Patent Publication Number: US-2023137791-A1

Title: Heuristic evaluation systems and methods for spatial information deliverability

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present disclosure claims the benefit of priority of co-pending U.S. Provisional Patent Application No. 63/272,776, filed on Oct. 28, 2021, and entitled “HEURISTIC EVALUATION SYSTEMS AND METHODS FOR SPATIAL INFORMATION DELIVERABILITY,” the contents of which are incorporated in full by reference herein. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to the automotive navigation and electric vehicle (EV) charging fields. More particularly, the present disclosure relates to heuristic evaluation systems and methods for spatial information deliverability. 
     BACKGROUND 
     Users traveling beyond the range of their current charge are faced with choices in the known dimensions of “time, distance, availability, and price/quality of service.” The established trade-off in these dimensions might only answer and deliver on core service needs. The problem of how to meet demand with these resources is the best possible match. The user choice is focused squarely on the vehicle, and delivering the answer in relation to the vehicle dimension of moving from point A to point B most efficiently; within the context of “time, distance, availability, and price/quality of service.” 
     The present background is provided by way of illustrative environmental context only. It will be readily apparent to those or ordinary skill in the art that the concepts and principles of the present disclosure may be implemented in other contexts equally, without limitation. 
     SUMMARY 
     The present disclosure provides heuristic evaluation systems and methods for spatial information deliverability for EV owners who are traveling and looking for charging location suggestions via their navigation systems, mobile applications, and/or the like. Suggestions are captured based on prior knowledge and user desires. In general, the system knows that a user is traveling from point A to point B and has a suggested optimal distance and/or time and/or cost to reach point B. The system gathers information related to the traveler&#39;s desires in relation to lesser known dimensions, to enhance the overall quality of the travel and charging experience. 
     In one illustrative embodiment, the present disclosure provides a heuristic evaluation system for spatial information deliverability, the system including: memory storing instructions executed by a processor to receive an origination point and a destination point from a user and determine and suggest charging station locations to the user traveling from the origination point to the destination point based on time and distance considerations, hardware capability and availability considerations, and user preference attributes received from the user; and a display operable for displaying an ordered list of scored charging station locations encompassing the suggestions to the user. The display is one of a vehicle navigation display and a mobile device display. Determining and suggesting charging station locations to the user traveling from the origination point to the destination point based on time and distance considerations includes: determining whether or not the destination point can be reached from the origination point without a charge; if the answer is no, then determining available charging locations along available routes between the origination point and the destination point that can be reached based on an available charge; and, if the answer is yes, then determining available charging locations proximate the destination point that can be reached subsequent to reaching the destination point based on the available charge. The ordered list of scored charging station locations is formulated based on one or more of: total travel time from the origination point to the destination point and each charging station received from one of a navigation system and a navigation application, air temperature along the route received from one or more of a vehicle and a mobile device, hardware temperature received from one or more of the vehicle and each charging station, an expected charging time received from one of the vehicle and each charging station, and an expected charging cost received from one of each charging station and an infrastructure database. The ordered list of scored charging station locations is also formulated based on one or more of: cost of available kWh during an expected charging time received from one of each charging station and an infrastructure database, cost of parking during the expected charging time received from one of each charging station and the infrastructure database, and available hardware received from one of each charging station and the infrastructure database. The ordered list of scored charging station locations is further formulated based on one or more of: weather conditions at each charging station received from one or more of each charging station and a weather database, expected time of day for charging at each charging station, reported crime rate at each charging station, and amenities available at each charging station. 
     In another illustrative embodiment, the present disclosure provides a heuristic evaluation method for spatial information deliverability, the method including: at a memory storing instructions executed by a processor, receiving an origination point and a destination point from a user and determining and suggesting charging station locations to the user traveling from the origination point to the destination point based on time and distance considerations, hardware capability and availability considerations, and user preference attributes received from the user; and, by a display, displaying an ordered list of scored charging station locations encompassing the suggestions to the user. The display is one of a vehicle navigation display and a mobile device display. Determining and suggesting charging station locations to the user traveling from the origination point to the destination point based on time and distance considerations includes: determining whether or not the destination point can be reached from the origination point without a charge; if the answer is no, then determining available charging locations along available routes between the origination point and the destination point that can be reached based on an available charge; and, if the answer is yes, then determining available charging locations proximate the destination point that can be reached subsequent to reaching the destination point based on the available charge. The ordered list of scored charging station locations is formulated based on one or more of: total travel time from the origination point to the destination point and each charging station received from one of a navigation system and a navigation application, air temperature along the route received from one or more of a vehicle and a mobile device, hardware temperature received from one or more of the vehicle and each charging station, an expected charging time received from one of the vehicle and each charging station, and an expected charging cost received from one of each charging station and an infrastructure database. The ordered list of scored charging station locations is also formulated based on one or more of: cost of available kWh during an expected charging time received from one of each charging station and an infrastructure database, cost of parking during the expected charging time received from one of each charging station and the infrastructure database, and available hardware received from one of each charging station and the infrastructure database. The ordered list of scored charging station locations is further formulated based on one or more of: weather conditions at each charging station received from one or more of each charging station and a weather database, expected time of day for charging at each charging station, reported crime rate at each charging station, and amenities available at each charging station. The method further includes, at a user interface associated with the display, receiving from the user a selection of a desired charging station of the ordered list of scored charging station locations and determining a route to the desired charging station and the destination point from a current location of the user. 
     In a further illustrative embodiment, the present disclosure provides a non-transitory computer-readable medium stored as instructions in a memory and executed by a processor to carry out steps including: receiving an origination point and a destination point from a user and determining and suggesting charging station locations to the user traveling from an origination point to the destination point based on time and distance considerations, hardware capability and availability considerations, and user preference attributes received from the user; and, by a display, displaying an ordered list of scored charging station locations encompassing the suggestions to the user. The display is one of a vehicle navigation display and a mobile device display. Determining and suggesting charging station locations to the user traveling from the origination point to the destination point based on time and distance considerations includes: determining whether or not the destination point can be reached from the origination point without a charge; if the answer is no, then determining available charging locations along available routes between the origination point and the destination point that can be reached based on an available charge; and, if the answer is yes, then determining available charging locations proximate the destination point that can be reached subsequent to reaching the destination point based on the available charge. The ordered list of scored charging station locations is formulated based on one or more of: total travel time from the origination point to the destination point and each charging station received from one of a navigation system and a navigation application, air temperature along the route received from one or more of a vehicle and a mobile device, hardware temperature received from one or more of the vehicle and each charging station, an expected charging time received from one of the vehicle and each charging station, and an expected charging cost received from one of each charging station and an infrastructure database. The ordered list of scored charging station locations is also formulated based on one or more of: cost of available kWh during an expected charging time received from one of each charging station and an infrastructure database, cost of parking during the expected charging time received from one of each charging station and the infrastructure database, and available hardware received from one of each charging station and the infrastructure database. The ordered list of scored charging station locations is further formulated based on one or more of: weather conditions at each charging station received from one or more of each charging station and a weather database, expected time of day for charging at each charging station, reported crime rate at each charging station, and amenities available at each charging station. The steps further include, at a user interface associated with the display, receiving from the user a selection of a desired charging station of the ordered list of scored charging station locations and determining a route to the desired charging station and the destination point from a current location of the user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated and described with reference to the various drawings, in which like reference numbers are used to denote like system components/method steps, as appropriate, and in which: 
         FIG.  1    is a flowchart illustrating one embodiment of the general method of the present disclosure; 
         FIG.  2    is a network diagram of a cloud-based system for implementing the various systems and methods of the present disclosure; 
         FIG.  3    is a block diagram of a server/processing system that may be used in the cloud-based system of  FIG.  2    or stand-alone; and 
         FIG.  4    is a block diagram of a remote device that may be used in the cloud-based system of  FIG.  2    or stand-alone. 
     
    
    
     DETAILED DESCRIPTION 
     Again, the present disclosure provides heuristic evaluation systems and methods for spatial information deliverability for EV owners who are traveling and looking for charging location suggestions via their navigation systems, mobile applications, and/or the like. Suggestions are captured based on prior knowledge and user desires. In general, the system knows that a user is traveling from point A to point B and has a suggested optimal distance and/or time and/or cost to reach point B. The systems gathers information related to the traveler&#39;s desires in relation to lesser known dimensions, to enhance the overall quality of the travel and charging experience. 
     Known dimensions in making EV charging suggestions include, for example:
     Preferred capabilities—Total time
       Total time to travel from point A to point B including—Time to charging station   Time to get ready for charging
           Air temperature   Hardware Temperature   
           Total charging time   
       Preferred capabilities—Money
       Cost of available kWh during charging time   Charging station
           Cost reported per kWh   Parking   
           
       

     Known requirements in making EV charging suggestions include, for example:
     Preferred capabilities—Distance
       Total distance from point A—Charging location (detours for charging)—Point B   GeoSpatial—within a radius
           Search based on coordinates   
           Maps: Actual distance
           Point A—Point B   
           
       Required capabilities—Hardware matches
       Can I charge at the station, is my plug compatible?   Wattage
           Vehicle capabilities: xkWh—nkWh   Charging station max: ykWh
               x &lt;y &lt;n   
               
           Plug type
           CCS combo
               Wattage configuration   
               ChDemo   Vehicle: aType   Charging stations
               0. . . n number of types   
               
           
       

     Lesser known dimensions are used as a feedback mechanism for optimizing suggestions. If choice A is normally made, the system will provide preferred suggestions based on lesser known desires. Choice C may be scored 98% based on lesser used preferences in a highlighted list of matching desires, with choice B being 76%, and choice A being 23%. 
     Some lesser known dimensions include, for example:
     Weather   At the time of arrival, what is the projected weather
       Snow, rain, sunny   Temperature   Humidity   Air quality   
       Available kWh for charging at a charge point
       Compared to the advertised kWh sticker, what is the actual available charging capability at the charging station for vehicle at the proposed time   
       Time-of-day at a charge point
       Arrive location at 5:30 pm, for example   
       Risk factors
       Crime rate statistics   Financial factors   
       Amenities available
       Shade   Open area   Area with light   Restaurants   Shopping malls   Manicure locations   Massage spa, etc.   Charge-and-park   
       Penalty for parking over threshold
       [YES/NO] with cost/hr
 
Thus, the user preference is matched with multiple dimensions to reach point B, with considerations beyond distance and time of arrival.
   
       

       FIG.  1    is a flowchart illustrating one embodiment of the general method  10  of the present disclosure. A user first queries their navigation system or mobile application  50  via an associated user interface/display  52  that he or she would like to proceed from current location point A to desired destination location point B  12 . The first inquiry is whether or not point B can be reached without a charge  14 . If the answer is no, then the system or application finds the best charging location(s) considering the location and destination  16 . If the answer is yes, then a charge is not immediately needed and the inquiry becomes, upon arrival at point B, will there be enough charge in the vehicle to reach the next available charging point  18 . If the answer is no, then the system or application again finds the best charging location(s) considering the location and destination  16 . If the answer is yes, then the trip proceeds from point A to point B without charging intervention. 
     The following is a use case describing the solution of the present disclosure:
     The question—I am traveling from point A to point B   Matching vehicle and charging point specifications (compatibility for optimal service)
       Type of charger, type of plug available   Type of service—DC fast charge, AC slow charge   Type of power—kWh, the strength of charge upper limit (kWh available at station, kWh receivable by vehicle in current state)   
       Realtime   

     Availability of service, is it booked, is it bookable, does it have/share the level of service when load-balancing for multiple vehicles charging
     Predictive analytics on the charger by charger with geo position
       Historical availability, when slots have been available   Historical reliability, level of service in relation to time, weather conditions   Load balancing, level of charge available when multiple vehicles are charging   
       Type of location
       Where is the charger located locally (lit, open, easily available in relation to service, bathroom, etc.)   State of the machine (vehicle)   State of charge current kWh   The temperature of the battery (readiness to charge)   
       Response to the question—I am traveling from point A to point B   Answer to the query regarding travel to point B, machine to machine will be optimized for time-distance based on the knowledge available from map, service, and vehicle/profile   The answer will be based on variations on Estimated Time to Arrival (ETA) based on optimal dimensions understood by the query   At optimal answer to ETA, the options delivered will be weighted results with distribution of service according to time to distance
       Time is dominant weight (time is defined as time to arrival, and therefore delivering power availability (charging speed) uncoupled by cost unless cost is defined by the user as a dominant factor   Distance is in relation to time but is not dominant in weighted response   Availability is factored into time, predicted availability determined by historical insight, and therefore makes up the dominant weight   Price is factored in with availability prediction, cost is given importance by the User   
       Shortest way/time spent on road/time spent on charger, fastest way in speed/time/time spent on a charger (no care for milage)   How do I want to distribute my resources to get from point A to point B based on the importance of each dimension?   User preferences surfaced up to for feedback (refinement of the parameters to enhance the answer):   Shorter time?
       Time spent charging (distributed on each charging location combined or optimized to limit stops at charging location)   Time spent on the road   Time to arrival   Availability of service, is the charger available to me?   Higher in charging level at the point of arrival, shorter time spent at charger?   Shorter in distance?   Can I travel a shorter distance?   Is the speed at which I travel giving me optimal discharge for distance traveled?   Can I get there with the best convenience?   Convenience is related to the availability of utilities, crime rate, food, shelter, connectivity, etc.   Cost of service   Impact of cost on time spent using service   Impact of cost on quality of service, location in relation to time   Impact of cost in relation to convenience.   
       Scoring—based on desires—vector space model utilized   

     The result is a set of charging locations. These charging locations are found by filtering charging locations based on required capabilities and sorted by the scoring of the sum of weighing known optional dimensions capabilities distance to desired optional capabilities and later scored by lesser known dimensions based on user choices. 
     So, a list of charging stations matching required attributes, Ri, is sorted by shortest time and distance. Required attributes collected from the vehicle include hardware requirements and distance to charge with remaining charge (the “island” case), for example. Preferred attributes, Pi, collected from the user or otherwise include amenities, risk factors, weather, time of day, kWh plus cost, parking penalties, etc. Charging point attributes, C-Pi, and user attributes , W-Pi, and weights collected from user input are also considered. 
     Thus, a vector space model is applied and a charging point scoring calculation is performed, providing a list of charging stations matching required attributes, sorted by the scoring determined by the user&#39;s desires and distance to actual values. The preferred attribute of a charging point matching a user&#39;s desired attribute is C-Pi*W-Pi. The score of a charging point is the sum of the weighted attributes of the charging point matching the user&#39;s desired attribute. 
     The following provides an example of the metadata utilized: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                  { 
               
               
                   
                    “resource”: { 
               
               
                   
                      “spatial-position” : “Coordinate”, 
               
               
                   
                      “specifications”: { 
               
               
                   
                        “capacity”: “Description of capacity”, 
               
               
                   
                        “compatibility”: { 
               
               
                   
                          “spefic-attribute”: { 
               
               
                   
                           “name”: “ ”, 
               
               
                   
                           “description”: “ ” 
               
               
                   
                          }, 
               
               
                   
                          “another-spefic-attribute”: { 
               
               
                   
                           “name”: “ ”, 
               
               
                   
                           “description”: “ ” 
               
               
                   
                          }, 
               
               
                   
                          “....” :“....” 
               
               
                   
                        } 
               
               
                   
                      } 
               
               
                   
                    } , 
               
               
                   
                   “demand”: Resource { 
               
               
                   
                     { 
               
               
                   
                       “spatial-position” : “Coordinate”, 
               
               
                   
                       “specifications”: { 
               
               
                   
                         “capacity”: “Description of capacity”, 
               
               
                   
                         “compatibility”: { 
               
               
                   
                          “spefic-attribute”: { 
               
               
                   
                           “name”: “ ”, 
               
               
                   
                           “description”: “ ” 
               
               
                   
                                “required”: true|false, 
               
               
                   
                                “computational”: 
               
               
                   
                 true|false, 
               
               
                   
                           }, 
               
               
                   
                           “another-spefic-attribute”: { 
               
               
                   
                            “name”: “ ”, 
               
               
                   
                            “description”: “ ” 
               
               
                   
                                 “required”: true|false 
               
               
                   
                           }, 
               
               
                   
                                “computational-attribute”: { 
               
               
                   
                            “name”: “charging-time”, 
               
               
                   
                            “description”: “ ” 
               
               
                   
                                 “required”: true|false, 
               
               
                   
                                 “computational”: true 
               
               
                   
                                 “compute-function”: { 
               
               
                   
                                  function compute 
               
               
                   
                 (resource.specx, demand.specy) { 
               
               
                   
                              .... 
               
               
                   
                             } 
               
               
                   
                                 } 
               
               
                   
                           }, 
               
               
                   
                               “computational-attribute-2”: { 
               
               
                   
                            “name”: “ ”, 
               
               
                   
                            “description”: “ ” 
               
               
                   
                                “required”: true|false, 
               
               
                   
                                “computational”: true 
               
               
                   
                                “compute-function”: { 
               
               
                   
                                 “stored- 
               
               
                   
                 function”: “functionx(resource, demand)” 
               
               
                   
                                } 
               
               
                   
                           }, 
               
               
                   
                           “....” :“....” 
               
               
                   
                         } 
               
               
                   
                       } 
               
               
                   
                     } , 
               
               
                   
                   } 
               
               
                   
                 } 
               
               
                   
                   
               
            
           
         
       
     
     The steps utilized are summarized as follows: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Step 
                 Actor 
                 Description 
                 Comment 
               
               
                   
               
             
            
               
                 0-a 
                 System 
                 Knows required attributes 
                 R I   
               
               
                 0-b 
                 System 
                 Knows preferred attributes 
                 P I   
               
               
                 0-c 
                 System 
                 Knows charging point attributes 
                 C-P I   
               
               
                 1 
                 Customer 
                 Selects a desired location to 
               
               
                   
                   
                 travel to using an interface 
               
               
                 2 
                 System 
                 List of charging stations 
               
               
                   
                   
                 matching required attributes R I   
               
               
                   
                   
                 Sorted by shortest time and 
               
               
                   
                   
                 distance using an interface 
               
               
                 3 
                 System 
                 Displays the list of preferred 
               
               
                   
                   
                 attributes defined at 0-c using 
               
               
                   
                   
                 an interface 
               
               
                 4 
                 Customer 
                 Provides desired attributes and 
                 W-P I   
               
               
                   
                   
                 their weights from a user 
               
               
                   
                   
                 interface using an interface 
               
               
                 5 
                 System 
                 Calculates scoring of each 
                 See Charging 
               
               
                   
                   
                 charging point based on 
                 point scoring 
               
               
                   
                   
                 customers desires 
                 calculation 
               
               
                 6 
                 System 
                 List of charging stations 
               
               
                   
                   
                 matching required attributes 
               
               
                   
                   
                 Sorted by the scoring 
               
               
                   
                   
                 determined by customer&#39;s 
               
               
                   
                   
                 desires distance to actual 
               
               
                   
                   
                 values using an interface 
               
               
                 7 
                 Customer 
                 Picks a charging station from a 
               
               
                   
                   
                 user interface to travel to using 
               
               
                   
                   
                 an interface 
               
               
                 8 
                 System 
                 Doesn&#39;t pick a location from 
               
               
                   
                   
                 the list, the solution chooses 
               
               
                   
                   
                 the highest scored item in 
               
               
                   
                   
                 the list presented 
               
               
                 9 
                 System 
                 Adds the point of interest from 
               
               
                   
                   
                 list into the journey 
               
               
                   
               
            
           
         
       
     
     It is to be recognized that, depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. It should be noted that the algorithms of the present disclosure may be implemented on an embedded processing system running a real time operating system (OS), which provides an assured degree of availability and low latency. As discussed below, processing in a cloud system may also be implemented if such availability and latency problems are addressed. 
       FIG.  2    is a network diagram of a cloud-based system  100  for implementing various cloud-based services of the present disclosure, where applicable. The cloud-based system  100  includes one or more cloud nodes (CNs)  102  communicatively coupled to the Internet  104  or the like. The cloud nodes  102  may be implemented as a server or other processing system  200  (as illustrated in  FIG.  3   ) or the like and can be geographically diverse from one another, such as located at various data centers around the country or globe. Further, the cloud-based system  100  can include one or more central authority (CA) nodes  106 , which similarly can be implemented as the server  200  and be connected to the CNs  102 . For illustration purposes, the cloud-based system  100  can connect to a regional office  110 , headquarters  120 , various individual&#39;s homes  130 , laptops/desktops  140 , and mobile devices  150 , each of which can be communicatively coupled to one of the CNs  102 . These locations  110 ,  120 , and  130 , and devices  140  and  150  are shown for illustrative purposes, and those skilled in the art will recognize there are various access scenarios to the cloud-based system  100 , all of which are contemplated herein. The devices  140  and  150  can be so-called road warriors, i.e., users off-site, on-the-road, etc. The cloud-based system  100  can be a private cloud, a public cloud, a combination of a private cloud and a public cloud (hybrid cloud), or the like. 
     Again, the cloud-based system  100  can provide any functionality through services, such as software-as-a-service (SaaS), platform-as-a-service, infrastructure-as-a-service, security-as-a-service, Virtual Network Functions (VNFs) in a Network Functions Virtualization (NFV) Infrastructure (NFVI), etc. to the locations  110 ,  120 , and  130  and devices  140  and  150 . Previously, the Information Technology (IT) deployment model included enterprise resources and applications stored within an enterprise network (i.e., physical devices), behind a firewall, accessible by employees on site or remote via Virtual Private Networks (VPNs), etc. The cloud-based system  100  is replacing the conventional deployment model. The cloud-based system  100  can be used to implement these services in the cloud without requiring the physical devices and management thereof by enterprise IT administrators. 
     Cloud computing systems and methods abstract away physical servers, storage, networking, etc., and instead offer these as on-demand and elastic resources. The National Institute of Standards and Technology (NIST) provides a concise and specific definition which states cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing differs from the classic client-server model by providing applications from a server that are executed and managed by a client&#39;s web browser or the like, with no installed client version of an application required. Centralization gives cloud service providers complete control over the versions of the browser-based and other applications provided to clients, which removes the need for version upgrades or license management on individual client computing devices. The phrase “software as a service” is sometimes used to describe application programs offered through cloud computing. A common shorthand for a provided cloud computing service (or even an aggregation of all existing cloud services) is “the cloud.” The cloud-based system  100  is illustrated herein as one example embodiment of a cloud-based system, and those of ordinary skill in the art will recognize the systems and methods described herein are not necessarily limited thereby. 
       FIG.  3    is a block diagram of a server or other processing system  200 , which may be used in the cloud-based system  100  ( FIG.  2   ), in other systems, or stand-alone, such as in the vehicle itself. For example, the CNs  102  ( FIG.  2   ) and the central authority nodes  106  ( FIG.  2   ) may be formed as one or more of the servers  200 . The server  200  may be a digital computer that, in terms of hardware architecture, generally includes a processor  202 , input/output (I/O) interfaces  204 , a network interface  206 , a data store  208 , and memory  210 . It should be appreciated by those of ordinary skill in the art that  FIG.  3    depicts the server or other processing system  200  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 202 ,  204 ,  206 ,  208 , and  210 ) are communicatively coupled via a local interface  212 . The local interface  212  may be, for example, but is not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  212  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  212  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  202  is a hardware device for executing software instructions. The processor  202  may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server  200 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the server  200  is in operation, the processor  202  is configured to execute software stored within the memory  210 , to communicate data to and from the memory  210 , and to generally control operations of the server  200  pursuant to the software instructions. The I/O interfaces  204  may be used to receive user input from and/or for providing system output to one or more devices or components. 
     The network interface  206  may be used to enable the server  200  to communicate on a network, such as the Internet  104  ( FIG.  2   ). The network interface  206  may include, for example, an Ethernet card or adapter (e.g., 10 BaseT, Fast Ethernet, Gigabit Ethernet, or 10 GbE) or a Wireless Local Area Network (WLAN) card or adapter (e.g., 802.11a/b/g/n/ac). The network interface  206  may include address, control, and/or data connections to enable appropriate communications on the network. A data store  208  may be used to store data. The data store  208  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  208  may incorporate electronic, magnetic, optical, and/or other types of storage media. In one example, the data store  208  may be located internal to the server  200 , such as, for example, an internal hard drive connected to the local interface  212  in the server  200 . Additionally, in another embodiment, the data store  208  may be located external to the server  200  such as, for example, an external hard drive connected to the I/O interfaces  204  (e.g., a SCSI or USB connection). In a further embodiment, the data store  208  may be connected to the server  200  through a network, such as, for example, a network-attached file server. 
     The memory  210  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory  210  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  210  may have a distributed architecture, where various components are situated remotely from one another but can be accessed by the processor  202 . The software in memory  210  may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory  210  includes a suitable operating system (O/S)  214  and one or more programs  216 . The operating system  214  essentially controls the execution of other computer programs, such as the one or more programs  216 , and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The one or more programs  216  may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein. 
     It will be appreciated that some embodiments described herein may include one or more generic or specialized processors (“one or more processors”) such as microprocessors; central processing units (CPUs); digital signal processors (DSPs); customized processors such as network processors (NPs) or network processing units (NPUs), graphics processing units (GPUs), or the like; field programmable gate arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more application-specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured or adapted to,” “logic configured or adapted to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments. 
     Moreover, some embodiments may include a non-transitory computer-readable medium having computer-readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc. each of which may include a processor to perform functions as described and claimed herein. Examples of such computer-readable mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, and the like. When stored in the non-transitory computer-readable medium, software can include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause a processor or the device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. as described herein for the various embodiments. 
       FIG.  4    is a block diagram of a user device  300 , which may be used in the cloud-based system  100  ( FIG.  2   ), as part of a network, or stand-alone. The user device  300  can be a vehicle, a smartphone, a tablet, a smartwatch, an Internet of Things (IoT) device, a laptop, a virtual reality (VR) headset, etc. The user device  300  can be a digital device that, in terms of hardware architecture, generally includes a processor  302 , I/O interfaces  304 , a radio  306 , a data store  308 , and memory  310 . It should be appreciated by those of ordinary skill in the art that  FIG.  4    depicts the user device  300  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 302 ,  304 ,  306 ,  308 , and  310 ) are communicatively coupled via a local interface  312 . The local interface  312  can be, for example, but is not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  312  can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  312  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  302  is a hardware device for executing software instructions. The processor  302  can be any custom made or commercially available processor, a CPU, an auxiliary processor among several processors associated with the user device  300 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the user device  300  is in operation, the processor  302  is configured to execute software stored within the memory  310 , to communicate data to and from the memory  310 , and to generally control operations of the user device  300  pursuant to the software instructions. In an embodiment, the processor  302  may include a mobile optimized processor such as optimized for power consumption and mobile applications. The I/O interfaces  304  can be used to receive user input from and/or for providing system output. User input can be provided via, for example, a keypad, a touch screen, a scroll ball, a scroll bar, buttons, a barcode scanner, and the like. System output can be provided via a display device such as a liquid crystal display (LCD), touch screen, and the like. 
     The radio  306  enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the radio  306 , including any protocols for wireless communication. The data store  308  may be used to store data. The data store  308  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  308  may incorporate electronic, magnetic, optical, and/or other types of storage media. 
     Again, the memory  310  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory  310  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  310  may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor  302 . The software in memory  310  can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of  FIG.  10   , the software in the memory  310  includes a suitable operating system  314  and programs  316 . The operating system  314  essentially controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The programs  316  may include various applications, add-ons, etc. configured to provide end user functionality with the user device  300 . For example, example programs  316  may include, but not limited to, a web browser, social networking applications, streaming media applications, games, mapping and location applications, electronic mail applications, financial applications, and the like. In a typical example, the end-user typically uses one or more of the programs  316  along with a network, such as the cloud-based system  100  ( FIG.  2   ). 
     Although the present disclosure is illustrated and described herein with reference to illustrative embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following non-limiting claims for all purposes.