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
This Office Action is in response to the claims filed on February 4, 2026.
Claims 1-15 have been presented for examination.
Claims 1-15 are currently rejected.
Claims 1, 4-6, 8-10, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Alalao et al. (U.S. Patent Publication Number 2020/0041994).
Claims 2-3, 7, 11-12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Alalao et al. (U.S. Patent Publication Number 2020/0041994) in view of Islam et al. (U.S. Patent Publication Number 2022/0179434).
Response to Argument
Claim Objections
The amendments to the claims overcome the claim objections. Accordingly, the claim objections are withdrawn.
35 U.S.C. 102
Applicant's arguments filed on February 4, 2026 with respect to 35 U.S.C. 102 have been fully considered but they are not persuasive.
The Applicant argues that the cited references do not describe a “method of assessing vehicle trajectories for deadlock scenarios in a site where multiple vehicles operate by following planned vehicle trajectories” (see Applicant Remarks page 7-8). The Applicant then appears to describe the disclosure of Alalao and the present claims.
The Examiner has considered the arguments presented and respectfully disagrees. First, the Applicant arguments appear to be directed toward the preamble of the claim. However, the MPEP states that “If the body of a claim fully and intrinsically sets forth all of the limitations of the claimed invention, and the preamble merely states, for example, the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, then the preamble is not considered a limitation and is of no significance to claim construction. Shoes by Firebug LLC v. Stride Rite Children’s Grp., LLC, 962 F.3d 1362, 2020 USPQ2d 10701 (Fed. Cir. 2020).” The preamble provided in claim 1 merely describes the purpose of the recited method, which is to assess vehicle trajectories for deadlock scenarios. Therefore, the preamble is not considered a limitation and is of no significance to the claim construction.
Second, the Applicant describes the disclosure of Alalao “in comparison” to the present claims (see Applicant Remarks page 8). However, the Applicant does not perform a comparison and instead merely appears to describe the disclosure of Alalao and separately describe the present claims without drawing a connection between their elements. For example, the Applicant describes that Alalao relates to a computer system for remotely monitoring and controlling operation and deployment of one or more autonomous vehicles, and separately describes that the present claims include a method for assessing vehicle trajectories for deadlock scenarios in a traffic-constrained area. The Applicant does not further explain how the present claims contrast to the teachings of Alalao, nor is contrary evidence provided establishing that the reference being relied on would not enable a skilled artisan to produce the recited limitations. Therefore, the Applicant’s arguments are not persuasive.
Similarly, the Applicant describes Alalao to include an autonomous vehicle encountering a physical obstruction and requesting remote human control, and “In contrast, the present application “aims at assessing vehicle trajectories for deadlock scenarios” (see Applicant Remarks page 9). As discussed above, the Applicant merely appears to describe the disclosure of Alalao and the present claims and conclude that the teachings of Alalao are “in contrast” to the present claimed invention without articulating their differences, nor is contrary evidence provided establishing that the reference being relied on would not enable a skilled artisan to produce the recited limitations. Therefore, the Applicant’s arguments are not persuasive.
The Applicant goes on to argue that the present application involves a systematic problem, and contends that Alalao is “not a systemic graph-based iterative classification of trajectory states” and that the present invention is “proactive in site configuration” whereas Alalao is “reactive to obstacles” (see Applicant Remarks page 9).
The Examiner has considered the arguments presented and respectfully disagrees. First, elements from the written description that are not part of the claim may not be read into the claim when the claim language is broader than the embodiment. Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). The claims, as written, do not expressly describe a “systematic problem,” nor does the Applicant define parameters for a problem to be “systematic.” Therefore, any disclosure addressing a defined issue, such as Alalao, may address a “systematic problem.” Further, the claims do not positively recite a “proactive” site configuration, nor is it suggested that the limitations are characterized to be “proactive.” For these reasons, the Applicant’s arguments are not persuasive.
Dependent claims 2-15 further limit the scope of independent claim 1 and are thereby supported under the same rationale.
For these reasons, the Examiner maintains the prior art rejection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 8-10, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Alalao et al. (U.S. Patent Publication Number 2020/0041994).
Regarding claim 1, Alalao discloses the computer-implemented method of assessing vehicle trajectories for deadlock scenarios in a site where multiple vehicles operate by following planned vehicle trajectories, wherein the site includes at least one traffic-constrained area via which at least two of the multiple vehicles passes when following their planned trajectories, the method comprising:
generating a starting vehicle state for each of the multiple vehicles (Alalao ¶ 92 “provide navigation instructions to each of the autonomous vehicles (e.g., provide a route or path between two locations ...”)
the starting vehicle state of each vehicle corresponding to placing each vehicle at a start position on its planned trajectory, (Alalao ¶ 100 discloses generating a trajectory from a starting location to navigate from a first location to a second location)
wherein each vehicle’s planned trajectory in the site is represented using a connected directed graph of the site, and (Alalao ¶ 150 discloses a directed graph 1000 used in path planning which “represent different metropolitan areas,” see Fig. 10 and ¶ 151)
wherein the starting vehicle state is represented by a starting node of the connected directed graph (Alalao Fig. 10), iteratively updating the states of the multiple vehicles operating in the site from each vehicle’s start position as the vehicles follow their planned trajectory, (Alalao ¶ 106 discloses generating data in “real-time [i.e., updated iteratively]” to “support operations of the AV,” wherein the data includes vehicle telemetry data indicating “a geographical location of the vehicle,” and “information regarding a route of the selected vehicle,” which are states of multiple vehicles operating in the site see ¶¶ 5 and 18. Also see ¶ 213 describing collecting information from other autonomous vehicles in the proximity.)
wherein with each iteration, for each vehicle: each vehicle’s state is updated to a new vehicle state which indicates either: the vehicle occupying another a further node along the connected directed graph to the node occupied by the vehicle in the previous iteration, or the vehicle occupying the node of the previous iteration; and (Alalao ¶ 116 discloses “the remotely located database 134 stores and transmits historical information about driving properties (e.g., speed and acceleration profiles) of vehicles that have previously traveled along trajectory 198 at similar times of day.”)
classifying, if the new vehicle state represents a current vehicle deadlock at the site, the new vehicle state as an explicit deadlock state, (Alalao ¶ 19 discloses receiving an indication that the path of the vehicle is obstructed [i.e., deadlock at the site] and presenting “an indication that a path of the first vehicle is obstructed [i.e., classifying a new vehicle state as an explicit deadlock state].” One having ordinary skill in the art would recognize that determining that the path is obstructed is classifying the vehicle state to be in the deadlock state, as opposed to having nothing obstructing the road. Also see ¶ 146. The Examiner notes that the limitation contains a contingent limitation (e.g., “if”) and is therefore not required to be performed under the broadest reasonable interpretation of the claim. See MPEP 2111.04.)
storing the new vehicle state as an explicit deadlock state in a first classification memory, (Alalao ¶ 112 discloses “AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121,” such that “the data storage unit 142 [stores] real-time ... information about the environment 190”)
classifying, if the new vehicle state represents a future vehicle deadlock at the site, the vehicle state as an implicit deadlock state; and (Alalao ¶ 146 discloses “the LiDAR system 602 will continue to detect light reflected by the next valid ground point 806 [i.e., a future vehicle deadlock as an implicit deadlock state] if nothing is obstructing the road” or “if an object 808 obstructs the road,” also see Fig. 8. One having ordinary skill in the art would recognize that determining to continue to detect light based on the determination that nothing is obstructing the road is a classification. The Examiner notes that the limitation contains a contingent limitation (e.g., “if”) and is therefore not required to be performed under the broadest reasonable interpretation of the claim. See MPEP 2111.04.)
storing the new vehicle state as an implicit deadlock state in a second classification memory. (Alalao ¶ 112 discloses “memory 144 [stores] predictive [i.e., implicit] information about the environment 190”)
Regarding claim 4, Alalao discloses the computer-implemented method according to claim 1, wherein:
an explicit deadlock state fulfils the condition that a set of vehicles block each other from moving along their planned trajectories. (Alalao ¶ 19 discloses receiving an indication that the path of the vehicle is obstructed [i.e., deadlock at the site] and presenting “an indication that a path of the first vehicle is obstructed [i.e., a new vehicle state as an explicit deadlock state],” wherein the object includes another car, see ¶ 152. Also see ¶ 146.)
Regarding claim 5, Alalao discloses the computer-implemented method according to claim 1, wherein:
the vehicles are autonomous vehicles following planned trajectories. (Alalao ¶ 108 “AV system 120 operates the AV 100 autonomously or semi-autonomously along a trajectory 198 through an environment 190 to a destination 199”)
Regarding claim 6, Alalao discloses the computer-implemented method according to claim 1, wherein:
the vehicles are manually operated vehicles following planned trajectories. (Alalao ¶ 107 discloses a vehicle having Level 1 automation, in which the driver operates the vehicle manually, see “What are the different levels of vehicle automation?”)
Regarding claim 8, Alalao discloses the computer-implemented method according to claim 1, wherein the method comprises:
identifying the nodes along the vehicle trajectories, where the vehicles have the greatest likelihood of encountering traffic deadlock situations. (Alalao ¶ 232 discloses “the map portion 3002 can indicate whether there is a slow down or delay along the autonomous vehicle's route or path. For example, if there is traffic congestion slowing down the autonomous vehicle [i.e., a location having the greatest likelihood of encountering traffic deadlock], the map portion 3002 can indicate the slow down [i.e., by indicating the path 3006],” wherein the locations are represented by nodes, see at least ¶ 151 and Fig. 10. Also see ¶ 95 disclosing that a detected condition includes an “imminent collision [i.e., high likelihood of encountering deadlock]”)
Regarding claim 9, Alalao discloses the computer-implemented method according to claim 1, wherein the method comprises:
minimising the likelihood of deadlocks by adjusting one or more or all of: the number of vehicles; their vehicle trajectories; and one or more traffic constraint rules along the vehicle trajectories. (Alalao ¶ 204 discloses “the user can input a path for the autonomous vehicle 1302a to navigate around and/or avoid the obstruction 1600,” wherein the “the path that is input by the user is modified before transmission to the autonomous vehicle,” see ¶ 213. One having ordinary skill in the art would recognize that avoiding an obstruction minimizes the likelihood of deadlock. Also see Fig. 21.)
Regarding claim 10, Alalao discloses a computer-implemented traffic planner (Alalao ¶ 135 “planning module 404, see Fig. 4) for planning a plurality of planned vehicle trajectories for vehicles within a site having at least one traffic-constrained location, the traffic planner comprising:
a deadlock classifier model configured to classify a traffic situation for vehicle traffic following planned vehicle trajectories within the site, (Alalao ¶ 117 discloses that “Computing devices 146 located on the AV 100 algorithmically [i.e., uses a model to] generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities, which includes presenting “an indication that a path of the first vehicle is obstructed [i.e., classifying a traffic situation],” see ¶ 19, wherein the vehicle travels autonomously along the trajectory, see ¶ 108. One having ordinary skill in the art would recognize that determining that the path is obstructed is classifying a traffic situation as being obstructed. Further, one having ordinary skill in the art would recognize that generating control actions algorithmically would involve using a model, as a model is merely a system of data, see Merriam-Webster; therefore, “algorithmically” includes a model.)
wherein the deadlock classifier model comprises first classification memory and a second classification memory, (Alalao ¶ 112 discloses “a data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190” including “traffic congestion updates”)
wherein the traffic planner is configured, based on classifications generated by the method according to claim 1, to populate the first classification memory with explicit deadlock states and the second classification memory with implicit deadlock states until all explicit and implicit deadlock states are found, (Alalao ¶ 112 discloses using a data storage unit 142 [i.e., first classification memory] and memory 144 [i.e., a second classification memory] to store “real-time [i.e., explicit]” and “predictive [i.e., implicit] information about the environment,” the environment information including objects and obstructions in proximity to the vehicle, see at least ¶ 223, such that “the AV 100 has access to all relevant navigation information provided by these objects” by obtaining “information about as many physical objects ... as possible,” see ¶ 142).
wherein the traffic planner is configured, based on the stored explicit and implicit deadlock states in the first classification memory and the second classification memory, to plan an action to perform along each planned vehicle trajectory in the site. (Alalao ¶ 148 discloses that the planning module outputs route planning data 908 used to traverse segments of route 902 based on conditions of the segment at a particular time, wherein “the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information,” see ¶ 117. For example, if the segment includes unexpected traffic, the speed constraint of the AV may limit the travel speed for the segment, see ¶ 148.)
Regarding claim 13, Alalao discloses the computer program product comprising program code for performing, when executed by the processing circuitry, the method of claim 1. (Alalao in at least ¶¶ 56 and 123)
Regarding claim 14, Alalao discloses the non-transitory computer-readable storage medium comprising instructions, which when executed by the processing circuitry, cause the processing circuitry to perform the method of claim 1. (Alalao in at least ¶ 127)
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 2-3, 7, 11-12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Alalao et al. (U.S. Patent Publication Number 2020/0041994) in view of Islam et al. (U.S. Patent Publication Number 2022/0179434).
Regarding claim 2, Alalao does not expressly disclose the computer-implemented method according to claim 1, wherein:
iteratively updating the states of the multiple vehicles comprises: selecting the other node positions for the multiple vehicles randomly or pseudo-randomly with each iteration.
However, Islam discloses:
iteratively updating the states of the multiple vehicles comprises: selecting the other node positions for the multiple vehicles randomly or pseudo-randomly with each iteration. (Islam ¶ 28 discloses performing “a selection between the activation time sets of such simulation, according to a suitable criterium, e.g. a random selection,” wherein “repeating the step [i.e., each iteration] of creating a set of starting times comprises changing one or more of the starting times by one or more predetermined time intervals Δt,” see ¶ 84)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the iterative updating of Alalao with selecting other node positions for vehicles randomly, as disclosed by Islam, with reasonable expectation of success, to allow for obtaining an optimized scheduling, even where there is a relatively large number of vehicles (Islam ¶ 22), rendering the limitation to be an obvious modification.
Regarding claim 3, Alalao does not expressly disclose the computer-implemented method according to claim 1, wherein
iteratively updating the states of the multiple vehicles comprises: selecting the other node positions for the multiple vehicles to maximise their moved distance with each iteration.
However, Islam discloses:
iteratively updating the states of the multiple vehicles comprises: selecting the other node positions for the multiple vehicles to maximise their moved distance with each iteration. (Islam ¶ 7 dislcoses “selecting, for controlling the vehicles, from the sets of activation times created by the repetition [i.e., each iteration] of the step of creating a set of activation times, a set of activation times” for the vehicle to depart from its starting position, also see ¶ 15, such that “the selected set of activation times may minimize the staying durations [i.e., maximize moved distance],” see ¶ 36. The system of Islam is executed on a control unit CU which is a computer, see ¶ 76. The Examiner notes that the limitation “to maximize” appears to recite an intended use and is not required under the broadest reasonable interpretation of the claim.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the iterative updating of Alalao with selecting other node positions for vehicles randomly, as disclosed by Islam, with reasonable expectation of success, to allow for obtaining an optimized scheduling, even where there is a relatively large number of vehicles (Islam ¶ 22), rendering the limitation to be an obvious modification.
Regarding claim 7, Alalao does not expressly disclose the computer-implemented method according to claim 1, wherein:
the traffic-constrained area is a single traffic lane.
However, Islam discloses:
the traffic-constrained area is a single traffic lane. (Islam ¶ 17 “a portion of the route is on a road, on which the vehicles move in both directions, and which has at least one portion with a single lane,” also see Fig. 2)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the traffic-constrained area of Alalao to be a single traffic lane, as disclosed by Islam, with reasonable expectation of success, because such a method provides for avoiding collisions between vehicles in the single lane portion (Islam ¶ 17), rendering the limitation to be an obvious modification.
Regarding claim 11, Alalao does not expressly disclose the computer-implemented traffic planner according to claim 10, wherein:
the traffic planner is configured, based on the stored explicit and implicit deadlock states in the first and second classification memories, to determine if a planned design of a site where multiple vehicles will operate by following planned vehicle trajectories will cause implicit and/or explicit deadlocks.
However, Islam discloses:
the traffic planner is configured, based on the stored explicit and implicit deadlock states in the first and second classification memories, to determine if a planned design of a site where multiple vehicles will operate by following planned vehicle trajectories will cause implicit and/or explicit deadlocks. (Islam ¶ 28 discloses “selecting, for controlling the vehicles, a set of activation times may comprise selecting the set of activation times for which the simulation shows that there is no positive time overlap of vehicles at any of the at least one single vehicle area,” such that “vehicles driving in opposite directions cannot meet in the single lane portions,” see ¶ 72, thereby determining that the planned design will not cause implicit or explicit deadlocks.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the planning module of Alalao with the control unit of Islam to determine if a planned design of a site where multiple vehicles will operate by following planned vehicle trajectories will cause implicit and/or explicit deadlocks, as disclosed by Islam, with reasonable expectation of success, to improve control of a vehicle fleet and provide a way to plan, simultaneously for all vehicles, vehicle movements, without having to adjust the speed of any vehicle (Islam ¶ 22), rendering the limitation to be an obvious modification.
Regarding claim 12, Alalao does not expressly disclose the computer-implemented traffic planner of claim 10, wherein:
the traffic-constrained location within the site through which the plurality of planned vehicle trajectories pass, comprises a one-way section along which at least two vehicle trajectories pass in different directions.
However, Islam discloses:
the traffic-constrained location within the site through which the plurality of planned vehicle trajectories pass, comprises a one-way section along which at least two vehicle trajectories pass in different directions. (Islam ¶ 72 discloses “vehicles driving in opposite directions cannot meet in the single lane portions,” also see Fig. 2)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the traffic-constrained location of Alalao with the location being a site through which the plurality of planned vehicle trajectories pass, comprises a one-way section along which at least two vehicle trajectories pass in different directions, as disclosed by Islam, with reasonable expectation of success because such a method provides for avoiding collisions between vehicles in the single lane portion (Islam ¶ 17), rendering the limitation to be an obvious modification.
Regarding claim 15, Alalao discloses the method of configuring a site layout, wherein a plurality of heavy-duty vehicles follow planned trajectories between a plurality of locations within the site, the method comprising:
performing a method according to claim 1 to determine if vehicle traffic comprising a plurality of vehicles following a plurality of predetermined vehicle trajectories in a site will lead to a vehicle deadlock; and (Islam ¶ 28 discloses “selecting, for controlling the vehicles, a set of activation times may comprise selecting the set of activation times for which the simulation shows that there is no positive time overlap of vehicles at any of the at least one single vehicle area,” such that “vehicles driving in opposite directions cannot meet in the single lane portions,” see ¶ 72, thereby determining that the planned design will not cause implicit or explicit deadlocks.)
if so, modifying the locations on the site until the predetermined vehicle trajectories do not lead to a deadlock. (Islam ¶¶ 16 and 31 discloses changing an activation time, wherein the activation time corresponds to a resumption position; therefore, changing the activation time would modify the location of the vehicle with respect to the one single vehicle area, such that “vehicles driving in opposite directions cannot meet in the single lane portions,” see ¶ 72. The Examiner notes that the limitation contains a contingent limitation (e.g., “if so”) and is therefore not required to be performed under the broadest reasonable interpretation of the claim. See MPEP 2111.04.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have combined the planning module of Alalao with the control unit of Islam to determine if a planned design of a site where multiple vehicles will operate by following planned vehicle trajectories will cause implicit and/or explicit deadlocks, as disclosed by Islam, with reasonable expectation of success, to improve control of a vehicle fleet and provide a way to plan, simultaneously for all vehicles, vehicle movements, without having to adjust the speed of any vehicle (Islam ¶ 22), rendering the limitation to be an obvious modification.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STEPHANIE T SU/Patent Examiner, Art Unit 3662