$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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09-01-2025 has been entered.
Response to Amendment
Claims 4-5, 12-13, and 20 have been cancelled.
Claims 1, 9, and 17 have been amended.
No new claims have been introduced.
Claims 1-3, 6-11, and 14-19 are currently pending.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6-11, and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quirynen (US 20240331535 A1) in view of Yamane (US 20080319639 A1).
REGARDING CLAIM 1, Quirynen discloses, a processor (Quirynen: [0027] The traffic control system comprises least one processor and a memory having instructions stored thereon that, when executed by the at least one processor, cause the traffic control system to collect digital representation of states of each of the CAVs, each of the HDVs, and each of traffic signs regulating traffic on the roads) configured to: receive traffic data from vehicles that are currently within a geographic area (Quirynen: [0090] the edge infrastructure devices may be stationary, which enable them in providing reliable communication with vehicles as well as in collecting relatively high-quality environmental data; [0101] the mapping and navigation system 304 may receive information either from the hierarchical traffic control system 302 or from each of the CAVs 306; [0162] At 1105, the CTC 404 receives additional inputs, for example, feedback signals on state and planned routing information from the CAVs ... [0163] the feedback signals are obtained directly or indirectly from the sensing infrastructure module 406 (e.g., RSUs) or from connected vehicles, which can be either autonomous, semi-autonomous and/or human-driven vehicles); generate a plurality of macroscopic fundamental diagrams (MFDs) (Quirynen: [ABS] a macroscopic traffic flow model in a centralized traffic controller (CTC) subject to convex relaxation of the integer constraints, solving a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, and transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs; [0011] At each control time step, the global CTC controller computes high-level targets for the traffic flow in the multi-intersection network. Based on the global CTC targets, at each control time step, the local ITCs independently compute safe and optimal control trajectories for one or multiple dynamic traffic rules and for one or multiple CAVs within a neighborhood around each of the traffic intersections in the transportation network; [0016]; [0100] a macroscopic traffic flow model for the transportation network of multiple interconnected intersections; [0106]; [0149-0150]; [0216]) for a plurality of sub-areas (Quirynen: [0011] decision variables to vehicles in a local neighborhood around one particular traffic intersection in the transportation network ... At each control time step, the global CTC controller computes high-level targets for the traffic flow in the multi-intersection network. Based on the global CTC targets, at each control time step, the local ITCs independently compute safe and optimal control trajectories for one or multiple dynamic traffic rules and for one or multiple CAVs within a neighborhood around each of the traffic intersections in the transportation network; [0015] the global CTC controller solves a medium or long horizon optimization problem that computes high-level targets for the traffic flow in the multi-intersection network, based on a simplified macroscopic traffic model for the network of connected intersections; [0027] a traffic control system for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) moving across multiple intersections; [0100] a macroscopic traffic flow model for the transportation network of multiple interconnected intersections; [0216] local area that is controlled by an ITC around one or multiple traffic intersections within the transportation network ... a model for one or multiple TLCs 1213, one or multiple physical and/or safety constraints for CAVs 1214, traffic rules in multi-lane road segments) within the geographic area (Quirynen: [0056] a microscopic model for mixed traffic in a local area that is controlled by an ITC around one or multiple traffic intersections within the transportation network that is controlled by a hierarchical traffic control system; [0084] traffic participants in a local area around each of the RSUs 122, 124; [0107] a microscopic traffic flow model for a local area around one or multiple intersections within the transportation network) based on the received traffic data (Quirynen: [ABS] collecting digital representation of states of each of the CAVs, HDVs, and traffic signs, solving an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller); estimate a number of connected vehicles in each sub-area (Quirynen: [0158] the state information vector in the macroscopic traffic flow model includes both the vehicle density values 1020, the traffic flow values 1040 through one or multiple intersections, and the position of vehicles in each road segment of the transportation network. In some embodiments, each road segment is divided in one or multiple smaller sub-segments and current vehicle density values are estimated ([ABS] The method comprises collecting digital representation of states of each of the CAVs; [0028] collecting digital representation of states of each of the CAVs; [0054]; [0074] Details of the collection of the states of each of the CAVs); [0190] each road segment r is divided in one or multiple smaller sub-segments r.sub.1, r.sub.2, . . . , r.sub.n, and current vehicle density values are estimated) of the plurality of sub-areas (Quirynen: [0097] FIG. 4B shows an example of a traffic scenario in a local area of interconnected intersections and routing information for multiple connected and automated vehicles) based on messages transmitted among connected vehicles currently operating in the geographic area (Quirynen: [0090] the edge infrastructure devices may be stationary, which enable them in providing reliable communication with vehicles as well as in collecting relatively high-quality environmental data; [0101] the mapping and navigation system 304 may receive information either from the hierarchical traffic control system 302 or from each of the CAVs 306; [0162] At 1105, the CTC 404 receives additional inputs, for example, feedback signals on state and planned routing information from the CAVs ... [0163] the feedback signals are obtained directly or indirectly from the sensing infrastructure module 406 (e.g., RSUs) or from connected vehicles, which can be either autonomous, semi-autonomous and/or human-driven vehicles); identify a sub-area among the plurality of sub-areas that is congested (Quirynen: [0158] each road segment is divided in one or multiple smaller sub-segments and current vehicle density values are estimated and future vehicle density values are predicted for each of the sub-segments in order to improve the accuracy of the macroscopic traffic flow model; [0190] each road segment r is divided in one or multiple smaller sub-segments r.sub.1, r.sub.2, . . . , r.sub.n, and current vehicle density values are estimated and future vehicle density values are predicted for each of the sub-segments; [0307] decision making system 1500 can receive traffic information for one or multiple of the interconnected conflict zones; [0016] The state for the macroscopic traffic model may include the number of vehicles that are planning to drive straight, turn left or turn right in each of the road segments represented as nodes in the directed graph. The inputs for the macroscopic traffic model may include the number of vehicles that are transitioning from one road segment to a next, given the directed graph, given the maximum capacity of each traffic intersection; [0140] CTC traffic flow values 840 are not allowed to exceed the number of vehicles arriving at the traffic intersection; [0156-0157] the traffic flow value for crossing direction d.sub.1 of 2.3 at the first time step 1055 represents the number of vehicles that is predicted to flow in direction d.sub.1, i.e., from road segment s.sub.1 to road segment s.sub.11 or s.sub.12. The dynamic behavior of the traffic flow value 1041, and the vehicle density values ... the traffic flow value for crossing direction d.sub.2 of 1.7 at the first time step 1055 represents the number of vehicles that is predicted to flow in direction d.sub.2 1062; [0159]; [0195-0196]; [0268]; [0184] may include one or multiple of the following optimization variables: [0185] (a) z.sub.r,w(t)∈custom-character: vehicle density variables, i.e., number of vehicles (real-valued traffic density) for each road segment-maneuver (r, w) pair and a traffic flow maneuver at each time step t∈[0, N] in a prediction time window of the CTC 404, [0186] (b) f.sub.r,w.sup.in(t)∈custom-character: number of vehicles entering (real-valued in-flow) for each road segment-maneuver (r, w) pair at each time step t∈[0, N−1] in a prediction time window of the CTC 404, [0187] (c) f.sub.r,w.sup.out(t)∈custom-character: number of vehicles exiting (real-valued out-flow) for each road segment-maneuver (r, w) pair at each time step t∈[0, N−1] in a prediction time window of the CTC 404, [0188] (d) e.sub.r,s(t)∈custom-character: number of vehicles transitioning) based on the estimated number of connected vehicles (Quirynen: [0090] the edge infrastructure devices may be stationary, which enable them in providing reliable communication with vehicles as well as in collecting relatively high-quality environmental data; [0101] the mapping and navigation system 304 may receive information either from the hierarchical traffic control system 302 or from each of the CAVs 306; [0162] At 1105, the CTC 404 receives additional inputs, for example, feedback signals on state and planned routing information from the CAVs ... [0163] the feedback signals are obtained directly or indirectly from the sensing infrastructure module 406 (e.g., RSUs) or from connected vehicles, which can be either autonomous, semi-autonomous and/or human-driven vehicles).
Quirynen does not explicitly recite the terminology, identify a sub-area among the plurality of sub-areas which is congested based on the plurality of MFDs. However, Quirynen discloses persistent monitoring of macro, micro, and sub-region flow rates, vehicle density, travel speeds, vehicle in-flow and out-flow, capacity limits, and a series of equations for creating and monitoring traffic models. Which, the examiner respectfully submits, is/does/will identify any area/region that is congested.
Quirynen does not explicitly disclose, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area to attract vehicles to the different sub-area; and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area, wherein the routing instructions are generated based on the link cost.
However, in the same field of endeavor, Yamane discloses, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]) to attract vehicles to the different sub-area (Yamane: [0115] When receiving the predictive traffic information delivered from the predictive traffic information creating apparatus 1 (Step S83), the car navigation system 3 searches for a guide route from the current position to the destination (Step S84) based on the received predictive traffic information, and displays the searched guide route and predictive traffic congestion information on the display unit 33 together with a road map including the current position of the vehicle and the guide route (Step S85). [0116] ... if an incident such as an accident occurs, an accident spot 104 is displayed on the road map, and furthermore, traffic congestion situations of links around the accident spot 104 are displayed, for example, by differences in line thickness, line color, line type and the like. In the example of FIG. 13, the traffic congestion situations are represented by differences in line thickness, in which the link highlighted by a thick line 105 shows a heavily congested state, and the link indicated by a medium-thick line 106 shows a congested state. [0117] The predictive traffic information delivered from the predictive traffic information creating apparatus 1 has substantially the same constitution as of the statistical traffic information shown in FIG. 4, and has traffic congestion information as information of each link. Therefore, the predictive traffic information creating apparatus 1 can add the traffic congestion levels determined depending on the travel speed (vehicle speed) of the links as the traffic congestion information. This allows the car navigation system 3 to display traffic congestion levels easily); and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area (Yamane: [0007]when an incident such as a traffic accident occurs, it is not possible to estimate a guide route which avoids the incident occurring place only based on the statistical traffic information. Therefore, conventionally, information provided from a traffic information center is attached with information on traffic congestion, accidents, and traffic restrictions in addition to the link travel time, so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link; [0011] a microscopic implantation is performed on how vehicles move when receiving route guidance to avoid the accident place in association with the route guidance information for the vehicles), wherein the routing instructions are generated based on the link cost (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
REGARDING CLAIM 2, Quirynen, as modified, remains as applied above to claim 1, and further, Quirynen also discloses, based on the received traffic data (Quirynen: [0090] the edge infrastructure devices may be stationary, which enable them in providing reliable communication with vehicles as well as in collecting relatively high-quality environmental data; [0101] the mapping and navigation system 304 may receive information either from the hierarchical traffic control system 302 or from each of the CAVs 306; [0162] At 1105, the CTC 404 receives additional inputs, for example, feedback signals on state and planned routing information from the CAVs ... [0163] the feedback signals are obtained directly or indirectly from the sensing infrastructure module 406 (e.g., RSUs) or from connected vehicles, which can be either autonomous, semi-autonomous and/or human-driven vehicles), determine an average speed (Quirynen: [0106] the general behavior of vehicles at the level of the transportation network is modeled using traffic flow values, density values and mean speed values of traffic streams) and an average density (Quirynen: [0106] the general behavior of vehicles at the level of the transportation network is modeled using traffic flow values, density values and mean speed values of traffic streams) from a plurality of links of the plurality of sub-areas (Quirynen: [0106] to a macroscopic traffic flow model for the entire transportation network of multiple interconnected intersections) wherein the plurality of MFDs are generated based on the average speed and the average density (Quirynen: [0106] transportation network is modeled using traffic flow values, density values and mean speed values of traffic streams).
REGARDING CLAIM 3, Quirynen, as modified, remains as applied above to claim 1, and further, Quirynen also discloses, generate the routing instructions based on the link cost (Quirynen: see [0025-0026] for machine learning and neural network for determining travel time (examiner: increasing or decreasing cost), and, [0213] At 1220, the ITC 408 or 410 computes an optimal sequence of target control commands for each CAV in a local area around one or multiple traffic intersections within the transportation network … [0214] At 1225, the ITC 408 or 410 sends commands to a multi-layer guidance and control architecture for each CAV; [0120] including either lane following, lane changing or stopping).
Quirynen does not explicitly disclose, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area.
However, in the same field of endeavor, Yamane discloses, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area (Yamane: [0007]when an incident such as a traffic accident occurs, it is not possible to estimate a guide route which avoids the incident occurring place only based on the statistical traffic information. Therefore, conventionally, information provided from a traffic information center is attached with information on traffic congestion, accidents, and traffic restrictions in addition to the link travel time, so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
REGARDING CLAIM 6, Quirynen, as modified, remains as applied above to claim 1, and further, Quirynen also discloses, based on the plurality of MFDs (Quirynen: [ABS] a macroscopic traffic flow model … a microscopic traffic flow model), identify a second sub-area that is not congested (Quirynen: [0158] each road segment is divided in one or multiple smaller sub-segments and current vehicle density values are estimated and future vehicle density values are predicted for each of the sub-segments in order to improve the accuracy of the macroscopic traffic flow model (examiner: identifying density and flow of sub-segments); [0190] vehicle positions may be used to compute a vehicle density value z.sub.r,w, i.e., a number of vehicles in each road segment-maneuver pair (r, w). In some embodiments, each road segment r is divided in one or multiple smaller sub-segments r.sub.1, r.sub.2, . . . , r.sub.n, and current vehicle density values are estimated and future vehicle density values are predicted for each of the sub-segments z.sub.r.sub.1.sub.,w,z.sub.r.sub.2.sub.,w in order to improve the accuracy of the macroscopic traffic flow model in the CTC 404 of the hierarchical traffic control system 600 (examiner: identifying density and flow of sub-segments)) and transmit routing instructions to vehicles entering the second sub-area (Quirynen: [0305] each of the CAVs, the commands received from the hierarchical traffic control system can be used by a multi-layer guidance and control architecture to control the motion of the vehicle in order to improve overall safety, time and energy efficiency of the traffic flow in the transportation network; [0027] optimizes a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, subject to the integer constraints to produce values of control commands changing states of each of the CAVs associated with an intersection of the multiple intersections; [0094-0095] the traffic control system computes a coarse motion plan for each CAV in the transportation network along its route from the current position of the CAV to a desired destination of the CAV ... The coarse motion plan may include a sequence of entering and exit times and of average velocity values for each CAV at each intersection along the CAV's route from its current position to its desired destination; [0107]; [0128] such as the ITC 410 computes the future control commands 466 for the group of CAVs 416 and the group of TLCs 418 that are assigned to the ITC 410, given real-time information from the infrastructure sensing 406 and given the CTC traffic flow values 462 for each of the crossing directions of one or multiple intersections that are assigned to the ITC 410 within the transportation network) to control a flow rate of the vehicles entering the second sub-area (Quirynen: see [0151] for controlling vehicle flow in and out so that they match).
REGARDING CLAIM 7, Quirynen, as modified, remains as applied above to claim 6, and further, Quirynen also discloses, identify a number of additional vehicles needed within the second sub-area to reach a predetermined capacity (Quirynen: [0016] each road segment is represented as a node and each traffic direction is represented as an edge in the directed graph. The state for the macroscopic traffic model may include the number of vehicles that are planning to drive straight, turn left or turn right in each of the road segments represented as nodes in the directed graph. The inputs for the macroscopic traffic model may include the number of vehicles that are transitioning from one road segment to a next, given the directed graph, given the maximum capacity of each traffic intersection; [0140] the summation of CTC traffic flow values 840 is not allowed to exceed a maximum capacity limit at each time step and for each traffic intersection in the transportation network. Similarly, the CTC traffic flow values 840 are not allowed to exceed the number of vehicles arriving at the traffic intersection that are predicted to cross the traffic intersection in that particular crossing direction; [0151] the traffic intersection is upper bounded by a maximum capacity limit at each time step and for each traffic intersection in the transportation network; [0192-0196] one or multiple traffic intersection capacity limit constraints … correspond to one or multiple crossing directions of a traffic intersection j∈custom-character in the transportation network, and z.sub.j.sup.max denotes a maximum number of vehicles that is able to cross through the traffic intersection at a time; [0204] Traffic intersection capacity limits: Eq. (6), [0205] Out-flow and edge equality constraints: Eq. (7), [0206] In-flow and edge equality constraints: Eq. (8)) and based on the predetermined capacity (Quirynen: [0016] The inputs for the macroscopic traffic model may include the number of vehicles that are transitioning from one road segment to a next, given the directed graph, given the maximum capacity of each traffic intersection; [0083] capacity limit constraints for each of the multiple intersections or each of road segments in the transportation network; [0140] the summation of CTC traffic flow values 840 is not allowed to exceed a maximum capacity limit at each time step and for each traffic intersection in the transportation network. Similarly, the CTC traffic flow values 840 are not allowed to exceed the number of vehicles arriving at the traffic intersection that are predicted to cross the traffic intersection in that particular crossing direction. For example, given the future CTC traffic flow values 840 in FIG. 8C, a total of 2.3+1.0+0.7=4 vehicles are crossing the traffic intersection at the time step 841, after arriving at the traffic intersection from the North 815 direction; [0151] the traffic intersection is upper bounded by a maximum capacity limit at each time step and for each traffic intersection in the transportation network), generate the routing instructions to control a speed of the vehicles entering the second sub-area (Quirynen: [0083] The dynamic traffic rules may be displayed on digital displays on the road segments 105, 107, 109, 111, 113, 114, 116 to inform the vehicles 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146 of state of the dynamic traffic rules. For example, the traffic light display 150 may be configured to display a state of the traffic light for the multiple interconnected traffic intersection 101, a lane speed display configured to display a lane speed limit; [0184] number of vehicles transitioning (along edge in directed graph) from each road segment r to each connected road segment s in the transportation network).
Quirynen does not explicitly recite the terminology "identify a number of additional vehicles needed". However, Quirynen discloses equations 1-12, wherein equations 1-12, inter alia, are used to track vehicle flow in, vehicle flow out, and capacity limits. Which, the examiner respectfully submits, is counting vehicles to prevent exceeding the maximum limit. Thus, is aware of "a number of additional vehicles needed".
Quirynen does not explicitly recite the terminology "control a speed of the vehicles entering the second sub-area". However, Quirynen discloses, inter alia, managing speeds of vehicles transitioning through segments and intersections in an entire macro network. Which, the examiner respectfully submits, is "control a speed of the vehicles entering the second sub-area".
REGARDING CLAIM 8, Quirynen, as modified, remains as applied above to claim 1, and further, Quirynen also discloses, transmit re-routing instructions to a navigation system of a vehicle that is operating outside of the sub-area (Quirynen: [0094-0095] The coarse motion plan may include a sequence of entering and exit times and of average velocity values for each CAV at each intersection along the CAV's route from its current position to its desired destination (examiner: controlling vehicles "outside" (entering vehicles (see instant specification [0064] transports outside one subnetwork can imply the amount of vehicles entering the subnetwork))); [0104] the number of vehicles may vary significantly as traffic participants enter and exit the transportation network in which the hierarchical traffic control system 302 operates; [0110] In an embodiment, the CTC 404 predicts, in the macroscopic traffic model, a number of external vehicles entering the transportation network from each of in-flow directions at each time step in a prediction time window; [0128]; [0304-0305] the hierarchical traffic control system is designed to transmit an optimal sequence of entering and exiting times and average velocities 1522 and/or a velocity profile, one or multiple lane change commands and a sequence of planned stops 1524 to each of the connected and automated vehicles (CAVs) along its future planned route ... the decision making system 1500 includes a transmitter interface 1510, using a transmitter 1512 and/or one or multiple of the devices 1508, configured to transmit an optimal sequence of entering and exiting times and average velocities 1522 and/or a velocity profile, one or multiple lane change commands and a sequence of planned stops 1524, determined by one or multiple processors 1514, to each of the connected and automated vehicles (CAVs) along its future planned route in the transportation network. In each of the CAVs, the commands received from the hierarchical traffic control system can be used by a multi-layer guidance and control architecture to control the motion of the vehicle in order to improve overall safety, time and energy efficiency of the traffic flow in the transportation network (examiner: communicating with "outside" (entering vehicles) and transmitting entering commands, exiting commands, and how the vehicle will behave between entering and exiting)).
In this case, “outside” is interpreted as including “entering”. See instant specification: “[0064] transports outside one subnetwork can imply the amount of vehicles entering the subnetwork”.
REGARDING CLAIM 9, Quirynen discloses, receiving traffic data from vehicles that are currently within a geographic area (Quirynen: [0090]; [0101]; [0162-0163]); generating a plurality of macroscopic fundamental diagrams (MFDs) (Quirynen: [ABS]; [0011]; [0016]; [0100]; [0106]; [0149-0150]; [0216]) for a plurality of sub-areas (Quirynen: [0011]; [0015]; [0027]; [0100]; [0216]) within the geographic area (Quirynen: [0056]; [0084]; [0107]) based on the received traffic data (Quirynen: [ABS]); estimating a number of connected vehicles in each sub-area (Quirynen: [0158] ([ABS] The method comprises collecting digital representation of states of each of the CAVs; [0028] collecting digital representation of states of each of the CAVs; [0054]; [0074] Details of the collection of the states of each of the CAVs); [0190]) of the plurality of sub-areas (Quirynen: [0097]) based on messages transmitted among connected vehicles currently operating in the geographic area (Quirynen: [0090]; [0101]; [0162-0163]) identifying a sub-area among the plurality of sub-areas that is congested (Quirynen: [0158]; [0190]; [0307]; [0016]; [0140]; [0156-0157]; [0159]; [0195-0196]; [0268]; [0184-0188]) based on the estimated number of connected vehicles (Quirynen: [0090]; [0101]; [0162-0163]).
Quirynen does not explicitly recite the terminology, identify a sub-area among the plurality of sub-areas which is congested based on the plurality of MFDs. However, Quirynen discloses persistent monitoring of macro, micro, and sub-region flow rates, vehicle density, travel speeds, vehicle in-flow and out-flow, capacity limits, and a series of equations for creating and monitoring traffic models. Which, the examiner respectfully submits, is/does/will identify any area/region that is congested.
Quirynen does not explicitly disclose, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area to attract vehicles to the different sub-area; and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area, wherein the routing instructions are generated based on the link cost.
However, in the same field of endeavor, Yamane discloses, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]) to attract vehicles to the different sub-area (Yamane: [0115] When receiving the predictive traffic information delivered from the predictive traffic information creating apparatus 1 (Step S83), the car navigation system 3 searches for a guide route from the current position to the destination (Step S84) based on the received predictive traffic information, and displays the searched guide route and predictive traffic congestion information on the display unit 33 together with a road map including the current position of the vehicle and the guide route (Step S85). [0116] ... if an incident such as an accident occurs, an accident spot 104 is displayed on the road map, and furthermore, traffic congestion situations of links around the accident spot 104 are displayed, for example, by differences in line thickness, line color, line type and the like. In the example of FIG. 13, the traffic congestion situations are represented by differences in line thickness, in which the link highlighted by a thick line 105 shows a heavily congested state, and the link indicated by a medium-thick line 106 shows a congested state. [0117] The predictive traffic information delivered from the predictive traffic information creating apparatus 1 has substantially the same constitution as of the statistical traffic information shown in FIG. 4, and has traffic congestion information as information of each link. Therefore, the predictive traffic information creating apparatus 1 can add the traffic congestion levels determined depending on the travel speed (vehicle speed) of the links as the traffic congestion information. This allows the car navigation system 3 to display traffic congestion levels easily); and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area (Yamane: [0007]when an incident such as a traffic accident occurs, it is not possible to estimate a guide route which avoids the incident occurring place only based on the statistical traffic information. Therefore, conventionally, information provided from a traffic information center is attached with information on traffic congestion, accidents, and traffic restrictions in addition to the link travel time, so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link; [0011] a microscopic implantation is performed on how vehicles move when receiving route guidance to avoid the accident place in association with the route guidance information for the vehicles), wherein the routing instructions are generated based on the link cost (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
REGARDING CLAIM 10, Quirynen, as modified, remains as applied above to claim 9, and further, Quirynen also discloses, based on the received traffic data (Quirynen: [0090]; [0101]; [0162-0163]), determining an average speed (Quirynen: [0106]) and an average density (Quirynen: [0106]) from a plurality of links of the plurality of sub-areas (Quirynen: [0106]) wherein the plurality of MFDs are generated based on the average speed and the average density (Quirynen: [0106]).
REGARDING CLAIM 11, Quirynen, as modified, remains as applied above to claim 9, and further, Quirynen also discloses, generate the routing instructions based on the link cost (Quirynen: see [0025-0026] for machine learning and neural network for determining travel time (examiner: increasing or decreasing cost), and, [0213] At 1220, the ITC 408 or 410 computes an optimal sequence of target control commands for each CAV in a local area around one or multiple traffic intersections within the transportation network … [0214] At 1225, the ITC 408 or 410 sends commands to a multi-layer guidance and control architecture for each CAV; [0120] including either lane following, lane changing or stopping).
Quirynen does not explicitly disclose, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area.
However, in the same field of endeavor, Yamane discloses, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area (Yamane: [0007]; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
REGARDING CLAIM 14, Quirynen, as modified, remains as applied above to claim 9, and further, Quirynen also discloses, based on the plurality of MFDs (Quirynen: [ABS]), identify a second sub-area that is not congested (Quirynen: [0158] (examiner: identifying density and flow of sub-segments); [0190] (examiner: identifying density and flow of sub-segments)) and transmit routing instructions to vehicles entering the second sub-area (Quirynen: [0305]; [0027]; [0094-0095]; [0107]; [0128]) to control a flow rate of the vehicles entering the second sub-area (Quirynen: see [0151] for controlling vehicle flow in and out so that they match).
REGARDING CLAIM 15, Quirynen, as modified, remains as applied above to claim 14, and further, Quirynen also discloses, identify a number of additional vehicles needed within the second sub-area to reach a predetermined capacity (Quirynen: [0016]; [0140]; [0151]; [0192-0196]; [0204-0206]) and based on the predetermined capacity (Quirynen: [0016]; [0083]; [0140]; [0151]), generate the routing instructions to control a speed of the vehicles entering the second sub-area (Quirynen: [0083]; [0184]).
Quirynen does not explicitly recite the terminology "identify a number of additional vehicles needed". However, Quirynen discloses equations 1-12, wherein equations 1-12, inter alia, are used to track vehicle flow in, vehicle flow out, and capacity limits. Which, the examiner respectfully submits, is counting vehicles to prevent exceeding the maximum limit. Thus, is aware of "a number of additional vehicles needed".
Quirynen does not explicitly recite the terminology "control a speed of the vehicles entering the second sub-area". However, Quirynen discloses, inter alia, managing speeds of vehicles transitioning through segments and intersections in an entire macro network. Which, the examiner respectfully submits, is "control a speed of the vehicles entering the second sub-area".
REGARDING CLAIM 16, Quirynen, as modified, remains as applied above to claim 9, and further, Quirynen also discloses, transmit re-routing instructions to a navigation system of a vehicle that is operating outside of the sub-area (Quirynen: [0094-0095]; [0104]; [0110]; [0128]; [0304-0305]).
In this case, “outside” is interpreted as including “entering”. See instant specification: “[0064] transports outside one subnetwork can imply the amount of vehicles entering the subnetwork”.
REGARDING CLAIM 17, Quirynen discloses, receiving traffic data from vehicles that are currently within a geographic area (Quirynen: [0090]; [0101]; [0162-0163]); generating a plurality of macroscopic fundamental diagrams (MFDs) (Quirynen: [ABS]; [0011]; [0016]; [0100]; [0106]; [0149-0150]; [0216]) for a plurality of sub-areas (Quirynen: [0011]; [0015]; [0027]; [0100]; [0216]) within the geographic area (Quirynen: [0056]; [0084]; [0107]) based on the received traffic data (Quirynen: [ABS]); estimating a number of connected vehicles in each sub-area (Quirynen: [0158] ([ABS] The method comprises collecting digital representation of states of each of the CAVs; [0028] collecting digital representation of states of each of the CAVs; [0054]; [0074] Details of the collection of the states of each of the CAVs); [0190]) of the plurality of sub-areas (Quirynen: [0097]) based on messages transmitted among connected vehicles currently operating in the geographic area (Quirynen: [0090]; [0101]; [0162-0163]) identifying a sub-area among the plurality of sub-areas that is congested (Quirynen: [0158]; [0190]; [0307]; [0016]; [0140]; [0156-0157]; [0159]; [0195-0196]; [0268]; [0184-0188]) based on the estimated number of connected vehicles (Quirynen: [0090]; [0101]; [0162-0163]).
Quirynen does not explicitly recite the terminology, identify a sub-area among the plurality of sub-areas which is congested based on the plurality of MFDs. However, Quirynen discloses persistent monitoring of macro, micro, and sub-region flow rates, vehicle density, travel speeds, vehicle in-flow and out-flow, capacity limits, and a series of equations for creating and monitoring traffic models. Which, the examiner respectfully submits, is/does/will identify any area/region that is congested.
Quirynen does not explicitly disclose, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area to attract vehicles to the different sub-area; and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area, wherein the routing instructions are generated based on the link cost.
However, in the same field of endeavor, Yamane discloses, decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]) to attract vehicles to the different sub-area (Yamane: [0115] When receiving the predictive traffic information delivered from the predictive traffic information creating apparatus 1 (Step S83), the car navigation system 3 searches for a guide route from the current position to the destination (Step S84) based on the received predictive traffic information, and displays the searched guide route and predictive traffic congestion information on the display unit 33 together with a road map including the current position of the vehicle and the guide route (Step S85). [0116] ... if an incident such as an accident occurs, an accident spot 104 is displayed on the road map, and furthermore, traffic congestion situations of links around the accident spot 104 are displayed, for example, by differences in line thickness, line color, line type and the like. In the example of FIG. 13, the traffic congestion situations are represented by differences in line thickness, in which the link highlighted by a thick line 105 shows a heavily congested state, and the link indicated by a medium-thick line 106 shows a congested state. [0117] The predictive traffic information delivered from the predictive traffic information creating apparatus 1 has substantially the same constitution as of the statistical traffic information shown in FIG. 4, and has traffic congestion information as information of each link. Therefore, the predictive traffic information creating apparatus 1 can add the traffic congestion levels determined depending on the travel speed (vehicle speed) of the links as the traffic congestion information. This allows the car navigation system 3 to display traffic congestion levels easily); and transmit routing instructions to the vehicles within the geographic area to divert them away from the sub-area (Yamane: [0007]when an incident such as a traffic accident occurs, it is not possible to estimate a guide route which avoids the incident occurring place only based on the statistical traffic information. Therefore, conventionally, information provided from a traffic information center is attached with information on traffic congestion, accidents, and traffic restrictions in addition to the link travel time, so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link; [0011] a microscopic implantation is performed on how vehicles move when receiving route guidance to avoid the accident place in association with the route guidance information for the vehicles), wherein the routing instructions are generated based on the link cost (Yamane: [0005] a route which minimizes the total link travel time to a destination is displayed on a map; [0007] so that the car navigation system sets greater the value of a link travel time of a link involved in traffic congestion, accidents, and traffic restrictions, thereby to estimate a guide route to avoid this link ... [0008] to estimate a guide route, link travel time of not only the link where an accident has occurred but also links around the concerned link should be set greater in value; [0011-0013]; [0115-0117]; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
REGARDING CLAIM 18, Quirynen, as modified, remains as applied above to claim 17, and further, Quirynen also discloses, based on the received traffic data (Quirynen: [0090]; [0101]; [0162-0163]), determining an average speed (Quirynen: [0106]) and an average density (Quirynen: [0106]) from a plurality of links of the plurality of sub-areas (Quirynen: [0106]) wherein the plurality of MFDs are generated based on the average speed and the average density (Quirynen: [0106]).
REGARDING CLAIM 19, Quirynen, as modified, remains as applied above to claim 17, and further, Quirynen also discloses, generate the routing instructions based on the link cost (Quirynen: see [0025-0026]; [0213-0214]; [0120]).
Quirynen does not explicitly disclose, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area.
However, in the same field of endeavor, Yamane discloses, increase a link cost associated with the sub-area based on an amount of congestion in the sub-area to detour vehicles away from the sub-area (Yamane: [0007]; [FIG. 13]), for the benefit of avoiding increased travel times due to incident or accident.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify a hierarchical optimization-based coordinated system disclosed by Quirynen to include vehicle diversion taught by Yamane. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to avoid increased travel times due to incident or accident.
Response to Arguments
Applicant's arguments filed 09-01-2025 have been fully considered but they are not persuasive. To the examiner’s best understanding, the applicant has contended that the prior art of Yamane (US 20080319639 A1) fails to disclose:
decrease a link cost associated with a different sub-area based on an amount of congestion in the different sub-area to attract vehicles to the different sub-area;
[0115] When receiving the predictive traffic information delivered from the predictive traffic information creating apparatus 1 (Step S83), the car navigation system 3 searches for a guide route from the current position to the destination (Step S84) based on the received predictive traffic information, and displays the searched guide route and predictive traffic congestion information on the display unit 33 together with a road map including the current position of the vehicle and the guide route (Step S85). [0116] ... if an incident such as an accident occurs, an accident spot 104 is displayed on the road map, and furthermore, traffic congestion situations of links around the accident spot 104 are displayed, for example, by differences in line thickness, line color, line type and the like. In the example of FIG. 13, the traffic congestion situations are represented by differences in line thickness, in which the link highlighted by a thick line 105 shows a heavily congested state, and the link indicated by a medium-thick line 106 shows a congested state. [0117] The predictive traffic information delivered from the predictive traffic information creating apparatus 1 has substantially the same constitution as of the statistical traffic information shown in FIG. 4, and has traffic congestion information as information of each link. Therefore, the predictive traffic information creating apparatus 1 can add the traffic congestion levels determined depending on the travel speed (vehicle speed) of the links as the traffic congestion information. This allows the car navigation system 3 to display traffic congestion levels easily
The examiner respectfully submits, Yamane (US 20080319639 A1) discloses decreasing a link cost in a different area. Yamane (US 20080319639 A1) does not explicitly recite the terminology decreasing a link cost. However, in considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. In this case, Yamane (US 20080319639 A1) discloses changing link line color, line thickness, or line design/pattern to indicate level or less congestion. The examiner respectfully submits, one of ordinary skill, or an experienced driver, understands an indication of less congestion is indicating a links decreased or increased cost (e.g., time, fuel, wear and tear). Thus, Yamane (US 20080319639 A1) discloses an alternative link with a decreased cost. Further, while every limitation is given considerable patentable weight, “to attract vehicles to the different sub-area” appears to be a desired outcome, not an invention.
To the examiner’s best understanding, the applicant has also contended, Yamane (US 20080319639 A1) fails to use real-time data for link cost. The examiner respectfully submits, Yamane (US 20080319639 A1) discloses “[0043] The live traffic information 21 is traffic information created based on information acquired in real time”. Because Yamane (US 20080319639 A1) discloses that which is claimed, the examiner respectfully maintains the rejection of the independent claims under 35 USC §103, obviousness.
Conclusion
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/A.S./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663