all lanes are treated as one, i.e., compressed and just considering the longitudinal dimension).
It would have been obvious to one of ordinary skill in the art to combine/substitute the teaching of the lane graph as taught by Asakura with the prediction of gaps of Li yielding predictable results simplifying the lane determination condition into finding an appropriate gap in the longitudinal dimension thus improving and making more efficient lane changes.
Regarding claim 2, Nishira as modified by Li and Asakura teaches the method of claim 1, wherein the at least one object includes a first object and a second object (Nishira [0066] disclosing appropriate lane change condition between two vehicles given as a relationship between the positions of the host vehicle Xo and the predicted positions of the other two vehicles corresponding to a gap for a lane change. [0070] disclosing predicting the state quantity within a predicted period and determining if the speed of the vehicle allows meeting the lane changeable condition to execute the lane change. As shown in figure 9, the appropriate speed of the vehicle that satisfies the condition. See also [0050] disclosing the appropriate speed range allows the lane change into a gap between other vehicles on the target lane within a predetermined margin. It is interpreted from the citations that the chosen speed sets allow the vehicle to execute a lane change within a safety margin of the two vehicles in the gap based on the prediction of the locations of the vehicles during a period of time and the prediction of the location of the own vehicle by the projection of vehicle position based on a speed profile. See also 0053] discloses the appropriate speed range for the gap based on an appropriate lane change condition as to a positional relationship between the host vehicle and the other vehicle where the equation is based on an x value which relates to longitudinal travel as shown in figure 4. [0055] disclosing determining a condition where the host vehicle would travel at a position ahead of the other vehicle by a headway time. [0056] disclosing predicting the change of state quantity in relationship and [0057] disclosing determining appropriate speed range that reaches the lane changeable condition within a predicted period in time, i.e., filtering set of speed profiles and only selecting a set that are the appropriate speed range).
Asakura further teaches and the respective forward projections on the axis a first position of the first object at a future time, a second position of the second object at a future time and a third position of the machine at the future time (fig. 10 and [0102] disclosing the future projection of the vehicle position and at least two other objects).
Nishira already teaches projection along the longitudinal dimensions to find an appropriate gap [0053]-[0075], thus, It would have been obvious to one of ordinary skill in the art to combine/substitute the teaching of the lane graph as taught by Asakura with the prediction of gaps of Nishira yielding predictable results simplifying the lane determination condition into finding an appropriate gap in the longitudinal dimension thus improving and making more efficient lane changes.
Regarding claim 5, Nishira as modified by Li and Asakura teaches the method of claim 1,
Wherein the speed profile is selected based at least one the evaluating indicating the machine would not reach the candidate gap using one or more speed profiles (Nishira [0066] disclosing appropriate lane change condition between two vehicles given as a relationship between the positions of the host vehicle Xo and the predicted positions of the other two vehicles corresponding to a gap for a lane change. [0070] disclosing predicting the state quantity within a predicted period and determining if the speed of the vehicle allows meeting the lane changeable condition to execute the lane change. See also [0071] disclosing the condition where the position of the host vehicle is between the position of the leading vehicle and trailing vehicle on the target lane within a safety margin has to be satisfied. As shown in figure 9, the appropriate speed of the vehicle that satisfies the condition. It is interpreted from the citations that the speed profiles that do not meet the criteria for the lane change within the safety margin are filtered out and thus speed profiles that do not reach the lane change condition are filtered out. See also [0050] disclosing the appropriate speed range allows the lane change into a gap between other vehicles on the target lane within a predetermined margin. [0055] disclosing the margin is a time to collision. It is interpreted from the citations that the chosen speed sets allow the vehicle to execute a lane change within a safety margin where the time to collision is greater than a threshold ,i.e., avoiding collision, of the two vehicles in the gap based on the prediction of the locations of the vehicles during a period of time and the prediction of the location of the own vehicle by the projection of vehicle position based on a speed profile, i.e., the filtered set eliminates the trajectories that cause an intersection in positions of the projected positions of the other two vehicles and the host vehicle “machine”. See also 0053] discloses the appropriate speed range for the gap based on an appropriate lane change condition as to a positional relationship between the host vehicle and the other vehicle where the equation is based on an x value which relates to longitudinal travel as shown in figure 4. [0055] disclosing determining a condition where the host vehicle would travel at a position ahead of the other vehicle by a headway time. [0056] disclosing predicting the change of state quantity in relationship and [0057] disclosing determining appropriate speed range that reaches the lane changeable condition within a predicted period in time, i.e., filtering set of speed profiles and only selecting a set that are the appropriate speed range)
Regarding claim 7, Nishira as modified by Li further teaches the method of claim 1, further comprising:
Specifically Li teaches ranking the set of speed profiles based at least on the evaluating (Li [0055]-[0056] disclosing ranking each trajectory by a cost and trajectory with the lowest cost is selected. The cost is based on a speed difference of the speed profile of the trajectory, i.e., ranking the set of speed profiles); and
selecting the speed profile based at least on the ranking ([0056] disclosing selecting the trajectory with the lowest cost, i.e., based on the ranking, the selection of the speed profile is interpreted as selecting it as a filtered profile to use based on the ranking).
It would have been obvious to one of ordinary skill in the art to combine/substitute the teaching of Li of selecting the speed profile based on a raking with the selection method of Nishira yielding predictable results in order to select the lowest cost speed profile in order to select the best candidate speed profile as taught by Li [0056].
Regarding claim 11, Nishira as modified by Li and Asakura teaches the at least one processor of claim 10, wherein the speed profile is selected from the speed profiles based at least on the evaluating indicating the machine is capable of reaching the candidate gap using the speed profile (Nishira [0066] disclosing appropriate lane change condition between two vehicles given as a relationship between the positions of the host vehicle Xo and the predicted positions of the other two vehicles corresponding to a gap for a lane change. [0070] disclosing predicting the state quantity within a predicted period and determining if the speed of the vehicle allows meeting the lane changeable condition to execute the lane change. As shown in figure 9, the appropriate speed of the vehicle that satisfies the condition. See also [0050] disclosing the appropriate speed range allows the lane change into a gap between other vehicles on the target lane within a predetermined margin. It is interpreted from the citations that the chosen speed sets allow the vehicle to execute a lane change within a safety margin of the two vehicles in the gap based on the prediction of the locations of the vehicles during a period of time and the prediction of the location of the own vehicle by the projection of vehicle position based on a speed profile. See also 0053] discloses the appropriate speed range for the gap based on an appropriate lane change condition as to a positional relationship between the host vehicle and the other vehicle where the equation is based on an x value which relates to longitudinal travel as shown in figure 4. [0055] disclosing determining a condition where the host vehicle would travel at a position ahead of the other vehicle by a headway time. [0056] disclosing predicting the change of state quantity in relationship and [0057] disclosing determining appropriate speed range that reaches the lane changeable condition within a predicted period in time, i.e., filtering set of speed profiles and only selecting a set that are the appropriate speed range).
Regarding claim 15, Nishira as modified by Li teaches the at least one processor of claim 10, wherein the processor is comprised in at least one of:
control system for an autonomous or semi-autonomous machine (Nishira at least [0102]-[0103] disclosing the control is a lane assist that sets the speed of the vehicle); a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources.
Regarding claim 17, Nishira as modified by Li and Asakura teaches the system of claim 16, wherein the speed profile is selected from the speed profiles based at least on the evaluating indicating the machine is capable of reaching the candidate gap using the speed profile (Nishira [0066] disclosing appropriate lane change condition between two vehicles given as a relationship between the positions of the host vehicle Xo and the predicted positions of the other two vehicles corresponding to a gap for a lane change. [0070] disclosing predicting the state quantity within a predicted period and determining if the speed of the vehicle allows meeting the lane changeable condition to execute the lane change. As shown in figure 9, the appropriate speed of the vehicle that satisfies the condition. See also [0050] disclosing the appropriate speed range allows the lane change into a gap between other vehicles on the target lane within a predetermined margin. It is interpreted from the citations that the chosen speed sets allow the vehicle to execute a lane change within a safety margin of the two vehicles in the gap based on the prediction of the locations of the vehicles during a period of time and the prediction of the location of the own vehicle by the projection of vehicle position based on a speed profile. See also 0053] discloses the appropriate speed range for the gap based on an appropriate lane change condition as to a positional relationship between the host vehicle and the other vehicle where the equation is based on an x value which relates to longitudinal travel as shown in figure 4. [0055] disclosing determining a condition where the host vehicle would travel at a position ahead of the other vehicle by a headway time. [0056] disclosing predicting the change of state quantity in relationship and [0057] disclosing determining appropriate speed range that reaches the lane changeable condition within a predicted period in time, i.e., filtering set of speed profiles and only selecting a set that are the appropriate speed range).
Claim 10, 16 are rejected for similar reasons as claim 1, see above rejection. At least [0065] of Nishira disclosing a processing unit.
Claims 14 are rejected for similar reasons as claim 5 respectively, see above rejection.
Claims 20 are rejected for similar reasons as claims 15 respectively, see above rejection.
Claims 3, 6, 9, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable by Nishira (US20050256630) in view of Li (US20200307589) and Asakura (US20170313313) and Pan (US20210181742) and Pierson (US20190354109).
Regarding claim 3, Nishira as modified by Li and Asakura does not teach the method of claim 1, wherein the respective forward projections are computed by a one-dimensional longitudinal projection of beads respectively corresponding to the at least one first position and the at least one second position in a two-dimensional top down view of the environment.
Pan teaches wherein the respective forward projections are computed by a one-dimensional longitudinal projection ([0055]-[0062], fig. 9a-9b disclosing the one dimensional lane representation corresponds to the two dimensional top down environment trajectory of 9A).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Nishira as modified by Li and Asakura to combine/substitute the teaching of Pan of the longitudinal projections computed corresponding to at least first and second position in a two dimensional top down yielding predictable results allowing the representation of surrounding lane vehicles on the graph improving safety and efficient lane changes. While the trajectory is shown as one line, it is obvious to try and represent trajectories as points, i.e., beads with reasonable expectations of success.
Pierson teaches the projection of beads (Fig. 6, and [0102]-[0110] disclosing the representation of vehicles as nodes, beads, and ensuring no collision with nodes).
It would have been obvious to one of ordinary skill in the art to combine/substitute the representation of a trajectory or projections as beads in order to represent occupancy maps allowing to determine collision free lane changes yielding predictable results improving safety and refining lane changes and verifying collision free paths.
Regarding claim 6, Nishira as modified by Li and Asakura teaches the method of claim 1, but does not teach wherein the respective forward projections comprise, for each time of a plurality of future times, a respective one-dimensional longitudinal projections.
Pan teaches wherein the respective forward projections comprise, for each time of a plurality of future times, a respective one-dimensional longitudinal projections ([0055]-[0062], fig. 9a-9b disclosing the one dimensional lane representation corresponds to the two dimensional top down environment trajectory of 9A).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Nishira as modified by Li and Asakura to combine/substitute the teaching of Pan of the longitudinal projections computed corresponding to at least first and second position in a two dimensional top down yielding predictable results allowing the representation of surrounding lane vehicles on the graph improving safety and efficient lane changes. While the trajectory is shown as one line, it is obvious to try and represent trajectories as points, i.e., beads with reasonable expectations of success.
Pierson teaches the projection of beads (Fig. 6, and [0102]-[0110] disclosing the representation of vehicles as nodes, beads, and ensuring no collision with nodes).
It would have been obvious to one of ordinary skill in the art to combine/substitute the representation of a trajectory or projections as beads in order to represent occupancy maps allowing to determine collision free lane changes yielding predictable results improving safety and refining lane changes and verifying collision free paths.
Regarding claim 9, Nishira as modified by Li and Asakura teaches the method of claim 1, but does not teach wherein the evaluating the respective forward projections within the one-dimensional lane graph comprises, determining for each time of a plurality of future times, whether a respective bead representing the machine at least partially overlaps on the axis with a respective bead representing the at least one object.
wherein the evaluating the respective forward projections within the one-dimensional lane graph comprises, determining for each time of a plurality of future times, whether a respective ([0055]-[0062], fig. 9a-9b disclosing the one dimensional lane representation and determining the trajectory of the lane change end up in a position where the trajectories do not intersect).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Nishira as modified by Li and Asakura to combine/substitute the teaching of Pan of the longitudinal projections computed corresponding to at least first and second position in a two dimensional top down yielding predictable results allowing the representation of surrounding lane vehicles on the graph improving safety and efficient lane changes. While the trajectory is shown as one line, it is obvious to try and represent trajectories as points, i.e., beads with reasonable expectations of success.
Pierson teaches the projection of beads and determining the beads do not intersect (Fig. 6, and [0102]-[0110] disclosing the representation of vehicles as nodes, beads, and ensuring no collision with nodes).
It would have been obvious to one of ordinary skill in the art to combine/substitute the representation of a trajectory or projections as beads in order to represent occupancy maps allowing to determine collision free lane changes yielding predictable results improving safety and refining lane changes and verifying collision free paths.
Claim 12, 18 are rejected for similar reasons as claim 3, see above rejection.
Claims 4, 13, 19 are rejected under 35 U.S.C. 103 as being unpatentable by Li (US20200307589) in view of Nishira (US20050256630) and Asakura (US20170313313) and Wongpiromsarn (US20200189575).
Regarding claim 4, Nishira as modified by Li teaches the method of claim 1. Nishira as modified by Li does not teach wherein the speed profile is selected based at least on evaluating indicating the machine would collide with one or more objects using the one or more speed profiles.
Wongpiromsarn teaches wherein wherein the speed profile is selected based at least on evaluating indicating the machine would collide with one or more objects using the one or more speed profiles ([0114] disclosing motion constraint that removes speed profiles that can cause collision with another vehicles and keeps the range of speed profiles that will ensure no collision with other vehicles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wongpiromsarn with the method of selection of speed profiles of Nishira yielding predictable results in order to avoid collisions as taught by Wongpiromsarn [0114].
Claims 13 and 19 are rejected for similar reasons as claim 4, see above rejection.
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable by Li (US20200307589) in view of Nishira (US20050256630) and Asakura (US20170313313) and Jafari (US20210074162 from IDS).
Regarding claim 8, Nishira as modified by Li teaches the method of claim 1, further comprising based at least on the evaluating, selecting, for a lane change maneuver, the candidate gap from a plurality of lane change gaps (Nishira [0102] disclosing selecting the target gap).
Nishira as modified by Li does not teach wherein the selecting the speed profile is based at least on the selecting of the candidate gap for the lane change maneuver.
Jafari teaches wherein the selecting the speed profile is based at least on the selecting of the candidate gap for the lane change maneuver ([0061] disclosing selecting optimal actions for lane change including velocity profiles. [0063] disclosing selecting the optimal action, i.e., speed profile, for the target gap).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined or substituted the teaching of Nishira as modified by Li to combine the teaching of Jafari of wherein the selecting the speed profile is based at least on the selecting of the candidate gap for the lane change maneuver, yielding predictable restuls, in order to select the optimal action for the lane change taking in consideration the feasibility, safety and comfort requirements for a gap as taught by Jafari [0060].
Response to Arguments
Applicant’s arguments filed on 03/17/2026 has been fully considered but they are not persuasive.
the arguments are moot since the rejection relies on new prior art for teaching the amended limitations of the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods.
US20220073076 disclosing selecting a speed based on the preceding vehicle speed and following vehicle speed of a target gap.
US9475491 disclosing controlling the speed to match the target speed of the vehicle corresponding to the gap.
US20200189598 disclosing the adjustment of speed to go into gap
US20200331476 discloses ranking trajectories and choosing best trajectory.
US20170102705 discloses minimum and maximum target position for a lane change based on predicted other vehicle’s positions.
US20210269041 discloses predicting position of the vehicles and a safety margin for a lane change between them.
US11027735 discloses predicting target position for a lane change based on future predicted positions of other vehicles forming a gap.
US20190329777 discloses controlling other vehicles to increase the available gap for a lane change based on their predicted positions.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi Tran can be reached on (571) 272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMAD O EL SAYAH/Examiner, Art Unit 3664B
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