Patent Application
20160334235
Inefficiencies of the present situation of sole dependence only on the private automobile and congested highways cost U.S. commuters 4.2 billion hours, 2.8 billion gallons of fuel, and $200 billion per year, not to mention road rage. The Informed Traveler Program and Application (“ITPA”) employs a smartphone and personal electronic device based interface to provide personalized, timely information and advice regarding the most efficient and cost-effective travel paths consistent with the traveler's destination and schedules. This includes information about whether to use transit, delay the start of a trip to avoid congestion, or take an alternate route to avoid rush hour, construction, accident or other delays. It is a predictive and real time decisional system that integrates large volumes of stochastic data into an end-user's choice process. It takes into account specific user needs and travel or medical limitations when providing travel information and choices. It provides specific guidance as to timing allowed for and directions during intermodal transfers. The system is constantly predictive in nature, utilizing environmental, real time and archival data to allow the end user to make better travel decisions even before entering a private vehicle. It also offers ITPA users the holistic possibilities of express transit routes and faster parking in “smart” garages associated with the system, which is a major time saver. It works on smart phones and mobile computing devices, and has both audio and visual capabilities similar to standard GPS-based devices, but possesses intelligence beyond these standard capabilities that considers user needs, situational conditions, multimodal and intermodal options, and safety and environmental concerns, to produce a customized and holistic solution.
This unique application of innovative technology and computational power to transit is a pioneering step intended to serve as a model for communities throughout the nation. IPTA thereby arms travelers with both the information and the confidence, through experiential reinforcement arising from continued IPTA use, to delay travel plans, change routes, or take public transit instead of following a reflexive pattern of automotive travel. It must thus be seen that the intelligent system is not only continuously and iteratively training itself but is also training the end-user and their choices.
An ITPA user will no longer thoughtlessly jump into a private vehicle without synergistically combining, when warranted, other forms of transport including bus, ferry/water taxi, transit, bicycle, underground, and even walking in the alternative sequences suggested by ITPA. ‘Stop and Go’ can translate into a smooth ‘Fast and Slow’. Depending on the need, services like Smart Cars and Uber could be integrated into the system.
For a better understanding of how the ITPA can benefit users, consider the following three scenarios, whose examples below are summarized for better understanding.
A third of the way on her commute, she discovers through ITPA, which uses not only real-time traffic information, but also historical inputs and alerts her of arterial highway congestion. She is given the following information.
1. Congested traffic on the normal arterial route.
2. Time and cost benefits of choosing one of several mass transit alternatives by exiting the arterial route.
3. Alternatively rescheduling the appointment and waiting at the next exit and profitably using the time either for shopping or working on her laptop.
Annette is in a rush and she takes ITPA advice to park at the Sheridan Street Tri-Rail Station two exists southward of her current location and take the Tri-Rail train that ITPA says is scheduled to leave within five minutes after her projected Sheridan Station arrival. ITPA confirms that the train is expected to arrive on time.
ITPA's assistance includes indicating parking spots at the destination in the most convenient smart-garage associated with the system. Consequently Annette arrives more refreshed and suffers less stress while starting her day's work.
A colleague of Annette who was also informed by ITPA of congested conditions on the highway nevertheless consciously chooses to bring his car to the office. He has an overriding need to keep his car with him for onward travel during the day.
Francisco would have missed his appointment but for the ITPA system locating a parking spot and reserving it in the smart garage nearest to his appointment.
Jose does not have a car. He relies on ITPA for transit guidance.
Several existing patents suggest an intelligent traffic navigating and routing system with several inputs for guidance. Others cover vehicle safety and parking monitoring. None, however, actually takes into account all the variables of the present ITPA system and indeed none approaches the intelligent algorithmic functionality that ITPA provides.
U.S. Pat. No. 7,957,871 B1 issued to Echeruo on Jun. 7, 2011, assigned to Hopstop.com, Inc. in New York City discloses a Method and Apparatus for Navigation in Urban Environments, whose embodiments include methods, systems, and apparatuses for providing maps and directions, including walking and mass transit directions in urban environments where users do not drive cars. Additionally, driving directions can be included. Only certain preferences, such as amount of walking, for or against use of particular streets, certain mass transit vehicle types, as well as the number of transfers between mass transit vehicles, are considered in the routing calculations, as well as non-individual information such as current conditions and other information. The system provides for a user feedback loop to determine subsequent routing decisions. The invention also provides web services including textual and graphic input for third parties, such as the travel industry, to access such services and for clients to edit mass transit information. Specifically the system teaches away from and does not provide for integration of the use of a personal vehicle. Moreover, it does not provide for analytics of archival data, historic patterns, or crowd-sourced data. It largely consists of a path-solver based on an extension to Dijkstra's algorithm.
United States Patent Application US 2005/0021224 A1 by Gray, published Jan. 27, 2005, discloses a control system which activates countermeasures to hazards indicated by a database and by information systems external to a vehicle by activating a plurality of vehicle systems. This control system analyzes location and time information regarding multiple situations that may coincide with the path of the vehicle and controls vehicle systems to provide countermeasures to hazards when probability of encountering those hazards meets a sufficient threshold. The system is an advanced vehicle and road safety tool but in no way rises to the level of a routing or traffic control system at the macro level and simply provides for making driving safer as opposed to combining vehicle use with transit in a smart way so as to optimize a client's time and expense and leave a greener carbon footprint for the environment.
United States Patent Application US 2002/0109610 A1 by Katz, published Aug. 15, 2002, for a Parking Status Control System and Method, discloses a parking status control system and method allowing a parking space, or plurality of parking spaces, to be automatically monitored to detect unauthorized use. The system and method may be applied to metered parking spaces or to situations where controlled access to a parking space or area is desired. Presence of a vehicle in a monitored parking space is determined using a vehicle presence detector, which communicates a signal indicative of such presence to a central system. A user or vehicle based authorization module is configured to transmit an authorization input to facilitate automated satisfaction of a space authorization device, e.g., payment of a parking meter. Where the space is detected as occupied without proper authorization, the central system declares a violation and communicates this to another system or individual charged with taking corrective action. The system does not discuss monitoring of all parking spaces within a given vicinity or neighborhood by a central system and integration with an intelligent commuter based solution.
United States Patent Application Publication US 2009/0005963 A1 by Jarvinen, published Jan. 1, 2009 and assigned to Nokia Corporation, discloses an apparatus for providing improved route planning, which may include a processing element. The processing element may be configured to receive calendar information from at least one driver and at least one passenger that are each registered to a service having a routing functionality, and determine a transportation plan including a travel route based on the received calendar information.
United States Patent Application Publication US 2011/0208417 A1 by Fink et al., published Aug. 25, 2011, for a System and Method of Representing Route Information, assigned to Research in Motion, wherein a route comprises interconnected road segments which may be traveled to get from one location to another (fixed destination), wherein a route is represented as a scaled linear shape, whose portions represent portions of the route, regardless of spatial arrangement of the apportioned segments. A scale is applied between characteristics of the route and the linear shape, integrating information elements along the linear shape which correspond, according to the scale, to locations on the route corresponding to these information elements, and allowing for color-coding or cross-hatching to represent traffic congestion. Incident reports may also be represented by indicators along the linear shape. Alternative routes, such as detours, may be represented by respective separate linear shapes; lead lines may connect such alternative route linear shapes to points along the primary route linear shape where each such detour or alternative would be taken. This system is a perfect route indicator and uses very instructive coding, but does not really take into account integrating multiple modes of transportation, or optimization of a user's schedule or time.
U.S. Pat. No. 7,761,225 B2 issued to Vaughn on Jul. 20, 2010, assigned to IBM, discloses a routing method and system. The method includes receiving a user profile via a GPS transceiver, which comprises user preference data and destination location data. The GPS transceiver also retrieves first geospatial coordinate values for the user's current as well as destination location. The GPS transceiver then processes the user profile and the first geospatial coordinate values to identify a geographical route for traveling from the current location to the destination location. The GPS transceiver retrieves current and historical traffic speed and density data associated with second geospatial coordinate values for various locations located along the first geographical route, and processes the data to determine if the first geographical route comprises an efficient route for the user.
U.S. Pat. No. 7,741,977 B2 issued to Buchalo et al. on Jun. 22, 2010, assigned to Motorola, discloses a method and apparatus for calculating the travel time of a vehicle as it transits through multiple locations. It includes a device for detecting a radio signal from a vehicle, attaching information to the radio signal, and transmitting a message packet with the signal and attached information to a central server, which then stores the packet. The central server compares the information in the message packet against other stored message packets received from multiple locations. When matching information is found, an algorithm is run to compute a vehicle travel time between two locations.
U.S. Pat. No. 6,317,686 B1 issued to Ran on Nov. 13, 2001, assigned to Trafficcast.com, discloses a method of providing travel time, comprising a traffic information system for predicting travel times that utilizes Internet-based collecting and disseminating of information. The system accounts for vehicle type, driver specific disposition, and its predictions of future traffic account for the effects of predictable events, particularly weather, on traffic patterns. The traffic information system includes a computer model of a transportation route map, the route map having a multiplicity of possible destinations points connected by route segments. An equation is developed for each route segment, the equation incorporating variables and constants which relate to the fixed and variable parameters which are indicative of the time it will take to travel along a particular route segment. Predicted travel time along the route segment can be improved over historical data for a time in the future for which there are reasonably accurate weather predictions. Incorporation of the effect of predicted weather on travel time over a route segment can be accomplished by developing a correlation between weather conditions and decreased traffic speeds. Personalized prediction times are generated by taking into account the vehicle type and level of aggressiveness of a particular driver.
U.S. Pat. No. 5,687,360 issued to Chang on Nov. 11, 1997, assigned to Intel, discloses a branch predictor using multiple prediction heuristics and a heuristic identifier in the branch instruction, wherein, in a computer program, a branch instruction selects a prediction heuristic from a plurality of prediction heuristics for predicting whether the particular branch will be taken during execution of the program by a computer. A current pattern comprises a number of consecutive identical branch decisions for the instruction. A prior pattern comprises a number of consecutive identical prior branch decisions for the instruction, the prior branch decisions occurring prior to the branch decisions comprised by the current pattern. The selected prediction heuristic generates a branch prediction using the current pattern and the prior pattern. The selected prediction heuristic is identified by adding profiling instructions to the program to compute history information for the branch instruction. The profiling instructions input the branch history information to a plurality of prediction heuristics, and each prediction heuristic outputs a prediction of whether the branch instruction will be taken. The program is executed with a sample data set, and the output of each prediction heuristic is compared to the branch decision for the instruction to identify which heuristic most accurately predicts the branch decision for the branch instruction.
The Informed Traveler Program and Applications (ITPA) is an advanced travel advisory system that provides its smart phone-based users with predictive analytical information as to multimodal travel routing and parking options. This commuter-oriented analytical system supports large-scale transportation demand management and provide useful information to travelers allowing them to make informed decisions regarding: time and duration of travel; choices of mode of travel; and alternative non-direct multimodal routes. By this means, ITPA is intended to provide travel advice to help distribute its users in time and space and by mode of travel in order to optimize: individual ITPA customer trips; the choice of options which may be availed when trips are delayed; as well as multimodal transportation system capacities and relief of congestion on major traffic arteries.
ITPA informs travelers who are ITPA customers of the best possible options available to them given a full understanding of choices in space, over time and by mode using predictive analysis. Such an array of options is not seen in the prior art.
As ITPA predicts if one segment of the transportation fills up or is not otherwise available to be timely used based on predictive departure and arrival times, travelers are dispersed to minimize trip segment times and costs and so as to avoid unsafe or maybe unpleasant conditions and so as to usefully put to use time before trip starts and after trip ends as well as during intermodal transfer times. By this means, travel is optimized for each ITPA customer given system capacity to deliver multimodal transport, timely intermodal transfers and desired ETAs are selected destination.
The present invention further relates to a method of informed decision making in multi-modal travel to a definite final destination via a plurality of potential routes comprising the following steps: 1. Utilizing of real-time travel-related data relative to a plurality of inputs such as present traffic flow, emergency events, roadway construction, special community events, weather, historic traffic-affecting trends, and parking conditions at the informed traveler's destination to calculate a plurality of potential decisional outputs; 2. Archiving at least one such decisional output of the first step to a historical archival database of decisional outputs/decisions/choices; 3. Generating a current spatial analysis of real-time traffic flow; 4. Applying predictive and analytical computational models, such as goal planning, heuristic methods and fuzzy logic, using rule-based constraints to selective decisional outputs of the first and third steps; 5. Identifying, verifying, authenticating authorized users; and 6. Providing electronic access portals to the informed traveler.
The present method of providing traveler guidance to reach a predetermined destination in a multi-modal transit network may further comprise the following steps: 1. Establishing a database including a matrix of daily routes of category A trains, inclusive of times of day at each station or stop thereof, said stations or stops within a commutable distance of said pre-determined destination, the trains including any mode of fixed guide-way transit (such as trains, trams, trolleybuses, and the like); 2. Establishing a database including a matrix of daily routes of Category B trains and Category B express buses having routes that include a common station or transfer point of intersection within the route of at least one of said Category A trains, the stops and stations of these category B trains and buses within a commutable distance of the final destination; 3. Establishing a database including a matrix of daily routes of local buses or community transit vehicles of Category C within a commutable distance of said destination, at least one of said routes having a common stop, station or transfer point of intersection with at least one of the routes of said trains or buses of category A or B; 4. Monitoring real-time traffic conditions upon major road arteries within the commutable distance of the final destination; 5. Monitoring real-time events of actual and prospective vehicle congestion, by sector, upon said major arteries within said communicable distance, and generating wireless alerts to concerned travelers when definable actual or prospective vehicle congestion occurs; and 6. Generating, for use by actual and prospective en-route travelers on the major arteries, suggestions of alternative routing by transferring to a train station of said Category A or B trains, or bus routing to the final destination, by electronically overlaying or querying said matrices of said databases regarding the schedules of Category A trains, Category B trains or buses, and Category C local buses or community transit vehicles, to determine stops or stations thereof in the vicinity of the traveler upon the said major artery, allowing the traveler, should he wish, to park his vehicle and, within a user acceptable timeframe, board a train or bus and thereafter, as necessary, board a second train or bus to more efficiently reach the pre-determined destination.
An objective of IPTA is to provide the traveler or commuter with the benefits of a basic Intelligent Transportation System (ITS) along with the benefits of a modeling system that predicts traffic conditions hours before they occur such that it can advise the ITPA users how to make optimum use of the existing transportation capacity through large-scale Transportation Demand Management (TDM) strategies. When applied to a multitude of individual informed travelers, the need to build additional highway capacity is reduced and the ridership and customer revenues of mass transit are increased.
Another objective of the system is to significantly reduce traffic congestion.
A yet further objective is to enable transportation agencies to collect real-time data needed to measure and improve the performance and capacity of the transportation system by the least expensive means possible, making ITPA the centerpiece of efforts to reform surface transportation system and hold providers accountable for results.
A still further objective is to use advanced information technology platforms, advanced computing architecture, ITS, predictive modeling systems, specific traveler interests and needs, and smartphone-based software, to substantially reduce: vehicle miles traveled; greenhouse gas (GHG) emissions; and travel time, costs, and stress.
Another object of the invention is to provide to transportation system managers an expert transportation information system planning tool for their communities, which identifies locations where, despite the operation of the system, traffic bottlenecks in fact still accumulate, so that they can better plan how best to scale multimodal transportation capacity in the future by projecting the existence of alternative multimodal improvements and determining by scenario which alternative performs best in optimized traffic conditions through benefits and costs analysis.
The above recited objects and advantages of the present invention will become apparent from the hereinafter set forth Brief Description of the Drawings, Detailed Description of the Invention and Claims appended herewith, but are not intended to limit the scope of the invention.
As may be seen in
Situational awareness when applied to travel may been seen as “a perception of elements in the environment within a volume of time and space, a proper factoring in of their relative impacts (particularly when aggregated in relation to an operator's goals), and their projected significance in the near future”. Transportation Data Management has been broadly defined as: “the application of strategies and policies to reduce travel demand, especially that of single-occupancy private vehicles, or to redistribute this demand in space and time.”
Many of these situations include everyday routine scenarios, such as rush hour traffic congestion and safety issues arising out of stop-and-go rush hour traffic as well as traffic congestion, and from specific anomalies, such as accidents, weather, public events such as sporting events, concerts, parades and processions, construction, government contingencies (i.e., security convoys for visiting dignitaries and the like), parking logjams, or commercial contingencies (e.g., special oversized passenger or freight movements).
A basis for advanced situational awareness and enhanced large-scale transportation data management is achieved through the use of historical traffic data and the development of best methods for data integration and analysis of the following statistical and situational data:
1. Detailed maps, routes and driving directions
2. Regional express bus, fixed-guideway transit, and train schedules (regional mass transit) between residential communities, universities, multimodal transportation centers, airports, seaports, major regional destinations, tourist hubs and job centers
3. Airline and waterborne transport schedules
4. Transportation capacities of common carriers providing services in, to, or from a region
5. Real-time location and actual and projected arrival/departure times for regional mass transit, airline, and waterborne transport
6. Real time traffic congestion information (rush hour or otherwise) on highways and arterials and on certain local streets that are determined by transportation system managers to be useful for regional travel as shortcuts or alternate routes as between highways as well as around frequently congested highway segments
7. Intermodal timing estimates for movements between specified locations on highways, arterials, and certain identified local streets, and the immediate access points for regional mass transit, airline, and waterborne transport
8. Smart parking garage information as to location of available parking spaces or reserved parking opportunities
9. Information that is confidentially retained by ITPA as to transportation preferences provided by the individuals who subscribe the ITPA service to help optimize the subscriber's trips
10. Information provided by ITPA sponsors who are featured as useful alternate business or other destination and broadcasted to ITPA users when needed to fulfill a travel requirement or need.
The above is shown conceptually in the non-technical overview of
As may be seen, ITPA core services 100 communicates with interior interfaces 102, 104, 106, 108, 110, 112, 114, and 116 in which:
Each of the above, in turn, interfaces with outermost layers 118, 120, 122, 124, 126, 128 and 130 in which:
The implementation of the above may be more fully appreciated with reference to
More specifically,
Moving to the left of TerraFly Services 119 in
All transportation and related data is updated at the most frequently possible intervals. This capability provides the system with the necessary information backbone to keep ITPA travelers current and informed and thus able to make intelligent decisions as follows.
With further reference to
Further shown under ITPA services 101 in
Moving to “In-field Subsystems” within
Smart phone app (see
Beneath Operations Center 110 is shown parking event detection module 112A, which includes camera systems, License Plate Recognition/License Plate Inventory (LRP/LPI), a data management system (DMS) and a database for recording information obtained therefrom. This information is available to all individual drivers 127 that subscribe to the present system. Therefrom individual drivers 127, after registration, may receive recommendations as to other routes or modes of travel which may result in savings of time, cost, or enhanced safety.
At the bottom center of
An example is to inform a traveler of a given planned route and current situational awareness information, for which a typical delay for automobile transportation along the planned route is 75 minutes while the delay for public transport is likely to be 15 minutes. If requested, ITPA could also estimate cost for each transportation option (i.e., cost to travel by private vehicle versus cost of regional mass transit and parking costs).
The system might recommend taking public transportation in this case, and provide information and routing guidance, which include various regional mass transit options (e.g., which trains, fixed-guideway transit or express buses to take and their likely departure time). This capability uses rules, analyses and predictive statistics (as well as heuristics and stochastics) to arrive at a recommendation. At first, travel suggestions would be limited the regional routes and destinations described in the situational awareness discussion above for which situational data is available. As the user base, database and historical archive grow, this would then make more choices available.
ITPA methodology involves routing instructions and guidance, including providing a traveler with alternative travel options and routing instructions based on their plans, on parking availability, and on situational awareness per CCTVs and other such inputs. For example, as ITPA anticipates congested roadways ahead, it would recommend alternative highways, arterial and street routes, identify locations of regional mass transit stations, and confirm ticket availability for boarding on such regional mass transit alternates. Importantly, when routing is requested, the system can also specifically include an analysis of available data concerning potential return trip conditions based upon time of day and reminder of any parking location used. The ITPA user will be given an opportunity at that time to make return trip plans and arrangements or to defer the decisions until later in the day. This provides users with more viable options; particularly in terms of the availability of regional mass transit (e.g., is regional mass transit available at the expected return time and what are the expected costs, including parking one's car in one location compared to another). If the recommended return trip is not desired by the ITPA user, a different return choice would be identified.
Routing guidance includes historical and predictive analyses of situational data for major routes when available. If neither real-time, nor historical, nor predictive data is available, then users are provided with, at the very least, turn-by-turn routing guidance similar to that available in standard GPS based navigation devices.
In
In
ITPA focuses on advanced real-time and predictive analytics rather than extensive in-situ hardware and sensor systems, although some of these are necessarily part of the system. It utilizes both crowd-sourced data, e.g. data collected by smart-phone apps, as well publicly available data, e.g. from toll and traffic monitoring stations, or directly on board transit-vehicles. Without dedicated sensors at its disposal, ITPA often does not have exact information but reaches high rates of accuracy by utilizing geospatial data-mining technologies, combined with agent-based traffic modeling and simulation, and using problem-solving algorithms, as well as combinatorial heuristics such as genetic algorithms and ant-colony optimization.
ITPA consists of several key elements as above noted:
The smart-phone app 138 (see
The computational server 108 (see
The routing server 104B enables inter-modal routing and recommendations, e.g. considering pedestrian and car traffic (including parking recommendations), taxi cabs, express and community transit, short-term car and bicycle rentals, and serves as a bridge to other participating transportation service companies, as well as planning authorities.
The database server 106 administers an extensive database of historical traffic data, updated in real-time from smart-phones, vehicle interaction modules and public data sources. It also stores a history of predictions, thus allowing for further validation and improvements of the predictive models.
The communications server 99 (see
The operations center module 110 (see
The vehicle interaction modules (see
To create the innovative ITPA system and software, existing intelligent transportation component assets already in use in “smart cities” around the world are combined with intelligent transportation and business analytics, spatial analytics, and other intelligent components as suggested by the ITPA non-technical architecture overview shown in
Within a common view elements screen 159 are said map based view 115 having a map layer 103A, transit layer 130, parking layer 112A and a user's GPS position 120A.
Within the parking data view 112 of the regional operations center module 111 is predictive data 148, real time data 150 and historic data 152. Within transit data view 114 is predictive data 154, real time data 156 and historic data 158. Module 111 interfaces with ITPA communication services 101A/101B through communications buss 160 of operations module 111. Therebetween are provided an exchange of a variety of information inclusive of requests, map data, user notifications, parking information, transit information, express transit routes and community transit routes.
Beneath the common view elements screen 159 of regional center module 111 is a smaller screen 160, above referenced, upon which one may selectably observe or modify express transit routes and configurations 162, community transit routes and configurations 164, parking recommendations and configurations 166, user data views 168, and user notifications 170 of edits and status to the common view elements screen 159.
The following snippets of pseudocode illustrate the operation a heuristic algorithm (morning-advice), with predictive models and rule-based constraints to all potential routes with potential intermodal transfer points, using real-time and historic data available to compute an optimal route and mode of transportation; and an optimization algorithm (navigate) by time, cost, duration and safety, to further narrow down options point-by-point along the route. The snippet syntax should be taken as generic and self-explanatory:
Shown in
Moving to the left of TerryFly data services 120 are a breakout of the specific data services 106 employed in the present system, namely, maps 115, parking 112, transit routes 114A, user tracking 109, and statistics 106A. Therefrom data flow proceeds to ITPA services 101 and, specifically, to user handling 101A, which, as may be noted, includes map requests, parking information, parking recommendations, transit information, transit requests, user tracking and user notifications. Therefrom data flows to a communications interface 161 and therefrom to smart phone application 102. Between interface 161 and smart phone app are data exchanges relative to maps, transit information, transit requests, parking information, and user notifications. Beneath user handling functions 101A are shown event handling functions 101B, namely, parking events, transit events, passenger information, transit tracking, transit routes 114, traffic information and user notifications 107. These in turn are fed to a communications interface 112A/160, above described in
As depicted in
In regard to
The overall aim, regardless of the model and paradigm being used is to reduce overall time and expense of trips, both to the individual driver as well as to the transit system, and to maximize and optimize usage of the transit system where possible. Routes are calculated with these aims in mind. A combination of heuristic and analytic algorithms may be used as well as a variety of hardware architectures.
Shown in
In regard to
In regard to
Option 3 as may be noted, consists of the rescheduling of her appointment, noted by block 404 and thereupon using such additional time to exit the interstate and use the generated time for other purposes, such as shopping, before resuming her trip, so that she may arrive at a proper time at the rescheduled appointment.
Option 2, as indicted in
As may be seen from the above at block 407 the driver must make a decision whether to park her vehicle at an available parking space at the auto vehicle garage of the Sheridan Tri-Rail Station and then board the Tri-Rail within 10 minutes thereafter, to continue in the congested traffic conditions (Option 1), or to re-schedule her appointment as indicated by Option 3 and at the right of
Nothing in this disclosure or claims should be construed as limiting the scope of implementation of the present invention, but merely as exemplary embodiments thereof.
This application claims the benefit under 35 USC 119 (e) of Provisional Patent Application Ser. No. 61/612,932, filed Mar. 19, 2012, as well as serves as a continuation in part of patent application Ser. No. 13/847,024 filed Mar. 19, 2013, which same applications are herewith incorporated in their entirety by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13847024 | Mar 2013 | US |
| Child | 14589809 | US |
