DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Pending
1-3, 6-12
Cancelled
4-5
35 U.S.C. 103
1-3, 6-12
Response to Amendment
This office action is in response to applicant’s arguments and amendments filed 02/12/2026, which are in response to USPTO Office Action mailed 12/16/2025. Applicant’s arguments and amendments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-3, 6-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stolfus (US 2018/0309592 A1, “Stolfus”) and further in view of Abdel-Aty et al. (US 2021/0217307 A1, “Abdel”).
Regarding claim 1: Stolfus teaches: A vehicle supervision device, comprising: ([0008]; [0067]; [0075])
a communication interface that communicates externally; and one or more processors configured to: ([0117]; [0069]; [0176]; [0075] processors)
acquire obstacle information generated by a plurality of externally-provided obstacle detectors, through the communication interface, to thereby ([0013]; [0070]-[0071])
individually identify autonomous driving vehicles and obstacles other than these vehicles; ([0008]; [0013]; [0071]; [0079]; Fig. 2)
[. . .];
evaluate a congestion degree for each area [. . .]; ([0083]-[0084]; [0092]; [0097]; [0127])
[. . .]; and
[. . .], send to said autonomous driving vehicle, an instruction for causing that vehicle to avoid entering the at least one congested area, through the communication interface ([0008]; [0112]; [0175]; [0097]; [0117]).
However, Stolfus does not explicitly teach: generate a time-series map that tracks occupation areas of the autonomous driving vehicles and obstacles at each of a plurality of time points, based on data from an obstacle- position statistic database; evaluate a congestion degree for each area based on a current occupation and predicted future positions of the identified autonomous driving vehicles and obstacles; extract at least one congested area from the time-series map where the congestion degree greater than or equal to a predetermined threshold and determine whether an autonomous driving vehicle is expected to enter the at least one congested area; and based on a determination that the autonomous driving vehicle is expected to enter the at least one congested area, send to said autonomous driving vehicle, an instruction for causing that vehicle to avoid entering the at least one congested area.
Abdel teaches: generate a time-series map that tracks occupation areas of the autonomous driving vehicles and obstacles at each of a plurality of time points, based on data from an obstacle- position statistic database; ([0041] user interfaces for operation of the system and decision-making within system, including real-time traffic info, high-risk locations, critical driving events, high or severe crash risk locations. plots of temporal safety conditions based on actual and predicted crashes, and critical driving events. [0082] upstream and downstream traffic volume data are visualized in real-time. FIG. 13B difference between upstream and downstream volumes, downstream station is more congested than upstream. These kinds of visualization outputs could assist the traffic operator to better monitor the real-time traffic safety conditions, as well as understand the reason why the predicted road safety situations change, which could also improve the efficiency and accuracy of the short-term decision-making process)
evaluate a congestion degree for each area based on a current occupation and predicted future positions of the identified autonomous driving vehicles and obstacles; ([0041] plots of temporal safety conditions based on actual and predicted crashes, and critical driving events. [0063] tracks history of implementations of PATM strategies and provides visualization of locations where problem and countermeasure are repeated frequently. [0069] decision safety support system can suggest most effective countermeasures for each identified hotspot crash location based on its attributes. [0080] To monitor real-time traffic safety conditions, and traffic status of selected segment, operators could click “Real-Time Status” tab, then real-time risk, speed, volume characteristics visualized. bottom left line chart in FIG. 13B shows the real-time crash risk and severe crash risk of a selected segment, which indicates that the crash risk starts to increase from the timestamp 130 minutes. [0082] upstream and downstream traffic volume data are visualized in real-time. FIG. 13B difference between upstream and downstream volumes, downstream station is more congested than upstream. These kinds of visualization outputs could assist the traffic operator to better monitor the real-time traffic safety conditions, as well as understand the reason why the predicted road safety situations change, which could also improve the efficiency and accuracy of the short-term decision-making process)
extract at least one congested area from the time-series map where the congestion degree greater than or equal to a predetermined threshold and ([0080] To monitor real-time traffic safety conditions, and traffic status of selected segment, operators could click “Real-Time Status” tab, then real-time risk, speed, volume characteristics visualized. bottom left line chart in FIG. 13B shows the real-time crash risk and severe crash risk of a selected segment, which indicates that the crash risk starts to increase from the timestamp 130 minutes. [0082] upstream and downstream traffic volume data are visualized in real-time. FIG. 13B difference between upstream and downstream volumes, downstream station is more congested than upstream. These kinds of visualization outputs could assist the traffic operator to better monitor the real-time traffic safety conditions, as well as understand the reason why the predicted road safety situations change, which could also improve the efficiency and accuracy of the short-term decision-making process)
determine whether an autonomous driving vehicle is expected to enter the at least one congested area; and ([0044]-[0047] Real-Time Crash Prediction. [0063] tracks history of implementations of PATM strategies and provides visualization of locations where problem and countermeasure are repeated frequently. icons on map illustrates frequency of high-risk events. Suggestions to decision makers provided to repeat solution at same location(s) automatically for certain time periods to pro-actively alleviate risk. report contains detailed info of segment name, strategy type and historical implementation frequency of last seven days)
based on a determination that the autonomous driving vehicle is expected to enter the at least one congested area, send to said autonomous driving vehicle, an instruction for causing that vehicle to avoid entering the at least one congested area ([0044]-[0047] Real-Time Crash Prediction. [0063] tracks history of implementations of PATM strategies and provides visualization of locations where problem and countermeasure are repeated frequently. icons on map illustrates frequency of high-risk events. Suggestions to decision makers provided to repeat solution at same location(s) automatically for certain time periods to pro-actively alleviate risk. report contains detailed info of segment name, strategy type and historical implementation frequency of last seven days. [0068] Suggestion of Optimal Countermeasure).
Stolfus and Abdel are analogous art to the claimed invention since they are from the similar field of traffic management and collision avoidance. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Stolfus with the aspects of Abdel to create, with a reasonable expectation for success, a vehicle supervision device that generates a time-series map that tracks occupation areas of the autonomous driving vehicles and obstacles at each of a plurality of time points, evaluates a congestion degree for each area, extracts congested areas and determines whether the vehicle is to enter the congested area, and send the vehicle an instruction to avoid the congestion area. The motivation for modification would have been to provide a safety visualization system that utilizes real-time and historical data to efficiently predict traffic events via calculations and suggest traffic safety changes to improve automobile flow and reduce the number of traffic incidents, while employing an easy-to-navigate and effective graphical user interface (Abdel, [0013]).
Regarding claim 2: Stolfus-Abdel further teach: The vehicle supervision device as set forth in wherein the one or more processors are further configured to acquire through the communication interface, a part of the obstacle information transmitted from a vehicle provided with the obstacle detector that generates nearby obstacle information on a basis of an output of an in-vehicle sensor, and (Stolfus: [0013]; [0075]; [0113]; [0123]; [0101])
a part of the obstacle information transmitted from a roadside monitoring device provided with the obstacle detector that generates nearby obstacle information on a basis of an output of a roadside sensor (Stolfus: [0134]; [0174]; [0071]; [0126]).
Regarding claim 3: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 1, further comprising: an action plan storage that acquires and stores, through the communication interface, action plans of the respective autonomous driving vehicles that contain information of their traveling routes and estimated transit time points, (Stolfus: [0117] present alternate route to entities via COMMs associated with entity; [0073] provide specific alternate route to entities; adjust alternate route provided via COMMs; [0075] dispatch server, processor, memory, COMMs, transmits/receives info. [0073] adjust alternate route provided via COMMs; [0091] provide alternate routes to node sections based on number of entities in each section at a given time; [0092] completely congested (100%) due to traffic condition; [0123] optimal routing, alternative routes based on real-time traffic data; [0174] receive real-time traffic data from traffic control devices; [0081] assumed destination, temporary destination, based on travel info of entity (path, speed, direction, exit nodes along path); [0175] providing anticipatory routing to entities along the path having the turbulence),
wherein the one or more processors are further configured to evaluate the congestion degree for each area at each time point, (Stolfus: [0074] traffic states (normal, impacted, mitigated, recovery, return to normal); [0084] travel path separated into node-to-node distances, evaluation zones; [0126] times associated with detected traffic, durations, expiration, weather, number of vehicles, locations, locations of vehicles, throughput, times associated therewith; [0127] Severity of traffic condition measured against predetermined threshold stored in memory),
based on the identified autonomous driving vehicles and obstacles and the action plans of the respective autonomous driving vehicles stored in the action plan storage. (Stolfus: [0070] TMM has routing engine, traffic rules, memory; receives traffic info from sources (traffic monitors, COMMs), finds alternate routes, provides to COMMs; [0126] times, associated traffic, durations, expiration, number of vehicles, locations, throughput, times associated; [0127] Severity of traffic measured against predetermined threshold stored in memory).
Regarding claim 6: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 3,wherein the one or more processors are further configured to create action-plan correction candidates for the autonomous driving vehicle, and select an optimum action-plan correction candidate from the action-plan correction candidates, to thereby send to said autonomous driving vehicle, an optimum action-plan correction instruction for causing that vehicle to avoid entering the at least one congestion area, through the communication interface (Stolfus: [0083]; [0074]; [0080]; [0090] cost; [0094]; [0117]; [0084]; [0175]; [0097]).
Regarding claim 7: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 6, wherein the one or more processors are further configured to select, as the optimum action-plan correction candidate, the action-plan correction candidate that makes an action-plan completion time of said autonomous driving vehicle earliest (Stolfus: [0083]; [0089]; [0090]; [0094]).
Regarding claim 8: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 6, wherein the one or more processors are further configured to select, as the optimum action-plan correction candidate, the action-plan correction candidate that makes a maximum value of the congestion degrees according to the action-plan correction candidates smallest (Stolfus: [0083]; [0133]; [0136]).
Regarding claim 9: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 6, wherein the one or more processors are further configured to: designate an action-plan objective for determining: (Stolfus: [0083]; [0075])
a departure place, a via place, a destination place, and (Stolfus: [0087]; [0080]; [0105]; [0081]) loading/unloading of goods, (Stolfus: [0019]) as basic elements for creating the action plan of each of the autonomous driving vehicles, and (Stolfus: [0074]; [0094]) time restrictions on the basic elements; and (Stolfus: [0074]; [0094])
create the action-plan correction candidates for said autonomous driving vehicle on a basis of the action-plan objective (Stolfus: [0081]; [0083]).
Regarding claim 10: Stolfus-Abdel further teach: The vehicle supervision device as set forth in Claim 6, wherein the one or more processors are further configured to: designate a selection index for selecting one of the action-plan correction candidates for said autonomous driving vehicle; and (Stolfus: [0008]; [0116]; [0075])
select the optimum action-plan correction candidate from among the action-plan correction candidates on a basis of the selection index designated by the selection index designator (Stolfus: [0083]; [0089]; [0090]; [0074]; [0176]).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stolfus (US 2018/0309592 A1, “Stolfus”) and Abdel-Aty et al. (US 2021/0217307 A1, “Abdel”) and further in view of Farid et al. (US 2024/0331532 A1, “Farid”).
Regarding claim 11: Stolfus-Abdel further teach: A vehicle supervision system, comprising: (Stolfus: [0008]; [0067]) the vehicle supervision device as set forth in Claim 1; (Stolfus: [0067]; [0069]; [0070]; [0071]; [0075])
[. . .], that has the obstacle detector and that communicates with the vehicle supervision device through an in-vehicle communication interface; and (Stolfus: [0069]; [0013]; [0071]; [0075]; [0008]; [0117])
a roadside monitoring device that (Stolfus: [0134]; [0013]) includes the obstacle detector and a roadside communication interface configured to communicate with the vehicle supervision device (Stolfus: [0117]; [0134]; [0174]; [0013]; [0019]; [0067]).
However, Stolfus-Abdel does not explicitly teach: an autonomous driving controller that is mounted on each of the autonomous driving vehicles.
Farid teaches: an autonomous driving controller that is mounted on each of the autonomous driving vehicles ([0058] server generates and sends instructions to autonomous vehicle which actually trigger/control AV to move to different location to avoid congestion; messages delivered from server to transport via network; [0065] sending control instructions that cause/control vehicles to move to different route to avoid congestion; control instructions transmitted to autonomous vehicles and/or human-driven vehicles; [0079] transports communicate with one another via processors and transceivers, transmitters, receivers, storage, sensors, and COMMs; occur directly, or via other transports; processors communicate with sensor, wired device, wireless device, database, processor, memory, software; [0088] processors and/or computer readable medium may fully or partially reside in interior or exterior of transports).
Stolfus-Abdel and Farid are analogous art to the claimed invention since they are from the similar field of vehicle traffic control and congestion management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Stolfus-Abdel with the aspects of Farid to create, with a reasonable expectation for success, a vehicle supervision system and device that includes a controller (for autonomous driving or manual driving) mounted to autonomous vehicles that communicates with said supervision system. The motivation for modification would have been to more effectively manage congestion associated with metropolitan areas, varying road traffic conditions, and dynamic distributions of connected vehicles, in order to maximize the performance of entire networks, and efficiently control large scale traffic systems to reduce congestion and improve traffic conditions (Farid, [0054]).
Regarding claim 12: Stolfus-Abdel-Farid further teach: The vehicle supervision system as set forth in Claim 11, further comprising; an auxiliary driving controller that is mounted on a manual driving vehicle, that has the obstacle detector and that communicates with the vehicle supervision device through an in-vehicle communication interface (Stolfus: [0007]; [0021]; [0013]; [0071]; [0117]; [0069]; [0176]. Farid: [0079]; [0065]; [0088]; [0114]). The motivation for modification would have been to more effectively manage congestion associated with metropolitan areas, varying road traffic conditions, and dynamic distributions of connected vehicles, in order to maximize the performance of entire networks, and efficiently control large scale traffic systems to reduce congestion and improve traffic conditions (Farid, [0054]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3,6-12 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MADISON B EMMETT whose telephone number is (303)297-4231. The examiner can normally be reached Monday - Friday 9:00 - 5:00 ET.
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, Tommy Worden can be reached at (571)272-4876. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MADISON B EMMETT/Examiner, Art Unit 3658
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