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
Claims 160-177 are pending in this application.Claim 160 is presented as a currently amended claim.
Claims 161-177 are presented as previously presented claims.
No claims are newly presented.
No claims are newly cancelled.
Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 9, 2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 160-177 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 160 recites “cloud-based platform” and "cloud" and the “cloud” throughout the claim, in particular at (i) limitation (e); (ii) line 19 on page 3; (iii) line 11 on page 4; (iv) line 16 on page 4; (v) line 21 on page 4. At a minimum there is insufficient antecedent basis for “the cloud” as used in line 19 on page 3 as it references “the cloud” without antecedent basis rendering it unclear if it is attempting to reference “cloud-based platform” of limitation (e).
While the recited limitations are provided the broadest reasonable interpretation in light of the specification, the scope of the claim is rendered indefinite. It is unclear if the “cloud” is the same as the same as the “cloud-based platform” or if there are two cloud systems each with specific responsibilities. For the purposes of the prior art rejection below this term has been interpreted as there only being one cloud based platform.
Claims 161-177 are rejected at least based on dependency on a previously rejected claim. Correction or clarification is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 160-163, 168-171, and 173-175 are rejected under 35 U.S.C. 103 as being unpatentable over Smith ("Transit Signal Priority (TSP): A Planning and Implementation Handbook") in view of Aoude (US 20190287403 A1) in view of Tachet R. (“Revisiting Street Intersections Using Slot-Based Systems”) in view of "Internet Of Vehicles: From Intelligent Grid To Autonomous Cars And Vehicular Clouds” by M. Gerla, et al. (the combination of which is referred to as “combination Smith” hereinafter). As regards the individual claims:
Regarding claim 160, Smith substantially teaches a transit management system comprising a network to:
A) . . . detect traffic conditions, conduct data fusion, (Smith: pg. 055; [with] real-time information, buses . . . adapt to shifts in traffic and will change signal timing to meet demand) (see Smith: pg. 084; Fig. 002, etc.) . . . b) a traffic control unit (TCU) and traffic control center (TCC) network that process information and traffic operations instructions, conduct data fusion, predict behavior for individual CATVs at a microscopic level, and provide time sensitive control instructions to the CATVs; c) vehicle onboard units (OBUs) residing in the CATVs; d) traffic operations centers (TOC); and e) a cloud-based platform configured to provide information and computing services; (an overarching traffic management system that optimizes traffic signals to achieve maximum speed and minimum delay for transit buses through controlling individual traffic signals and prioritizing specific traffic over other specific and general-purpose traffic through the use real-time data fusion at a cloud based traffic control center processing supplied with vehicle-level data and broader system-wide data; see Smith: pg. 004; pg. 009; pg. 037; pg. 055; Fig. 005, etc.) . . .the plurality of RSUs are configured to comprise a data fusion module to fuse data from data sources comprising vehicle sensors, roadside sensors, and cloud; conduct transportation behavior prediction (an integrated traffic management system using vehicle detectors, priority-request generation units, priority request servers, local masters, and controllers that operate as RSUs to predict and avoid traffic delays by fusing real-time data obtained by vehicle and intersection sensors both locally and at a cloud based traffic control center; see Smith: pg. 004; pg. 009; pg. 037; pg. 055; pg. 084, Fig. 002, Figs. 005-006) etc.) . . . wherein said TCC is a computational module that is configured to optimize traffic operations, provide data processing and archiving functionality, and provide human operations interfaces; (a traffic management system with a human-staffed operations center that optimizes traffic signals; see Smith: pg. 004; pg. 009; pg. 037; pg. 055; pg. 062, pg. 084, pg. 119, Fig. 002, Figs. 005-006, etc.) . . .and the TCC and the TCU are configured to comprise a data fusion module to fuse data from data sources comprising vehicle sensors, roadside sensors, and cloud; conduct transportation behavior prediction and management at a microscopic level; provide planning and decision making at a microscopic level,(a traffic management system that optimizes traffic signals down to the individual bus stop and individual lane level by fusing real-time data obtained by vehicle and intersection sensors at a cloud based traffic control center; see Smith: pg. 004; pg. 009; pg. 037; pg. 055; pg. 062, pg. 064, pg. 084, pg. 119, Fig. 002, Fig. 005) etc.) . . . wherein said cloud-based platform is configured to perform traffic state estimation and prediction algorithms to estimate the traffic state based on a weighted data fusion method, wherein weights are determined by the quality of data provided by at least one of RSU, TCC/TCU, and TOC sensors with partial or complete detection; and (a traffic management system that optimizes a traffic grid by a simulation process wherein early real-world data is used to calibrate the expected results and refine a simulation machine and produce a calibrated model that is matched against later data, resulting in a weighted data-approach wherein less reliable data is de-weighted through model refinement and wherein a lack of data is addressed through the use of archived data; see Smith: pg. pg. 009; pg. 37, pg. 055; pg. 084, pg. 119, Fig. 002, Fig. 005, etc.)
However, Smith does not explicitly teach:
a) a roadside unit (RSU) network including a plurality of RSUs that receive data from the CATVs, . . . predict behavior for individual CATVs at a microscopic level, and send control instructions to the CATVs; . . . wherein said each of the plurality of RSUs comprises a communications module, a sensing module, and a data processing module; . . . wherein said transit management system is configured to provide safety and efficiency measures for CATV operations and control under adverse weather conditions, which comprise a location service provided by a local RSU and site- specific road weather and pavement condition information service provided by RSUs; but, Aoude does teach:
a) a roadside unit (RSU) network including a plurality of RSUs that receive data from the CATVs, (see Aoude: ¶ 064; RSE collects information from the sensors, other RSEs, OBEs) . . . predict behavior for individual CATVs at a microscopic level, and send control instructions to the CATVs; (see Aoude: ¶ 006; motion data received from the sensor about ground transportation entities at or near the intersection is [used] to predict imminent behaviors of the ground transportation entities.) . . . wherein said each of the plurality of RSUs comprises a communications module, a sensing module, and a data processing module; (see Aoude: ¶ 064; the RSE collects information from the sensors, other RSEs, OBEs, OPEs, local or central servers,) . . . wherein said transit management system is configured to provide safety and efficiency measures for CATV operations and control under adverse weather conditions, which comprise a location service provided by a local RSU and site- specific road weather and pavement condition information service provided by RSUs; (traffic system can receive specific weather information from sensors and the cloud and provide feedback to signs or control ground vehicles based on the data; (see Aoude: ¶ 007-008, Aoude: ¶ 150)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Smith with the teachings of Aoude because the use of a known technique to improve similar systems in the same way is obvious (KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 417, 82 USPQ2d at 1396.) In the instant case, both Smith and Aoude’s base systems are similar vehicular control and intersection management systems designed to improve flow and safety; however, Aoude’s system has been improved by the use of RSE capable of aggregating more sources of sensor data than Smith. Before the time of filing of the claimed invention, one of ordinary skill in the art could have applied Aoude’s known improvement to Smith using known methods and recognized that the results of the combination were predictable because each element merely performs the same function as it does separately. Further, such a combination would predictably create an expectation of advantage because it would provide a greater amount of data to use in algorithms designed to improve safety and performance by giving a more complete picture of system interactions.
Neither Smith nor Aoude explicitly teach:
and management at a microscopic level; provide planning and decision making at a microscopic level; and generate and provide vehicle control instructions for vehicle longitudinal and lateral position, speed, and steering and control to achieve automated driving for the CATVs; . . . said TCU is a computational module that is configured to provide automated real-time vehicle control and data processing;. . . and generate and provide vehicle specific control instructions including vehicle longitudinal and lateral position, speed, and steering and control to achieve automated driving for the CATVs; . . . wherein said cloud-based platform is configured to a perform a prediction function at a microscopic level, a mesoscopic level, and a macroscopic level; wherein said cloud-based platform is configured to perform planning and decision-making at a microscopic level, a mesoscopic level, and a macroscopic level; wherein said cloud-based platform is configured to perform vehicle control functions; . . . wherein said OBUs are configured to perform vehicle control functions by controlling vehicle longitudinal and lateral position, speed, and steering and control according to said vehicle control instructions however, Tachet does teach:
and management at a microscopic level; provide planning and decision making at a microscopic level; and generate and provide vehicle control instructions for vehicle longitudinal and lateral position, speed, and steering and control to achieve automated driving for the CATVs; (a system that communicates with autonomous vehicles to command uniform spacing and speeds and control traffic signals to allow fine grained travel and merging to produce coordinated flows; see Tachet: pg. 7; Tachet: pg. 2) . . . said TCU is a computational module that is configured to provide automated real-time vehicle control and data processing;(a system that communicates with autonomous vehicles to command uniform spacing and speeds and control traffic signals to allow fine grained travel and merging to produce coordinated flows; see Tachet: pg. 7; Tachet: pg. 2) . . .and generate and provide vehicle specific control instructions including vehicle longitudinal and lateral position, speed, and steering and control to achieve automated driving for the CATVs; (a system that communicates with autonomous vehicles to command uniform spacing and speeds and control traffic signals to allow fine grained travel and merging to produce coordinated flows; see Tachet: pg. 7; Tachet: pg. 2) . . . wherein said cloud-based platform is configured to a perform a prediction function at a microscopic level, (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control)a mesoscopic level, and a macroscopic level; wherein said cloud-based platform is configured to perform planning and decision-making at a microscopic level, a mesoscopic level, and a macroscopic level; wherein said cloud-based platform is configured to perform vehicle control functions; (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control). . .wherein said OBUs are configured to perform vehicle control functions by controlling vehicle longitudinal and lateral position, speed, and steering and control according to said vehicle control instructions.(a system that communicates with autonomous vehicles to command uniform spacing and speeds and control traffic signals to allow fine grained travel and merging to produce coordinated flows; see Tachet: pg. 7; Tachet: pg. 2)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Smith with the teachings of Tachet because combining prior art elements according to known methods to yield predictable results is obvious if the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. (KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416; MPEP § 2143(I)). In the instant case, Smith teaches the element of network traffic control system to monitor sensors and share data through the cloud with mass transit vehicles; and Tachet teaches a method of direct vehicle control thorough a centralized system. The combination of these elements results in the predictable benefit of a system that controls mass transit vehicles through a control thorough a centralized system. The mechanism of combination is well-known to those in the art as both teach wireless sharing of sensor data. Consequently, the combination is obvious to a person of ordinary skill in the art.
While previously applied art does not teach:
wherein said cloud-based platform is configured to fuse multi-source data collected from vehicle sensors, roadside sensors, and the cloud; and predict transportation network conditions for the CATV at a microscopic level, comprising longitudinal movements and lateral movements; wherein said cloud-based platform is configured to communicate, exchange, and share data in real-time with vehicles, the TCC/TCU network, such as advanced sensor data processing and intelligent transport the cloud, and other entities; wherein said OBUs are configured to fuse multi-source data collected from vehicle sensors, roadside sensors, and the cloud; and predict transportation network conditions for the CATV at a microscopic level, comprising longitudinal movements and lateral movements; wherein said OBUs operate the CATV using the vehicle-specific information to provide a sensing function at a microscopic level, a prediction function at a microscopic level, a decision-making function at a microscopic level, and a control function at a microscopic level;
Gerla does teach:
wherein said cloud-based platform is configured to fuse multi-source data (Gerla: pg. 242, col. 2; vehicular cloud will provide the ideal system environment for the coordinated deployment of the sensor aggregation, fusion and database sharing applications required by the future autonomous vehicles) collected from vehicle sensors, (Gerla: pg. 242, col. 2; uses a myriad of on board sensors, ranging from RADAR, GPS, video cameras to CAN Bus sensors that monitor vehicle’s internal operation status) roadside sensors, and the cloud; (Gerla: pg. 242, col. 2; the road is instrumented with smart dust components [3], RFID tags [4], . . . cloud provides a communication and computing environment on top of the grid so as to inter-network all Things) and predict transportation network conditions for the CATV at a microscopic level, comprising longitudinal movements and lateral movements; (Gerla: pg. 245, col. 1; can be packed in compact platoons and convoys. They can also make efficient use of preferred (or pay-per-service) lanes, by maintaining a “train on wheel” configuration on such lanes, and by allowing efficient in-and-out lane switches) wherein said cloud-based platform is configured to communicate, exchange, and share data in real-time with vehicles, (Gerla: Fig. 2; Cloud resources - data storage, sensors, and computing are shared to create a common virtual platform.)the TCC/TCU network, (Gerla: Pg. 245, Col. 1; AUVs depend on the infrastructure (e.g., WIFI access points, DSRC RSUs, and LTE) for several non-safety functions)such as advanced sensor data processing (Gerla: Fig. 2) and intelligent transport the cloud, and other entities; (Gerla: Pg. 245, Col. 1) wherein said OBUs are configured to fuse multi-source data collected from vehicle sensors, roadside sensors, and the cloud; and predict transportation network conditions for the CATV at a microscopic level, comprising longitudinal movements and lateral movements; wherein said OBUs operate the CATV using the vehicle-specific information to provide a sensing function at a microscopic level, a prediction function at a microscopic level, a decision-making function at a microscopic level, and a control function at a microscopic level; (Gerla: pg. 242, col. 1; autonomous vehicle must be capable of sensing its surroundings and of self-driving without human inputs. To do that, it uses a myriad of on board sensors, ranging from RADAR, GPS, video cameras to CAN Bus sensors that monitor vehicle’s internal operation status. An advanced autonomous driving system processes all the sensory data, constructs the traffic map, identifies appropriate paths and avoids obstacles on such paths)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gerla with the teachings of Smith because the combination would facilitate “continuous sharing of position data [which] is essential to guarantee stability of the autonomous fleet [and] crowdsourcing of road conditions (poor pavement conditions, obstacles, accidents, etc.) using the collection of available sensors will allow smooth driving even in perilous conditions“ (Gerla: pg. 242, col. 2).
Regarding claim 161, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
a) at least one of dedicated CATV lanes, non-dedicated lanes, (Smith: pg. 021; Physical priority in traffic flow (separated bus lane))
and dynamic CATV-only lanes (Smith: pg. 008; An example would be an exclusive left turn lane for transit vehicles. The left turn phase is only displayed when a transit vehicle is detected in the lane [dynamic configured])
b) at least one of physical barriers and logical barriers separating lanes used for CATVs from traditional lanes used by human-driven vehicles (Smith: pg. 180; Three-block section is separated by fences or barriers from the adjacent traffic lane) (Smith: pg. 049; the LRT applications were almost exclusively deployed on dedicated right-of way (ROW) separate from vehicular traffic flow.)
Regarding claim 162, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
at least one of dedicated CATV bus stops, non-dedicated CATV bus stops, curbside bus stops, and bus bay stops (Smith: pg. 029; placement of a bus stop on the [curbside] far side of an intersection provides many advantages in the design of a TSP system)
Regarding claim 163, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
at least one of manage the CATVs at intersections and diverging/merging locations (Smith: pg. 008; queue jump phase shows a signal (such as a white bar) that is intended for the transit vehicle only and allows the transit vehicle to move ahead of the rest of the traffic that is waiting for a green at the intersection. An application might be the location of a near-side bus bay; the queue jump phase allows the bus to re-enter [merge] the mainstream lane before the general traffic is given a green phase to move forward.)
using priority based on the total delay and average vehicle speed (Smith: pg. 007; signal system uses cycle lengths based on the travel speed of the buses on the Denver Transit Mall so that the buses can stay in sync with the signals and so that the cross streets can be coordinated across the mall.)
Regarding claim 168, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
perform sensing methods for at least one of dedicated lanes, non-dedicated lanes, transit stations, intersections, entrances to dedicated lanes, and in CATVs, wherein a) said methods for dedicated lanes comprise monitoring CATVs by RSUs and OBUs to collect dedicated lane data; processing, fusing, and sending said dedicated lane data to the TCC/TCU network; and sharing said dedicated lane data through the cloud platform; b) said methods for non-dedicated lanes comprise monitoring all vehicles by RSUs and monitoring the environment of CATVs by OBUs to collect non- dedicated lane data; processing, fusing, and sending said non-dedicated lane data to the TCC/TCU network; and sharing said non-dedicated lane data through the cloud platform (Smith: pg. 008; [some phases are] only displayed when a transit vehicle is detected in the [shared] lane [such as the] queue jump phase shows a signal (such as a white bar) that is intended for the transit vehicle only and allows the transit vehicle to move ahead of the rest of the traffic that is waiting for a green at the intersection. An application might be the location of a near-side bus bay; the queue jump phase allows the bus to re-enter [merge] the mainstream lane before the general traffic is given a green phase to move forward.) (Smith: Fig. 002; [showing sharing of data with cloud platform]) (Smith: pg. 049; LRT applications were almost exclusively deployed on dedicated right-of way (ROW) separate from vehicular traffic flow.)
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d) said methods for intersections comprise monitoring pedestrian and CATVs by RSUs installed at intersections (Smith: pg. 077; Traffic signals without pedestrian actuation or push buttons must rely on a recall in the traffic controller [here acting as an RSU] that ensures a pedestrian WALK indication will be displayed during each cycle. Pedestrian Recall calls up the time necessary for pedestrian timing for specific phases that do not have pedestrian actuation. For example, when pedestrian recall is on the nontransit vehicle phase, the minimum pedestrian cycle timing (Walk and Flash Don’t Walk) will occur, potentially delaying the transit vehicle.)
e) said methods for entrances to dedicated lanes comprise detecting vehicles by entrance sensors, recording vehicle identifying information, and notifying other vehicles of vehicle entrance; and (Smith: pg. 064; signal control software [acting as an RSU] allows for emergency vehicle and railroad pre-emption, as well as transit signal priority applications [and enables s]ystem wide control strategies can be achieved by having each individual intersection controller/software send information back to the central end (traffic control center) and then redistribute it out to the individual intersection controllers.)
f) said methods for CATVs comprise monitoring the status of vehicles and passengers by OBUs and transmitting said status to RSUs (Smith: pg. 147; On the 98 B-Line, priority is granted to buses that are two or more minutes late. The TSP Master Unit has the ability to distinguish four levels of ‘lateness’, and in case of conflict, forwards the request by the most severely late bus to the controller.)
Regarding claim 169, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
perform methods relating to at least one of transit-related emergencies, incidents, safety, and security, said methods comprising: (Smith: pg. 064; software . . . allows for emergency vehicle [emergency] and railroad pre-emption [safety], as well as transit signal priority applications [and enables s]ystem wide control strategies can be achieved by having each individual intersection controller/software send information back to the central end (traffic control center) and then redistribute it out to the individual intersection controllers.)
a) detecting and identifying events by OBUs and RSUs and producing event data (Smith: pg. 055; traffic control systems can monitor traffic more efficiently. Future traffic control systems [which include equipment acting as RSUs] will adapt to shifts in traffic and will change signal timing to meet demand. As traffic controllers and software acquire higher functionality, and wireless and fiber communications systems expand, it becomes more feasible for the transportation systems to track and provide priority to public transit vehicles, and therefore move more people more rapidly through the transportation system.)
b) transmitting event data to TOCs and the cloud-based platform (Smith: pg. 055; With heavy reliance on real-time information, buses could move as quickly as possible through traffic (utilizing TSP). Real-time arrival times could be provided directly to customers through beepers, pagers, e-mail, cell phones, PDAs and so on. With cell phones and PDAs becoming ubiquitous and AVL being rapidly deployed,)
c) analyzing and evaluating event data; d) producing action plans and CATV control strategies by the TOCs and transmitting said action plans and/or CATV control strategies to the cloud- based platform and/or TCC/TCU network (Smith: pg. 055; traffic control systems can monitor traffic more efficiently. Future traffic control systems will adapt [(produce action plans)] to shifts in traffic and will change signal timing to meet demand. As traffic controllers and software acquire higher functionality, and wireless [(cloud)] and fiber communications systems expand, it becomes more feasible for the transportation systems to track and provide priority to public transit vehicles, and therefore move more people more rapidly through the transportation system.)
e) sending warnings to transit users; f) updating a scheduling and dispatching plan to produce an updated scheduling and dispatching plan and transmitting said updated scheduling and/or dispatching plan to CATVs (Smith: pg. 055; Real-time arrival times could be provided directly to customers through beepers, pagers, e-mail, cell phones, PDAs and so on. With cell phones and PDAs becoming ubiquitous and AVL being rapidly deployed,)
Regarding claim 170, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Tachet further teaches:
configured to perform a method for transportation behavior prediction and management at a microscopic level, said method comprising (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control. . . the higher performance of SIs when compared with traffic lights comes from their increased flexibility, finer granularity in merging traffic flows) (Tachet: pg. 2; vehicles might communicate with roadside infrastructure and other vehicles to produce better coordinated flows. Furthermore, autonomous driving is starting to enable the careful control of vehicle trajectories and the synchronization of their arrival times at the intersections and better usage of road space.)
a) providing longitudinal and lateral control of CATVs wherein the control instructions include vehicle longitudinal and lateral position, speed, and steering and control; (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control. . . the higher performance of SIs when compared with traffic lights comes from their increased flexibility, finer granularity in merging traffic flows) (Tachet: pg. 2; vehicles might communicate with roadside infrastructure and other vehicles to produce better coordinated flows. Furthermore, autonomous driving is starting to enable the careful control of vehicle trajectories and the synchronization of their arrival times at the intersections and better usage of road space.)
b) detecting incidents, monitoring CATV components and sub-systems, providing real-time weather information, and adjusting speed according to detected speed zones; and (Aoude: ¶ 057; speed of will change due, for example, to traffic conditions, speed limits on the route, traffic signals, and other factors, the expected time of arrival at the intersection changes continuously [therefore are supplemented with] observed by a sensor tracking that entity either from the entity or from an external location. The data captured by these sensors can be used to model the patterns of motion, behaviors, and intentions of the entities. Machine learning can be used to generate complex models from vast amounts of data.) (Aoude: ¶ 150; An SRSE can process data received directly from sensors, or data received in broadcasts from nearby SRSEs, emergency and weather information, and other data)
And Smith further teaches:
c) providing route planning and guidance and managing transit network demand (Smith: pg. 055; traffic control systems can monitor traffic more efficiently. Future traffic control systems will adapt [(produce action plans)] to shifts in traffic and will change signal timing to meet demand. As traffic controllers and software acquire higher functionality, and wireless [(cloud)] and fiber communications systems expand, it becomes more feasible for the transportation systems to track and provide priority to public transit vehicles, and therefore move more people more rapidly through the transportation system.)
Regarding claim 171, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
a) dedicated lanes used by CATVs for customized and non-customized mobility services (Smith: pg. 049; LRT applications were almost exclusively deployed on dedicated right-of way (ROW) separate from vehicular traffic flow.)
b) dedicated lanes shared by CATVs and non-automated transit vehicles, wherein RSUs send control instructions to CATVs (Smith: pg. 049; streetcar systems, such as in Philadelphia, that operates on roadways with mixed traffic flow.) (Smith: Fig. 005; [showing exchange of information between OBEs in fleet vehicles and RSEs on traffic controllers])
and the control instructions include vehicle longitudinal and lateral position, speed, and steering and control;(Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control. . . the higher performance of SIs when compared with traffic lights comes from their increased flexibility, finer granularity in merging traffic flows) (Tachet: pg. 2; vehicles might communicate with roadside infrastructure and other vehicles to produce better coordinated flows. Furthermore, autonomous driving is starting to enable the careful control of vehicle trajectories and the synchronization of their arrival times at the intersections and better usage of road space.)
and c) non-dedicated lanes shared by CATVs and human-driven vehicles (Smith: pg. 049; streetcar systems, such as in Philadelphia, that operates on roadways with mixed traffic flow.)
Regarding claim 173, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Aoude further teaches:
configured to provide safety and efficiency measures for CATV operations and control to achieve automated driving for CATVs, said safety and efficiency measures comprising: (Aoude: ¶ 028; in an aspect, data is received from infrastructure sensors representing positions and motions of road vehicles being driven or pedestrians walking in a ground transportation network. Data is received in virtual basic safety messages and virtual personal safety messages about states of the road vehicles and pedestrians. The received data is applied to a machine learning model trained to identify dangerous driving or walking behavior of one of the road vehicles or pedestrians. The dangerous driving or walking behavior is reported to authorities) (Aoude: ¶ 166; the SRSE can build a unified view of the intersection that would help in the analysis of traffic flows and the detection and prediction of dangerous situations.)
a) RSUs providing a location service describing CATV location without the support of vehicle-based sensors (Aoude: ¶ 146; a radar 1004 mounted on a beam 1005 above the road at the intersection will detect the entity 1002 and its speed and distance. This information can be relayed to the connected entity 1001 through the SRSE 1011 serving as a bridge between the non-connected entity 1002 and the connected entity 1001.)
b) RSUs, TCC/TCU network, and cloud-based platform providing site-specific weather and pavement condition information (Aoude: ¶ 150; An SRSE can process data received directly from sensors, or data received in broadcasts from nearby SRSEs, emergency and weather information, and other data)
Tachet further teaches:
c) CATV control; wherein the control instructions for CATVs include vehicle longitudinal and lateral position, speed, and steering and control; (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control. . . the higher performance of SIs when compared with traffic lights comes from their increased flexibility, finer granularity in merging traffic flows) (Tachet: pg. 2; vehicles might communicate with roadside infrastructure and other vehicles to produce better coordinated flows. Furthermore, autonomous driving is starting to enable the careful control of vehicle trajectories and the synchronization of their arrival times at the intersections and better usage of road space.)
Aoude further teaches:
and d) CATV routing and control (Aoude: ¶ 225; [if an unconnected] vehicle about to merge into the same lane, process it and determine whether the maneuver presents a potential danger and if it should display a lane change warning to the vehicle's driver)
Regarding claim 174, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
a) hardware security; b) network and data security; and (Smith: pg. 088; Advanced (or Electronic) Fare Collection (AFC): means of collecting transit fares using an electronic medium such as a magnetic stripe or smart card (containing an embedded computer chip) which stores various data on validity, transactions, security protection)
c) reliability, resilience, and redundancy (Smith: pg. 003; method to enhance regional mobility by improving transit travel times and reliability,)
Regarding claim 175, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Aoude further teaches:
provide blind spot detection for CATVs (Aoude: ¶ 220; A BS/LCW addresses crashes where a vehicle made a lane changing/merging maneuver prior to the crashes and alerts drivers to the presence of vehicles approaching or in their blind spot in the adjacent lane)
in dedicated lanes and non-dedicated lanes, wherein (Aoude: ¶ 006; “ground transportation” [includes] motorized vehicles (autonomous, semi-autonomous, and non-autonomous) [non-dedicated lanes], and rail vehicles [dedicated lanes])
a) blind spot detection for dedicated lanes comprises collecting and fusing data collected by RSUs and OBUs describing the road and environment for CATVs and characterizing blind spots using said data; and controlling CATVs using said data b) blind spot detection for non-dedicated lanes comprises collecting and fusing data collected by RSUs and OBUs describing the road and environment for CATVs, non-automated vehicles, and moving entities on the road side; and controlling CATVs using said data; and c) displaying the data describing the road and environment for a CATV on a display in said CATV, wherein when the data collected by an RSU and OBU conflict, the confidence of each data source is used to judge and decide the final outputs (Aoude: ¶ 225; external sensors installed on the surrounding infrastructure can detect and track an unconnected vehicle attempting a lane change maneuver, collect basic information such as speed, acceleration, heading and past trajectory and transmit them to the RSE. The RSE will in turn build the predicted trajectory for the vehicle changing lanes using rule-based and machine learning algorithms, populate the required fields for the VBSM, and broadcast it on the behalf of the unconnected remote vehicle. The endangered vehicle's OBE will then receive the VBSM with information about a vehicle about to merge into the same lane, process it and determine whether the maneuver presents a potential danger and if it should display a lane change warning to the vehicle's driver. If the vehicle changing lanes is a connected vehicle, its OBE can similarly receive VBSMs from the RSE about a vehicle in its blind spot and determine whether the lane change maneuver presents a potential danger to surrounding traffic and if it should display a blind spot warning to the vehicle's driver)
Claim 164 is rejected under 35 U.S.C. 103 as being unpatentable over Smith in view of Aoude in view of Tachet as applied to claim 160 above and further in view of Galon (US 20200100057 A1). As regards the claims:
Regarding claim 164, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
c) detecting the opened or closed state of a CATV door (Smith: pg. 102; Once activation points are created, granting of priority may also be linked to other factors such as activation of passenger push buttons, length of time for door openings, and historic likeliness of stopping.)
d) detecting completion of passenger onboarding and offloading (Smith: pg. 153; Modern APC systems collect data on boardings, alightings (passengers getting off the vehicle), passenger loads, vehicle location, schedule adherence, vehicle speed, and vehicle movement (e.g. idling, stop dwell time, traffic stop and go movement, etc.). Data is collected continuously during the day and downloaded automatically in the garage for subsequent analysis.)
e) coordinating entry order and stops points for CATVs arriving at a stop; and providing warnings relating to abnormal states of CATVs and managing abnormal states of CATVs (Smith: pg. 064; signal control software [acting as an RSU] allows for emergency vehicle and railroad pre-emption, as well as transit signal priority applications [and enables s]ystem wide control strategies can be achieved by having each individual intersection controller/software send information back to the central end (traffic control center) and then redistribute it out to the individual intersection controllers.)
and f) providing warnings relating to abnormal states of CATVs and managing abnormal states of CATVs. (Aoude: ¶ 006; Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.)
Previously applied art does not explicitly teach:
a) determining the appropriate stop platform of a CATV; however, Galon does teach:
a) determining the appropriate stop platform of a CATV (Galon: ¶ 009; crowdsourcing method comprises: spatially clustering stop-lookup locations for the transit stop to identify a plurality of candidate clusters; determining a centroid for each of the plurality of candidate clusters and designating the plurality of centroids as a plurality of "candidate transit stop locations").
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Smith with the teachings of Galon because there are a finite number of predictable solutions to picking passengers up for transportation, including accurately determining a meeting location, or stop, and accurately guiding the vehicle to that location for boarding.
Galon further teaches:
b) determining the accuracy of CATV's position in relationship to the stop platform (Galon: ¶ 010; crowdsourcing method further comprises assessing if parameters of the winning candidate transit stop location meet threshold criteria for accuracy of data and/or likelihood of reflecting the actual transit stop location.)
Claims 165, 167, 172, and 176 are rejected under 35 U.S.C. 103 as being unpatentable over Smith in view of Aoude in view of Tachet in view of Gerla as applied to claim 160 above and further in view of Khosla (US 20190248396 A1). As regards the individual claims:
Regarding claim 165, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Smith further teaches:
configured to provide at least one of
and non-customized mobility services (Smith: pg. 178; comparisons are made between the actual arrival time and the scheduled [non-custom] time. If the bus is running on schedule then signal priority is not requested.)
Previously applied art does not explicitly teach:
provide customized mobility services; however, Khosla does teach:
provide customized mobility services (Khosla: ¶ 006; the autonomous vehicle (“AV”) can be configured among the other vehicles and railway to communicate with a rider on a peer-to-peer basis to pick up the rider on demand,).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Smith with the teachings of Khosla because known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art (MPEP 2143 (I)). Here the predictability of custom mobility services such as taxis and ride sharing applications would motivate an inventor to apply it to CATVs.
Regarding claim 167, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Previously applied art does not explicitly teach:
a) share and obtain traffic data between said transit management system and other shared mobility systems; however, Khosla does teach:
a) share and obtain traffic data between said transit management system and other shared mobility systems (Khosla: ¶ 007; initiating movement of the autonomous vehicle [and inter alia] may communicate with other autonomous vehicles or stationary devices alongside the track or road).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Smith with the teachings of Khosla because applying a known technique to a known method ready for improvement to yield predictable results is obvious (MPEP § 2143 (I)), here using communication systems to share traffic state data, is well known and understood in the art and is widely used by consumer products.
Khosla further teaches:
b) share and obtain traffic incidents between said transit management system and other shared mobility systems (Khosla: ¶ 012; examples can include a safety process to transport passengers or freight along the track or railway system by detecting events that can lead to an unsafe event for the passenger or freight or other objects such as cars in a cross-road.)
c) share and obtain passenger demand patterns between said transit management system and other shared mobility systems (Khosla: ¶ 010-011; system is configured to create a on demand schedule [based upon demand patterns and] the method further comprises moving the AV to an adjacent system configured with the rail, the adjacent system comprising at least one of a roadway, a waterway, airway, [etc.; and] dynamically detect a reflectance of an event from a plurality of events [which requires sharing the demand schedule])
d) dynamically adjust pricing; (Smith: pg. 088; smart card (containing an embedded computer chip) which stores various data on validity, transaction s, security protection, and in some cases user-related data type of user (e.g. senior, student or person with disabilities), loyalty programs, etc.)
e) provide for special agencies to delete, change, and share information (Smith: Fig. 002; [showing a database sharing data]).
(f) provide for the transit management system to take control of vehicle longitudinal and lateral position, speed, and steering and control to achieve automated driving for CATVs; (Tachet: pg. 7; vehicles approaching an SI are not grouped in queues near the intersection, but uniformly spread along the road thanks to speed control. . . the higher performance of SIs when compared with traffic lights comes from their increased flexibility, finer granularity in merging traffic flows) (Tachet: pg. 2; vehicles might communicate with roadside infrastructure and other vehicles to produce better coordinated flows. Furthermore, autonomous driving is starting to enable the careful control of vehicle trajectories and the synchronization of their arrival times at the intersections and better usage of road space.)
g) provide for CATVs forming platoons with vehicles of other shared mobility service providers (Khosla: ¶ 087; the vehicles, each of which is smaller in size than a conventional rail car, can convoy with each other to form a draft line for efficiency. The convoy can be a plurality of vehicles physically attached to each other or preferably coupled to each other via electronic control of each of the vehicles using the sensor array to keep a pair of vehicles from touching each other, while maintaining a desired distance to achieve minimal or improved wind resistance, and other efficiencies. Of course, there can be other variations, modifications, and alternatives.)
h) provide for special agencies to take control of vehicles; i) provide for the transit management system to take control of vehicles that arrive at a platform; and j) provide the transit management system to take control of vehicles that depart from a platform; (Khosla: ¶ 084; In an example, the vehicle has a controller device coupled to the communication device to initiate movement of the drive train of the autonomous vehicle to move the autonomous vehicle to pick up the user from a pickup location along the railway system and to move the autonomous vehicle, independently from the other plurality of autonomous vehicles, along the railway system to a destination location.)
Regarding claim 172, as detailed above, combination Smith teaches the invention as detailed with respect to claim 160. Previously applied art does not explicitly teach:
configured as a platform to provide functions for information inquiry by passengers and managers, customized automated driving services, legal and regulatory services, coordination and aid, broadcast, and/or user management; however, Khosla does teach:
configured as a platform to provide functions for information inquiry by passengers and managers, customized automated driving services, legal and regulatory services, coordination and aid, broadcast, and user management (Khosla: ¶ 006; autonomous vehicle (“AV”) can be con