Patent Publication Number: US-2023132714-A1

Title: System and method for enhanced virtual queuing

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     Priority is claimed in the application data sheet to the following patents or patent applications, the entire written description of each of which is expressly incorporated herein by reference in its entirety:
         Ser. No. 17/667,522   Ser. No. 17/677,034   Ser. No. 17/235,408   Ser. No. 16/836,798   Ser. No. 16/542,577   62/820,190   Ser. No. 17/572,620   Ser. No. 17/389,837   Ser. No. 16/985,093   Ser. No. 16/583,967   62/828,133   Ser. No. 16/523,501   Ser. No. 15/411,424       

    
    
     BACKGROUND OF THE INVENTION 
     Field of the Art 
     The disclosure relates to queuing, specifically to the field of cloud-implemented automated callback systems. 
     Discussion of the State of the Art 
     Queues have been around for at least 185 years. With urbanization and population growth increasing the length of most queues by orders of magnitude in some situations. The design of the queue has changed ever-so-slightly, zig-zagging the line for example, but the basic queue remains relatively unchanged. That was up until virtual queuing came around in the form of paper tickets and more recently electronic pagers. However, these new modes require a queued person to remain within earshot of an announcement or within visual range of a monitor, in the case of paper tickets. In the case of pagers, a queued person is still limited in physical space by the range of the pager. Newer virtual queuing systems have been devised to use a person&#39;s mobile device, but still haven&#39;t really added much to queuing. These current solutions fail to efficiently facilitate or even address at all the complexity of multiple queues, punctuality concerns and no-shows, and simply does not take advantage of modern-day advantages such as “Big Data.” 
     What is needed is a system and method for virtual queuing that overcomes the limitations of the prior art as noted above by organizing and motivating multiple persons between multiple queues and taking full advantage of the breadth of data available to make predictions and organize queues. 
     SUMMARY OF THE INVENTION 
     Accordingly, the inventor has conceived and reduced to practice, a system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events. 
     According to a first preferred embodiment, a system for enhanced virtual queuing is disclosed, comprising: a task blending service comprising at least a processor, a memory, and a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the processor to: receive a plurality of data relating to the historical throughput of a queue; model future iterations of the queue using the plurality of data; determine times of low queue throughput; reallocate computational resources used for queue simulations during times of low queue throughput; use the reallocated computational resources for simulating new queue configurations for the duration of the low queue throughput; analyze the new queue configuration simulations for an optimal configuration, wherein the optimal configuration is the simulation with the least wait time; and output the difference between the current queue configuration and the optimal queue simulation as a set of recommendations. 
     According to a second preferred embodiment, a method for enhanced virtual queuing is disclosed, comprising the steps of: receiving a plurality of data relating to the historical throughput of a queue; modelling future iterations of the queue using the plurality of data; determining times of low queue throughput; reallocating computational resources used for queue simulations during times of low queue throughput; using the reallocated computational resources for simulating new queue configurations for the duration of the low queue throughput; analyzing the new queue configuration simulations for an optimal configuration, wherein the optimal configuration is the simulation with the least wait time; and outputting the difference between the current queue configuration and the optimal queue simulation as a set of recommendations. 
     According to various aspects; wherein the task blending service predicts future queue iterations with machine learning; the system further comprising an accumulation service comprising at least a processor, a memory, and a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the processor to: receive a request to join a virtual queue from two or more persons forming a group; accumulate positions in the queue totaling the number of persons in the group; send confirmation of the request to the group; send periodic update notifications to the group based on a notification escalation plan, wherein the notification escalation plan comprises a rules-based multimodality means of communicating with the group; receive a plurality of check-in notifications from an entity indicating the group has begun to check-in, or indicating how many persons in the group have already checked-in, or indicating the group has finished checking-in; and remove the group from the virtual queue; wherein the multimodality means of communicating comprises internet-based communications, satellite communications, public telephone networks, mobile networks, Wi-Fi, Bluetooth, LoRa, public address systems, and near-field communications; wherein the request is selected from the group consisting of a request to join a queue, a request to leave a queue, a request to transfer to a different queue, a request for the current wait time of a queue, a request for additional time, a request to schedule a position in a queue for a later time, and a request to change places in a queue; further comprising a call blending feature comprising at least a processor, a memory, and a third plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the processor to: dynamically shift call center agents to place outbound marketing calls during times of low queue throughput; wherein the optimal configuration is the simulation with the least distance a queued person must travel; wherein the optimal configuration is the simulation with wherein the queue occupies the least amount of space while maintaining at least 6 feet of separation between queued persons; wherein the optimal configuration is the simulation with the least amount of cost to an entity hosting a physical queue associated with the virtual queue; wherein the optimal configuration is the simulation with the least cost to the queued persons. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way. 
         FIG.  1    is a block diagram illustrating an exemplary system architecture for operating a callback cloud, according to one aspect. 
         FIG.  2    is a block diagram illustrating an exemplary system architecture for a callback cloud operating over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  3    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including a calendar server, over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  4    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including a brand interface server, over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  5    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including a brand interface server and intent analyzer, over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  6    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including a privacy server, over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  7    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including a bot server, over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  8    is a block diagram illustrating an exemplary system architecture for a callback cloud operating including an operations analyzer over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  9    is a block diagram illustrating an exemplary system architecture for a callback cloud including a brand interface server, an intent analyzer, and a broker server, operating over a public switched telephone network and internet, to a variety of other brand devices and services, according to an embodiment. 
         FIG.  10    is a diagram illustrating trust circles of levels of privacy for a user of a callback cloud, according to an aspect. 
         FIG.  11    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, according to an embodiment. 
         FIG.  12    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a calendar server, according to an embodiment. 
         FIG.  13    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including gathering of environmental context data of users, according to an embodiment. 
         FIG.  14    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a brand interface server and intent analyzer, according to an embodiment. 
         FIG.  15    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a privacy server, according to an embodiment. 
         FIG.  16    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a bot server, according to an embodiment. 
         FIG.  17    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including an operations analyzer, according to an embodiment. 
         FIG.  18    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a brand interface server, intent analyzer, and broker server, according to an embodiment. 
         FIG.  19    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, according to an embodiment. 
         FIG.  20    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a calendar server, according to an embodiment. 
         FIG.  21    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server, according to an embodiment. 
         FIG.  22    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server and intent analyzer, according to an embodiment. 
         FIG.  23    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a privacy server, according to an embodiment. 
         FIG.  24    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a bot server, according to an embodiment. 
         FIG.  25    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including an operations analyzer, according to an embodiment. 
         FIG.  26    is a block diagram illustrating an exemplary hardware architecture of a computing device. 
         FIG.  27    is a block diagram illustrating an exemplary logical architecture for a client device. 
         FIG.  28    is a block diagram showing an exemplary architectural arrangement of clients, servers, and external services. 
         FIG.  29    is another block diagram illustrating an exemplary hardware architecture of a computing device. 
         FIG.  30    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server, intent analyzer, and broker server, according to an embodiment. 
         FIG.  31    is a block diagram illustrating an exemplary system for a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  32    is a block diagram illustrating an exemplary system architecture and the possible communication means for a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  33    is a block diagram illustrating an exemplary system architecture for a queue service, according to an embodiment. 
         FIG.  34    is a block diagram showing an exemplary use of a cloud-based queue service, according to one aspect. 
         FIG.  35    is a method diagram illustrating the use of a cloud-based virtual queuing platform with an end-device, according to an embodiment. 
         FIG.  36    is a method diagram illustrating another use of a cloud-based virtual queuing platform with an end-device, according to an embodiment. 
         FIG.  37    is a block diagram illustrating signage used to initiate bi-directional communication between a cloud-based virtual queuing platform and an end-device, according to one aspect. 
         FIG.  38    is a block diagram illustrating one aspect of an exemplary mobile application used in bi-directional communication between a cloud-based virtual queuing platform and an end-device, according to one aspect. 
         FIG.  39    is a block diagram illustrating another aspect of an exemplary mobile application used in bi-directional communication between a cloud-based virtual queuing platform and an end-device, according to one aspect. 
         FIG.  40    is a block diagram illustrating a graph output from an analysis module, according to one aspect. 
         FIG.  41    is a block diagram illustrating another graph output from an analysis module, according to one aspect. 
         FIG.  42    is a flow diagram illustrating a web-based GPS aspect of a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  43    is a flow diagram illustrating another web-based GPS aspect of a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  44    is a table diagram showing an exemplary and simplified rules-based notification escalation plan, according to one aspect. 
         FIG.  45    is a table diagram showing an exemplary and simplified rules-based notification escalation plan that further uses location data, according to one aspect. 
         FIG.  46    is a message flow diagram illustrating the exchange of messages and data between components of a cloud-based virtual queuing platform for sequential event queue management, according to an embodiment. 
         FIG.  47    is a flow diagram illustrating a load-balancing aspect of a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  48    is a method diagram illustrating a one-time password aspect in a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  49    is a block diagram illustrating an exemplary system architecture for a queue manager with task blending and accumulation, according to an embodiment. 
         FIG.  50    is a block diagram illustrating a graph representing task blending opportunities based on queue throughput, according to one aspect. 
         FIG.  51    is a block diagram illustrating four exemplary queue models used for queue simulations, according to one aspect. 
         FIG.  52    is a method diagram illustrating task blending in a cloud-based virtual queuing platform, according to an embodiment. 
         FIG.  53    is a method diagram illustrating an accumulation service used in a cloud-based virtual queuing platform, according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The inventor has conceived, and reduced to practice, a system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of lines for sequential events. 
     One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements. 
     Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical. 
     A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. 
     When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. 
     The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself. 
     Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art. 
     Definitions 
     “Callback” as used herein refers to an instance of an individual being contacted after their initial contact was unsuccessful. For instance, if a first user calls a second user on a telephone, but the second user does not receive their call for one of numerous reasons including turning off their phone or simply not picking up, the second user may then place a callback to the first user once they realize they missed their call. This callback concept applies equally to many forms of interaction that need not be restricted to telephone calls, for example including (but not limited to) voice calls over a telephone line, video calls over a network connection, or live text-based chat such as web chat or short message service (SMS) texting, email, and other messaging applications (e.g., WhatsApp, etc.). While a callback (and various associated components, methods, and operations taught herein) may also be used with an email communication despite the inherently asynchronous nature of email (participants may read and reply to emails at any time, and need not be interacting at the same time or while other participants are online or available), the preferred usage as taught herein refers to synchronous communication (that is, communication where participants are interacting at the same time, as with a phone call or chat conversation). 
     “Callback object” as used herein means a data object representing callback data, such as the identities and call information for a first and second user, the parameters for a callback including what time it shall be performed, and any other relevant data for a callback to be completed based on the data held by the callback object. 
     “Latency period” as used herein refers to the period of time between when a Callback Object is created and the desired Callback is initiated, for example, if a callback object is created and scheduled for a time five hours from the creation of the object, and the callback initiates on-time in five hours, the latency period is equal to the five hours between the callback object creation and the callback initiation. 
     “Brand” as used herein means a possible third-party service or device that may hold a specific identity, such as a specific MAC address, IP address, a username or secret key which can be sent to a cloud callback system for identification, or other manner of identifiable device or service that may connect with the system. Connected systems or services may include a Private Branch Exchange (“PBX”), call router, chat server which may include text or voice chat data, a Customer Relationship Management (“CRM”) server, an Automatic Call Distributor (“ACD”), or a Session Initiation Protocol (“SIP”) server. 
     Conceptual Architecture 
       FIG.  1    is a block diagram of a preferred embodiment of the invention, illustrating an exemplary architecture of a system  100  for providing a callback cloud service. According to the embodiment, callback cloud  101  may receive requests  140  via a plurality of communications networks such as a public switched telephone network (PSTN)  103  or the Internet  102 . These requests may comprise a variety of communication and interaction types, for example including (but not limited to) voice calls over a telephone line, video calls over a network connection, or live text-based chat such as web chat or short message service (SMS) texting via PSTN  103 . Such communications networks may be connected to a plurality of consumer endpoints  110  and enterprise endpoints  120  as illustrated, according to the particular architecture of communication network involved. Exemplary consumer endpoints  110  may include, but are not limited to, traditional telephones  111 , cellular telephones  112 , mobile tablet computing devices  113 , laptop computers  114 , or desktop personal computers (PC)  115 . Such devices may be connected to respective communications networks via a variety of means, which may include telephone dialers, VOIP telecommunications services, web browser applications, SMS text messaging services, or other telephony or data communications services. It will be appreciated by one having ordinary skill in the art that such means of communication are exemplary, and many alternative means are possible and becoming possible in the art, any of which may be utilized as an element of system  100  according to the invention. 
     A PSTN  103  or the Internet  102  (and it should be noted that not all alternate connections are shown for the sake of simplicity, for example a desktop PC  126  may communicate via the Internet  102 ) may be further connected to a plurality of enterprise endpoints  120 , which may comprise cellular telephones  121 , telephony switch  122 , desktop environment  125 , internal Local Area Network (LAN) or Wide-Area Network (WAN)  130 , and mobile devices such as tablet computing device  128 . As illustrated, desktop environment  125  may include both a telephone  127  and a desktop computer  126 , which may be used as a network bridge to connect a telephony switch  122  to an internal LAN or WAN  130 , such that additional mobile devices such as tablet PC  128  may utilize switch  122  to communicate with PSTN  102 . Telephone  127  may be connected to switch  122  or it may be connected directly to PSTN  102 . It will be appreciated that the illustrated arrangement is exemplary, and a variety of arrangements that may comprise additional devices known in the art are possible, according to the invention. 
     Callback cloud  101  may respond to requests  140  received from communications networks with callbacks appropriate to the technology utilized by such networks, such as data or Voice over Internet Protocol (VOIP) callbacks  145 ,  147  sent to Internet  102 , or time-division multiplexing (TDM) such as is commonly used in cellular telephony networks such as the Global System for Mobile Communications (GSM) cellular network commonly used worldwide, or VOIP callbacks to PSTN  103 . Data callbacks  147  may be performed over a variety of Internet-enabled communications technologies, such as via e-mail messages, application pop-ups, or Internet Relay Chat (IRC) conversations, and it will be appreciated by one having ordinary skill in the art that a wide variety of such communications technologies are available and may be utilized according to the invention. VOIP callbacks may be made using either, or both, traditional telephony networks such as PSTN  103  or over VOIP networks such as Internet  102 , due to the flexibility to the technology involved and the design of such networks. It will be appreciated that such callback methods are exemplary, and that callbacks may be tailored to available communications technologies according to the invention. 
     Additionally, callback cloud  101  may receive estimated wait time (EWT) information from an enterprise  120  such as a contact center. This information may be used to estimate the wait time for a caller before reaching an agent (or other destination, such as an automated billing system), and determine whether to offer a callback proactively before the customer has waited for long. EWT information may also be used to select options for a callback being offered, for example to determine availability windows where a customer&#39;s callback is most likely to be fulfilled (based on anticipated agent availability at that time), or to offer the customer a callback from another department or location that may have different availability. This enables more detailed and relevant callback offerings by incorporating live performance data from an enterprise, and improves customer satisfaction by saving additional time with preselected recommendations and proactively-offered callbacks. 
       FIG.  2    is a block diagram illustrating an exemplary system architecture for a callback cloud operating over a public switched telephone network and the Internet, and connecting to a variety of other brand devices and services, according to an embodiment. A collection of user brands  210  may be present either singly or in some combination, possibly including a Public Branch Exchange (“PBX”)  211 , a Session Initiation Protocol (“SIP”) server  212 , a Customer Relationship Management (“CRM”) server  213 , a call router  214 , or a chat server  215 , or some combination of these brands. These brands  210  may communicate over a combination of, or only one of, a Public Switched Telephone Network (“PSTN”)  103 , and the Internet  102 , to communicate with other devices including a callback cloud  220 , a company phone  121 , or a personal cellular phone  112 . A SIP server  212  is responsible for initiating, maintaining, and terminating sessions of voice, video, and text or other messaging protocols, services, and applications, including handling of PBX  211  phone sessions, CRM server  213  user sessions, and calls forwarded via a call router  214 , all of which may be used by a business to facilitate diverse communications requests from a user or users, reachable by phone  121 ,  112  over either PSTN  103  or the Internet  102 . A chat server  215  may be responsible for maintaining one or both of text messaging with a user, and automated voice systems involving technologies such as an Automated Call Distributor (“ACD”), forwarding relevant data to a call router  214  and CRM server  213  for further processing, and a SIP server  212  for generating communications sessions not run over the PSTN  103 . Various systems may also be used to monitor their respective interactions (for example, chat session by a chat server  215  or phone calls by an ACD or SIP server  212 ), to track agent and resource availability for producing EWT estimations. 
     When a user calls from a mobile device  112  or uses some communication application such as (for example, including but not limited to) SKYPE™ or instant messaging, which may also be available on a laptop or other network endpoint other than a cellular phone  112 , they may be forwarded to brands  210  operated by a business in the manner described herein. For example, a cellular phone call my be placed over PSTN  103  before being handled by a call router  214  and generating a session with a SIP server  212 , the SIP server creating a session with a callback cloud  220  with a profile manager  221  if the call cannot be completed, resulting in a callback being required. A profile manager  221  manages the storage, retrieval, and updating of user profiles, including global and local user profiles. The profile manager  221 , which may be located in a callback cloud  220  receives initial requests to connect to callback cloud  220 , and forwards relevant user profile information to a callback manager  223 , which may further request environmental context data from an environment analyzer  222 . Environmental context data may include (for example, and not limited to) recorded information about when a callback requester or callback recipient may be suspected to be driving or commuting from work, for example, and may be parsed from online profiles or online textual data, using an environment analyzer  222 . 
     A callback manager  223  centrally manages all callback data, creating a callback programming object which may be used to manage the data for a particular callback, and communicates with an interaction manager  224  which handles requests to make calls and bridge calls, which go out to a media server  225  which actually makes the calls as requested. For example, interaction manager  224  may receive a call from a callback requester, retrieve callback parameters for that callback requester from the callback manager  223 , and cause the media server  225  to make a call to a callback recipient while the callback requester is still on the line, thus connecting the two parties. After the call is connected, the callback programming object used to make the connection may be deleted. The interaction manager  224  may subsequently provide changed callback parameters to the callback manager  223  for use or storage. In this way, the media server  225  may be altered in the manner in which it makes and bridges calls when directed, but the callback manager  223  does not need to adjust itself, due to going through an intermediary component, the interaction manager  224 , as an interface between the two. A media server  225 , when directed, may place calls and send messages, emails, or connect voice over IP (“VoIP”) calls and video calls, to users over a PSTN  103  or the Internet  102 . Callback manager  223  may work with a user&#39;s profile as managed by a profile manager  221 , with environmental context from an environment analyzer  222  as well as (if provided) EWT information for any callback recipients (for example, contact center agents with the appropriate skills to address the callback requestor&#39;s needs, or online tech support agents to respond to chat requests), to determine an appropriate callback time for the two users (a callback requestor and a callback recipient), interfacing with an interaction manager  224  to physically place and bridge the calls with a media server  225 . In this way, a user may communicate with another user on a PBX system  211 , or with automated services hosted on a chat server  215 , and if they do not successfully place their call or need to be called back by a system, a callback cloud  220  may find an optimal time to bridge a call between the callback requestor and callback recipient, as necessary. 
       FIG.  3    is a block diagram illustrating an exemplary system architecture for a callback cloud including a calendar server operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  310  are present, including PBX system  311 , a SIP server  312 , a CRM server  313 , a call router  314 , and a chat server  315 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, callback cloud  320  contains multiple components, including a calendar server  321 , profile manager  322 , environment analyzer  323 , callback manager  324 , interaction manager  325 , and media server  326 , which similarly to user brands  310  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     A calendar server  321 , according to the embodiment, is a server which may store and retrieve, either locally or from internet-enabled services associated with a user, calendars which hold data on what times a user may be available or busy (or some other status that may indicate other special conditions, such as to allow only calls from certain sources) for a callback to take place. A calendar server  321  connects to the internet  102 , and to a profile manager  322 , to determine the times a callback requestor and callback recipient may both be available. 
       FIG.  4    is a block diagram illustrating an exemplary system architecture for a callback cloud including a brand interface server, operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  410  are present, including PBX system  411 , a SIP server  412 , a CRM server  413 , a call router  414 , and a chat server  415 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, callback cloud  420  contains multiple components, including a profile manager  421 , environment analyzer  422 , callback manager  423 , interaction manager  424 , and media server  425 , which similarly to user brands  410  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     Present in this embodiment is a brand interface server  430 , which may expose the identity of, and any relevant API&#39;s or functionality for, any of a plurality of connected brands  410 , to elements in a callback cloud  420 . In this way, elements of a callback cloud  420  may be able to connect to, and interact more directly with, systems and applications operating in a business&#39; infrastructure such as a SIP server  412 , which may be interfaced with a profile manager  421  to determine the exact nature of a user&#39;s profiles, sessions, and interactions in the system for added precision regarding their possible availability and most importantly, their identity. 
       FIG.  5    is a block diagram illustrating an exemplary system architecture for a callback cloud including a brand interface server and intent analyzer, operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  510  are present, including PBX system  511 , a SIP server  512 , a CRM server  513 , a call router  514 , and a chat server  515 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. Further shown is a callback cloud  520  contains multiple components, including a profile manager  521 , environment analyzer  522 , callback manager  523 , interaction manager  524 , and media server  525 , which similarly to user brands  510  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     Present in this embodiment is a brand interface server  530 , which may expose the identity of, and any relevant API&#39;s or functionality for, any of a plurality of connected brands  510 , to elements in a callback cloud  520 . In this way, elements of a callback cloud  520  may be able to connect to, and interact more directly with, systems and applications operating in a business&#39; infrastructure such as a SIP server  512 , which may be interfaced with a profile manager  521  to determine the exact nature of a user&#39;s profiles, sessions, and interactions in the system for added precision regarding their possible availability and most importantly, their identity. Also present in this embodiment is an intent analyzer  540 , which analyzes spoken words or typed messages from a user that initiated the callback request, to determine their intent for a callback. For example, their intent may be to have an hour-long meeting, which may factor into the decision by a callback cloud  520  to place a call shortly before one or both users may be required to start commuting to or from their workplace. Intent analysis may utilize any combination of text analytics, speech-to-text transcription, audio analysis, facial recognition, expression analysis, posture analysis, or other analysis techniques, and the particular technique or combination of techniques may vary according to such factors as the device type or interaction type (for example, speech-to-text may be used for a voice-only call, while face/expression/posture analysis may be appropriate for a video call), or according to preconfigured settings (that may be global, enterprise-specific, user-specific, device-specific, or any other defined scope). 
       FIG.  6    is a block diagram illustrating an exemplary system architecture for a callback cloud including a privacy server, operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  610  are present, including PBX system  611 , a SIP server  612 , a CRM server  613 , a call router  614 , and a chat server  615 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, a callback cloud  620  contains multiple components, including a profile manager  622 , environment analyzer  623 , callback manager  624 , interaction manager  625 , and media server  626 , which similarly to user brands  610  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     In this embodiment, a privacy server  621  may connect to the internet  102 , and to a profile manager  622  as well as a callback manager  624 , and allows for callback requestors to first be validated using trust-circles to determine if they are a trusted user. A trusted user may be defined using a variety of criteria (that may vary according to the user, interaction, device, enterprise, or other context), and may for example comprise a determination of whether the callback requestor is a friend or family member, or is using a trusted brand such as a piece of equipment from the same company that the callback recipient works at, or if the callback requestor is untrusted or is contacting unknown recipients, to determine if a callback request is permitted based on user settings. Further, a privacy server  621  may encrypt one or both of incoming and outgoing data from a callback manager  624  in such a way as to ensure that, for example, a callback recipient might not know who requested the callback, or their profile may not be visible to the recipient, or vice versa, and other privacy options may also be enabled as needed by a corporation. Encryption may utilize public or private keys, or may utilize perfect forward secrecy (such that even the enterprise routing the call cannot decrypt it), or other encryption schema or combinations thereof that may provide varying features or degrees of privacy, security, or anonymity (for example, one enterprise may permit anonymous callbacks while another may require a user to identify themselves and may optionally verify this identification). 
       FIG.  7    is a block diagram illustrating an exemplary system architecture for a callback cloud including a bot server, operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  710  are present, including PBX system  711 , a SIP server  712 , a CRM server  713 , a call router  714 , and a chat server  715 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, a callback cloud  720  contains multiple components, including a profile manager  721 , environment analyzer  722 , callback manager  723 , interaction manager  725 , and media server  726 , which similarly to user brands  710  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     In the present embodiment, a bot server  724  also is present in a callback cloud  720 , which allows for communication with a callback requestor. Bot server  724  allows a user to specify, through any available data type such as (including, but not limited to) SMS texting, email, or audio data, any desired parameters for the callback they would like to request. This is similar to an ACD system used by individual call-centers, but exists as a separate server  724  in a cloud service  720  which may then be configured as-needed by a hosting company, and behaves akin to an automated secretary, taking user information down to specify a callback at a later time from the callback recipient. 
       FIG.  8    is a block diagram illustrating an exemplary system architecture for a callback cloud including an operations analyzer operating over a public switched telephone network and the Internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  810  are present, including PBX system  811 , a SIP server  812 , a CRM server  813 , a call router  814 , and a chat server  815 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, a callback cloud  820  contains multiple components, including a profile manager  821 , environment analyzer  822 , callback manager  823 , interaction manager  825 , and media server  826 , which similarly to user brands  810  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . 
     In this embodiment, an operations analyzer  824  is present, which may determine a particular channel to be used to reach a callback recipient and callback requestor, for example (and not limited to), VoIP services such as SKYPE™ or DISCORD™, a PSTN phone connection, any particular phone number or user accounts to connect using, or other service, to determine the optimal method with which to reach a user during a callback. An operations analyzer  824  may also analyze and determine the points of failure in a callback cloud  820 , if necessary, for example if a callback attempt fails to connect operations analyzer  824  may bridge a callback requestor and recipient using an alternate communication channel to complete the callback at the scheduled time. 
       FIG.  9    is a block diagram illustrating an exemplary system architecture for a callback cloud including a brand interface server, an intent analyzer, and a broker server, operating over a public switched telephone network and internet, and connected to a variety of other brand devices and services, according to an embodiment. According to this embodiment, many user brands  910  are present, including PBX system  911 , a SIP server  912 , a CRM server  913 , a call router  914 , and a chat server  915 , which may be connected variously to each other as shown, and connected to a PSTN  103  and the Internet  102 , which further connect to a cellular phone  112  and a landline  121  or other phone that may not have internet access. As further shown, a callback cloud  920  contains multiple components, including a profile manager  921 , environment analyzer  922 , callback manager  923 , interaction manager  924 , and media server  925 , which similarly to user brands  910  may be interconnected in various ways as depicted in the diagram, and connected to either a PSTN  103  or the internet  102 . Also present are a plurality of network endpoints  960 ,  970 , connected to either or both of the internet  102  and a PSTN  103 , such network endpoints representing contact points other than a landline  121  or cell phone  112 , including laptops, desktops, tablet computers, or other communication devices. 
     Present in this embodiment is a brand interface server  930 , which may expose the identity of, and any relevant API&#39;s or functionality for, any of a plurality of connected brands  910 , to an intent analyzer  940 . In this way, elements of a callback cloud  920  may be able to connect to, and interact more directly with, systems and applications operating in a business&#39; infrastructure such as a SIP server  912 , which may be interfaced with a profile manager  921  to determine the exact nature of a user&#39;s profiles, sessions, and interactions in the system for added precision regarding their possible availability and most importantly, their identity. An intent analyzer  940  may analyze spoken words or typed messages from a user that initiated the callback request, to determine their intent for a callback, as well as forward data received from a brand interface server. For example, their intent may be to have an hour-long meeting, which may factor into the decision by a callback cloud  920  to place a call shortly before one or both users may be required to start commuting to or from their workplace. An intent analyzer  940  may forward all data through a broker server  950  which may allocate specific actions and responses to take between third-party brands  910  and callback cloud  920  components, as needed, as well as forward all data from the exposed and interfaced elements with the callback cloud  920 . 
       FIG.  10    is a diagram illustrating trust circles of levels of privacy for a user of a callback cloud, according to an aspect. These trust circles are data constructs enforced by a privacy server  621  which are determined with a profile manager  622 , which indicate the level of trust that callers may possess, and therefore the system&#39;s ability to schedule a callback with the caller and the recipient. A caller who calls from a recognized brand  1010 , for example a company&#39;s phone forwarded through their PBX  611 , may be recognized as having the highest level of trust, due to coming from a recognized source within the same organization. Family  1020  may (for example) be the second highest level of trust, allowing for just as many privileges with callbacks, or perhaps restricting callback requests to only certain hours, to prevent users from being disrupted during certain work hours. A callback recipient&#39;s friends  1030  may occupy a level of trust lower than that of family, representing users less-trusted than family  1020  callers, and may yet have more restricted access to making callback requests for a user, and a continuing, descending hierarchy may be used to model additional levels of trust. For example, additional trust levels may include (but are not limited to) social media  1040  recognized users, colleagues  1050  which may represent individuals only loosely affiliated with a potential callback recipient, and untrusted  1060 , representing users who are known to the system and deemed banned or untrustworthy, having the lowest ability to request an automated callback connection with a user. A further level of trust may exist, outside of the trust-circle paradigm, representing unknown contacts  1070 , which, depending on the settings for an individual user or an organization using a callback cloud system  620 , may be unable to request callbacks, or may only be able to request callbacks at certain restricted hours until they are set to a higher level of trust in the system, according to a preferred embodiment. 
     As shown in  FIG.  10   , trust circles need not be implicitly hierarchical in nature and may overlap in various ways similar to a logical Venn diagram. For example one individual may be a friend and also known on social media, or someone may be both family and a colleague (as is commonplace in family businesses or large companies that may employ many people). As shown, anybody may be considered “untrusted” regardless of their other trust groupings, for example if a user does not wish to receive callbacks from a specific friend or coworker. While the arrangement shown is one example, it should be appreciated that a wide variety of numerous overlapping configuration may be possible with arbitrary complexity, as any one person may be logically placed within any number of groups as long as the trust groupings themselves are not exclusive (such as a group for coworkers and one for individuals outside the company). 
     Expanding on the notion of trust circles, there may also be logical “ability” circles that correspond to various individuals&#39; capabilities and appropriateness for various issues, such as (for example) tech support skill or training with specific products, or whether a member of a brand  1010  is actually a member of the best brand to handle a specific reason for a callback, based on the callback request context. For example, a customer requesting a callback for assistance with booking a flight may not be adequately served by employees of airlines that don&#39;t offer flights to their intended destination, so combining the brand trust zone  1010  with a capability map would indicate to the callback system which individuals are more appropriate for the callback in question. This expands from merely trusting certain users and discarding others, to a form of automated virtual concierge service that finds the user for a callback request that is most capable and relevant to the request, ensuring optimum handling of the callback requestor&#39;s needs. 
       FIG.  11    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, according to an embodiment. According to an embodiment, a callback cloud  220  must receive a request for a callback to a callback recipient, from a callback requester  1110 . This refers to an individual calling a user of a cloud callback system  220 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1120 , using a callback manager  223 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1130  using a profile manager  221  in a cloud callback system, as well as an analysis of environmental context data  1140 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1150 . When such a time arrives, a first callback is attempted  1160  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1170 , allowing a media server  225  to bridge the connection when both are online, before deleting the callback object  1180 . 
       FIG.  12    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a calendar server, according to an embodiment. According to an embodiment, a callback cloud  320  must receive a request for a callback to a callback recipient, from a callback requester  1205 . This refers to an individual calling a user of a cloud callback system  320 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1210 , using a callback manager  324 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1215  using a profile manager  322  which manages the storage and retrieval of user profiles, including global and local user profiles. The profile manager  322 , which may be located in a cloud callback system, interfaces with user-specific calendars  1220  to find dates and timeslots on their specific calendars that they both may be available  1225  through use of a calendar server  321 , as well as an analysis of environmental context data  1230 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1235 . When such a time arrives, a first callback is attempted  1240  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1245 , allowing a media server  326  to bridge the connection when both are online, before deleting the callback object  1250 . 
       FIG.  13    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including gathering of environmental context data of users, according to an embodiment. According to an embodiment, a callback cloud  420  may interface with a brand interface server  430 , which may interface with third-party or proprietary brands of communications devices and interfaces such as automated call distributor systems  1305 . Through this brand interface, the system may receive a request for a callback to a callback recipient, from a callback requester  1310 . This refers to an individual calling a user of a cloud callback system  420 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1315 , using a callback manager  423 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1320  using a profile manager  421  in a cloud callback system, as well as an analysis of environmental context data  1325 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1330 . When such a time arrives, a first callback is attempted  1335  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1340 , allowing a media server  425  to bridge the connection when both are online, before deleting the callback object  1345 . 
       FIG.  14    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a brand interface server and intent analyzer, according to an embodiment. According to an embodiment, a callback cloud  520  may interface with a brand interface server  530 , which may interface with third-party or proprietary brands of communications devices and interfaces such as automated call distributor systems  1405 . Through this brand interface, the system may receive a request for a callback to a callback recipient, analyzing their intent from the provided input  1410 , followed by processing it as a callback request  1415 . Callback requestor intent in this case may indicate how long or what times are preferred for a callback to take place, which may be taken into account for a callback  1410 . This refers to an individual calling a user of a cloud callback system  520 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1420 , using a callback manager  523 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1425  using a profile manager  521  in a cloud callback system, as well as an analysis of environmental context data  1430 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1435 . When such a time arrives, a first callback is attempted  1440  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1445 , allowing a media server  525  to bridge the connection when both are online, before deleting the callback object  1450 . 
       FIG.  15    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a privacy server, according to an embodiment. According to an embodiment, a callback cloud  620  must receive a request for a callback to a callback recipient, from a callback requester  1505 . This refers to an individual calling a user of a cloud callback system  620 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. When a callback request is received  1505 , trust-circle rules are enforced using a privacy server  621 ,  1510  preventing untrusted users from requesting a callback, or insufficiently trusted users from scheduling callbacks at specific times or perhaps preventing them from requesting callbacks with certain callback recipients, depending on the privacy settings of a given callback recipient. All data may also be encrypted  1515  for added security, using a privacy server  621 . If a callback request is allowed to proceed, a callback object is instantiated  1520 , using a callback manager  624 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1525  using a profile manager  622  in a cloud callback system, as well as an analysis of environmental context data  1530 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1535 . When such a time arrives, a first callback is attempted  1540  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1545 , allowing a media server  626  to bridge the connection when both are online, before deleting the callback object  1550 . 
       FIG.  16    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a bot server, according to an embodiment. According to an embodiment, a callback cloud  720  may first utilize a bot server  724  to receive an automated callback request from a user  1605 , which may allow a user to specify their parameters for a callback directly to the system. The system may then receive a request for a callback to a callback recipient, from a callback requester  1610 . This refers to an individual calling a user of a cloud callback system  720 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1615 , using a callback manager  723 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1620  using a profile manager  721  in a cloud callback system, as well as an analysis of environmental context data  1625 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1630 . When such a time arrives, a first callback is attempted  1635  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1640 , allowing a media server  726  to bridge the connection when both are online, before deleting the callback object  1645 . 
       FIG.  17    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including an operations analyzer, according to an embodiment. According to an embodiment, a callback cloud  820  must receive a request for a callback to a callback recipient, from a callback requester  1705 . This refers to an individual calling a user of a cloud callback system  820 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. A callback object is instantiated  1710 , using a callback manager  823 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1715  using a profile manager  821  in a cloud callback system, as well as an analysis of environmental context data  1720 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1725 . When such a time arrives, a first callback is attempted  1730  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1735 , allowing a media server  826  to bridge the connection when both are online, before deleting the callback object  1740 . An operations analyzer  824  may then monitor operation of components and communication channels involved in the callback, analyze the results of the attempted callback bridge, and if it was unsuccessful, determine whether a component or communication channel of a callback cloud experiences a failure, and either select an alternate communication channel to complete the callback at a scheduled time or store such results  1745  for viewing by a later system administrator. 
       FIG.  18    is a method diagram illustrating the use of a callback cloud for intent-based active callback management, including a brand interface server, intent analyzer, and broker server, according to an embodiment. According to an embodiment, a callback cloud  920  may interface with a brand interface server  930 , which may interface with third-party or proprietary brands of communications devices and interfaces such as automated call distributor systems  1805 . Through this brand interface, the system may receive a request for a callback to a callback recipient, analyzing their intent from the provided input  1810 , before a broker server  940  communicates this request to the callback cloud  920 ,  1820  and not only exposes but also manages connections and interactions between various brands  910  and a callback cloud  920 ,  1815 . The system may then process a callback request  1820 . Callback requestor intent in this case may indicate how long or what times are preferred for a callback to take place, which may be taken into account for a callback  1810 . This refers to an individual calling a user of a cloud callback system  920 , being unable to connect for any reason, and the system allowing the caller to request a callback, thus becoming the callback requester, from the callback recipient, the person they were initially unable to reach. After receiving at least one callback request, a broker server  940  may further manage dealings between multiple callback requests and more than two requestors or recipients  1825 , selecting a plurality of specific actions to take during a callback and allocating each selected action to a system component involved in the callback. The broker server  940  may organize successive or nested callback attempts by user availability and times available, as well as the times the requests are received  1830 . At least one callback object is then instantiated  1835 , using a callback manager  923 , which is an object with data fields representing the various parts of callback data for a callback requester and callback recipient, and any related information such as what scheduled times may be possible for such a callback to take place. Global profiles may then be retrieved  1840  using a profile manager  921  in a cloud callback system, as well as an analysis of environmental context data  1845 , allowing for the system to determine times when a callback may be possible for a callback requestor and callback recipient both  1850 . When such a time arrives, a first callback is attempted  1855  to the callback requestor or callback recipient, and if this succeeds, a second call is attempted to the second of the callback requestor and callback recipient  1860 , allowing a media server  925  to bridge the connection when both are online, before deleting the callback object  1865 . 
       FIG.  19    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  1905 , a profile manager  1910 , an environment analyzer  1915 , an interaction manager  1920 , and a media server  1925 . A callback request is made  1930 , which is forwarded to a callback manager  1915 . A callback manager then requests profile information on a callback requestor and recipient  1935 , a profile manager  1910  then requesting environmental context  1940  from an environment analyzer  1915 . Profile information and environmental context information are both sent to the callback manager  1945 , before an interaction manager is sent the time for an attempted callback  1950 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  1955 . The call results are sent back to an interaction manager  1960 , which then sends the finished result of the attempt at bridging the callback to the callback manager  1965 . 
       FIG.  20    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a calendar server, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2005 , a profile manager  2010 , an environment analyzer  2015 , an interaction manager  2020 , a media server  2025 , and a calendar server  2030 . A callback request is made  2035 , which is forwarded to a callback manager  2015 . A callback manager then requests profile information on a callback requestor and recipient  2040 , a profile manager  2010  then requesting environmental context  2045  from an environment analyzer  2015 . Profile information and environmental context information are both sent to the callback manager  2050 , before a profile manager may request calendar schedules  2055  from both a callback requestor and a callback recipient, using a calendar server  2030 . If calendars are available for either or both users, they are forwarded to the callback manager  2060 . The interaction manager is then sent the time for an attempted callback  2065 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2070 . The call results are sent back to an interaction manager  2075 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2080 . 
       FIG.  21    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2105 , a profile manager  2110 , an environment analyzer  2115 , an interaction manager  2120 , a media server  2125 , and a brand interface server  2130 . A callback request is made  2135 , which is forwarded to a callback manager  2115 . A brand interface server may identify the devices or services communicating with the callback cloud system  2140 , and possibly allow for communication back to such services and devices. A callback manager then requests profile information on a callback requestor and recipient  2145 , a profile manager  2110  then requesting environmental context  2150  from an environment analyzer  2115 . Profile information and environmental context information are both sent to the callback manager  2155 , before an interaction manager is sent the time for an attempted callback  2160 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2165 . The call results are sent back to an interaction manager  2170 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2175 . 
       FIG.  22    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server and intent analyzer, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2205 , a profile manager  2210 , an environment analyzer  2215 , an interaction manager  2220 , a media server  2225 , a brand interface server  2230 , and an intent analyzer  2235 . After a callback request is made, a brand interface server may forward raw data from the services or applications used in making the request to an intent analyzer  2240 , before identifying the devices or services communicating with the callback cloud system  2245  and sending such data to a callback manager. An intent analyzer may then send data on callback request intent  2250  to a callback manager  2205 , which may indicate such things as the time a user may want to receive a callback, or what days they may be available, or how long the callback may take, which may affect the availability of timeslots for both a callback requestor and recipient. A callback manager then requests profile information on a callback requestor and recipient  2255 , a profile manager  2210  then requesting environmental context  2260  from an environment analyzer  2215 . Profile information and environmental context information are both sent to the callback manager  2265 , before an interaction manager is sent the time for an attempted callback  2270 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2275 . The call results are sent back to an interaction manager  2280 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2285 . 
       FIG.  23    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a privacy server, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2305 , a profile manager  2310 , an environment analyzer  2315 , an interaction manager  2320 , a media server  2325 , and a privacy server  2330 . A callback request is made  2335 , which is forwarded to a callback manager  2315 . A callback manager may then request privacy settings  2340  from a privacy server  2330 , being forwarded the privacy settings  2345  from said server, including information on a user&#39;s trust circles as needed. A callback manager  2305  then requests profile information on a callback requestor and recipient  2350 , a profile manager  2310  then requesting environmental context  2355  from an environment analyzer  2315 . Profile information and environmental context information are both sent to the callback manager  2360 , before an interaction manager is sent the time for an attempted callback  2365 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2370 . The call results are sent back to an interaction manager  2375 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2380 . 
       FIG.  24    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a bot server, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2405 , a profile manager  2410 , an environment analyzer  2415 , an interaction manager  2420 , a media server  2425 , and a bot server  2430 . A callback request is made  2435 , which is forwarded to a bot server  2430 . A bot server may handle a user in a similar manner to an automated call distribution server for example, allowing a user to communicate verbally or textually with it, or it may instead handle results from a chat server and parse the results of a user interacting with another chat server  715 . A callback manager may then receive parsed callback data  2440  from a bot server  2430 . A callback manager  2405  then requests profile information on a callback requestor and recipient  2445 , a profile manager  2410  then requesting environmental context  2450  from an environment analyzer  2415 . Profile information and environmental context information are both sent to the callback manager  2455 , before an interaction manager is sent the time for an attempted callback  2460 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2465 . The call results are sent back to an interaction manager  2470 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2475 . 
       FIG.  25    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including an operations analyzer, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  2505 , a profile manager  2510 , an environment analyzer  2515 , an interaction manager  2520 , a media server  2525 , and an operations analyzer  2530 . A callback request is made  2535 , which is forwarded to a callback manager  2505 . A callback manager then requests profile information on a callback requestor and recipient  2540 , a profile manager  2510  then requesting environmental context  2545  from an environment analyzer  2515 . Profile information and environmental context information are both sent to the callback manager  2550 , allowing a callback manager to forward initial callback object data to an operations analyzer  2555 , before an interaction manager is sent the time for an attempted callback  2560 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  2565 . The call results are sent back to an interaction manager  2570 , which then sends the finished result of the attempt at bridging the callback to the callback manager  2575 . At the end of this sequence, the callback result data, including any failures or lack of ability to bridge a call for a completed callback between at least two users, is forwarded to an operations analyzer  2580  for possible review by a human, if needed, and for adjustment of the parameters the system uses in attempts to make callbacks for said users. 
       FIG.  30    is a message flow diagram illustrating the exchange of messages and data between components of a callback cloud for intent-based active callback management, including a brand interface server, intent analyzer, and broker server, according to an embodiment. Key components exchanging messages in this embodiment include a callback manager  3005 , a profile manager  3010 , an environment analyzer  3015 , an interaction manager  3020 , a media server  3025 , a brand interface server  3030 , an intent analyzer  3035 , and a broker server  3090 . After a callback request is made, a brand interface server may forward raw data  3040  from the services or applications used in making the request to an intent analyzer  3035 , before identifying the devices or services communicating with the callback cloud system and sending such data to a broker server  3090 , which identifies and exposes brand information  3045  to the callback cloud while managing connections between the callback cloud and various brands. An intent analyzer may then send data on callback request intent  3050  to broker server  3090 , which forwards this information to a callback manager  3005 , which may indicate such things as the time a user may want to receive a callback, or what days they may be available, or how long the callback may take, which may affect the availability of timeslots for both a callback requestor and recipient. A callback manager then requests profile information on a callback requestor and recipient  3055 , a profile manager  3010  then requesting environmental context  3060  from an environment analyzer  3015 . Profile information and environmental context information are both sent to the callback manager  3065 , before an interaction manager is sent the time for an attempted callback  3070 , which then, at the designated time, sends the relevant IP addresses, usernames, phone numbers, or other pertinent connection information to a media server  3075 . The call results are sent back to an interaction manager  3080 , which then sends the finished result of the attempt at bridging the callback to the callback manager  3085 . 
       FIG.  31    is a block diagram illustrating an exemplary system for a cloud-based virtual queuing platform  3100 , according to an embodiment. A cloud-based virtual queuing platform  3100  establishes and manages virtual queues associated with real or virtual events hosted by entities  3102  and attended by end-devices or person&#39;s with end-devices  3101 . The benefits of cloud-based queue management comprise cost savings, security, flexibility, mobility, insight offerings, increased collaboration, enhanced quality control, redundant disaster recovery, loss prevention, automatic software updates, a competitive edge, and sustainability. A cloud-based virtual queuing platform  3100  may comprise a web-based (e.g., mobile or desktop browser) or some other Internet-based means (CLI, mobile and desktop applications, APIs, etc.) to create, manage, and analyze queues that may be accessed remotely by the hosting entity  3102 . A cloud-based virtual queuing platform  3100  may comprise an application-based means to create, manage, and analyze queues that may be accessed remotely by the hosting entity  3102 . Entities  3102  may communicate to a cloud-based virtual queuing platform  3100  via on-premise servers, the entity&#39;s own cloud-based environment, desktop and laptop computing platforms, mobile platforms, and comparable devices. Likewise, persons wishing to join, leave, or get the status of a queue (other reasons may exist, e.g., transfer queues) may use any electronic means that the entity  3102  may use. Referring now to  FIG.  32   , entities  3102  and end-devices  3101  may communicate over a plurality of communication networks (Internet, Satellite, PSTN, Mobile networks, Wi-Fi, BlueTooth, NFC, etc.)  3201  to a cloud-based virtual queuing platform  3100 . 
     The cloud based virtual queuing platform  3100  as described herein may make use of the embodiments from the previous figures and referenced applications by combining prior embodiments with at least one of the one or more components from the embodiments described henceforth. For example, a cloud platform for virtual queuing  3100  may employ a callback cloud  920  and/or user brands  910  as previously described to facilitate any features necessitated by the aspects of a cloud platform for virtual queuing  3100  as disclosed herein. As a specific example, a callback cloud  920  may handle the text and voice services used in a cloud platform for virtual queuing  3100 . Additionally, any previous embodiments may now implement the queue service  3200  as described in the following paragraphs and figures. For example, previous embodiments are directed towards call center applications. therefore, the queue service  3200  and its aspects as described herein, may better facilitate the queueing aspects of the call center embodiments or provide enhancements not disclosed in the previous embodiments. 
       FIG.  33    is a block diagram illustrating an exemplary system architecture for a queue service  3200 . According to one embodiment, A queue service  3200  may make use of one or more, or some combination of the following components: a queue manager  3301 , a queue sequencer  3302 , a queue load balancer  3303 , a prediction module  3304 , a notification module  3305 , a security module  3306 , an analysis module  3307 , and one or more databases  3308 . 
     A queue manager  3301  interfaces with entities and end-devices according to one embodiment. In another embodiment, a queue manager  3301  may use a callback cloud  920  to initiate messages and data flow between itself and entities and end devices. According to another embodiment, a notification module  3305  may take over notification functions to entities and end-devices. In yet another embodiment, a notification module  3305  instructs a callback cloud  920  as to what messages to send and when. According to an aspect of various embodiments, a notification module  3305  may manage notifications to end-devices based on a notification escalation plan, whereby notifications a means are dynamically adjusted based on a set of rules. According to one embodiment, a queue manager  3301  may handle the managing of a plurality of simple queues without the need for the other modules  3302 - 3307 , i.e., if the simple queues require no authentication, security, analysis, predictions, and other aspects, then a queue manager  3301  may be all that is required. The previously mentioned aspects may be implemented based on a pricing scheme, according to one embodiment. A tiered-pricing cloud-based virtual queuing platform wherein the tiered pricing is based off the features available to the entities. According to one embodiment, a queue manager  3301  works in tandem with other modules  3302 - 3307  to provide the full functionality of the features disclosed herein specifically in regards to handling sequences of queues. 
     Sequenced queues comprise two or more queues that are sequential, meaning at least one of the queues comes before another queue. Sequential queues may comprise parallel queues, meaning that one of the sequential queues is comprised of more than one queue for the same event. According to one embodiment, sequenced event queue management may be handled by a queue sequencer  3302 . Examples of sequenced events with associated sequenced lines include air travel, zoos, concerts, museums, interactive galleries, theme parks, and any event with multiple required or optional queues. Sequential queues may not typically be treated with a first-in-first-out algorithm because the rate at which one person completes a queue may not be the same as a different person. Consider air travel; the first line (check-in) of a sequence of lines (subsequently at least security and then boarding lines) is checking in at an airport. A person with no checked baggage will make it through faster than a person with baggage to be checked; and a person who preprinted their boarding pass is even faster. 
     The queue sequencer  3302  may be supplemented by a queue load balancer  3303  that manages the load across a plurality of queues, parallel or not, and sequential or not. The queue load balancer  3303  may take predictions from a prediction module  3304  to better manage wait times across the plurality of queues. Continuing with the air travel example; a queue load balancer  3303  may distribute persons across queues for the same event (multiple security queues, etc.) and may consider many factors. One factor may be distributing persons who all belong to a single group into different parallel queues, so that the group may finish clearing the queue(s) more closely in time than had they all queued at just one queue, rather than spread across multiple parallel queues. Another factor may be the consideration of a route a person or group of persons has to take to make it to the first queue or a subsequent queue. Still more factors may be alerting the entity to open or close more queuing lanes or to produce more or less manual or automatic scanners. A factor may also be to consider the estimated time of arrival for some individuals and yet another factor may be whether some individuals are willing to wait longer than others. In some embodiments the queue sequencer  3302  and queue load balancer  3303  work in tandem with the prediction module  3304  to run simulations of queues in order to achieve the minimal wait times possible. Simulations may have goals other than minimal wait times, e.g., to maximize distance between persons during a pandemic. 
     As one example, expanding on the routing factor, a prediction module  3304  may run simulations (using machine learning, according to one embodiment) where the possible combinations of each queued person and the possible wait-times of a sequence of queues is iterated over to find the optimal configuration of persons across all queues. A specific example may be a simulation which considers all the possible airline check-in counters, their physical location in relation to one or more security lines and each other, their historical check-in rates, the distance to trams, buses, and the like, the passengers and the requirements of their check-in (baggage, wheelchair service, preprinted ticket, groups size, etc.), when the passengers may arrive (using GPS or explicit requests for estimated time of arrival and mode of transportation), passenger walking rate (using sensors), departure times, and other factors such that the simulation produces an optimal time-to-check-in notification to each passenger. Simulations may be constrained not to create a perceptible unfairness to a queue. For example, putting a group of five people who just arrived in front of a single person who has been waiting onsite for a significant amount of time. This invention may also be used in air travel arrivals, expediting baggage claim processes and transportation services. These scenarios are merely exemplary and not to be limiting in any way. Many factors exist across multiple domains and likewise for the types of constraints for simulations. 
     According to various embodiments, a single queue is used for both walk up users scanning the QR code with a mobile device and users who book a spot in the queue using the web UI (e.g., webpage or webapp, etc.). In this case, users are in a single queue, however, the users who booked online have priority for that time slot they booked. So for instance, if the queue currently has a two hour wait time at 2 p.m., and a user books a time slot for 3 p.m., when 3 p.m. approaches the user will be prioritized and will be notified to enter the physical queue. The queue load balancer  3303  and prediction module  3304  work together to account for these time slots, the total people per time slot, and factor it into the predictive models to produce an accurate estimated wait time for walk-ups joining the virtual queue, according to some embodiments. In other words, if a user walks up and enters the virtual queue, the estimated wait time is taking into account all the users ahead of him or her including the ones in overlapping time slots. Additionally, if a user booked a time slot for 1 p.m. and the user shows up early at 12:30 p.m. and scans the QR code, the user will be provided the queue estimated wait time and given the option (e.g., via an SMS message, email, messaging application, etc.) to keep the booked time slot or cancel the booked time slot and enter the queue like anyone else (that way if the estimated wait time is less than 30 minutes, the user can enter the queue early and not have to wait around). 
     Factors described above and elsewhere herein may be informed and/or supplemented using large or small data repositories (both private and public), streaming real-time (or near-real-time) data (e.g., traffic, etc.), sensor data, “Big Data”, and many other sources of data  3308 . Another example from a separate domain is the emergency room (ER). The various hospital departments/clinics, staffing, and procedures that go into the ER service forms a complex logistical system that must be adhered to for regulatory and safety reasons. A queue service  3200  may be used with a predictive medical prognosis module (not illustrated) or simply data entries from front desk staff to prioritize patient queuing. Scheduling ER visits is also possible given the proper circumstances and may reduce wait times. Scheduling appointments and managing walk-ins spans multiple domains and is another factor that is considered by a queue service  3200 . 
     According to some embodiments, the queue service  3200  and/or cloud platform for virtual queuing  3100  may be configured to integrate with one or more internet-of-things (IoT) devices and/or sensors in order to facilitate data exchange between the one or more IoT devices and sensors and the queue service  3200  and/or platform  3100 . In some embodiments, one or more IoT devices and/or sensors may be used to detect the number of people in the physical queue and use that information in conjunction with queue load balancer  3303  and/or prediction module  3304  to automatically adjust the throughput of the users being dequeued. Types of IoT devices and/or sensors that may be used include, but are not limited to, thermal sensors, pressure sensors, force sensors, vibration sensors, piezo sensors, position sensors, photoelectric sensors, switches, transducers, and cameras. In some embodiments, received sensor data may be processed using one or more algorithms best suited for processing the particular type of data received from the sensor. For example, a camera may be set up to watch the queue and return live video data to the queue service  3200 , which may be configured to apply facial recognition algorithms in order to determine the number of unique faces in the queue, and thus the number of individuals waiting in the queue. As another example, one or more pressure sensors may be deployed in the path of the queue and when pressure is detected and the data sent to queue service  3200 , it may determine each set of pressure data corresponds to a new individual entering or leaving the queue. In yet another embodiment, multiple sensors of different types may be used simultaneously in order to determine the number of people waiting in a queue. According to an embodiment, upon determination of the number of people in a queue, queue service  3200  may automatically predict and adjust the queue wait times and subsequently the throughput of the users being dequeued. 
     A security module  3306  may be used to generate QR codes, one-time passwords, two-factor authentication codes, and the like. A security module  3306  may automatically authentic queued persons at biometric stations, NFC stations, entity scanning devices, or use similar technologies which may identify the uniqueness of a device or person. A security module  3306  may receive an acknowledgement from an entity from a manual verification, or a verification using the entities own equipment (using APIs as one example). A security module  3306  may report the success or failure of an authentication attempt to a 3 rd  party, such as security forces or electronic alarm. The success or failure of an authentication attempt may drive the next steps of one or more components of a cloud based virtual queuing platform  3100 . A security module  3306  may monitor sensors that checks if the correct amount of people enters a designated location. For example, a hotel may use the disclosed invention to automate check-ins; where NFC beacons at the front desk identify the person at the front desk by scanning the device which would have been pre-registered with the guest&#39;s profile and could then could trigger the release a locked compartment containing the guest&#39;s room key and hotel information. Additionally, rules may be implemented which do not allow the release of the locked compartment if the queued person&#39;s turn was not up or has past. 
     An analysis module  3307  may provide statistical analysis of past, current (i.e., real-time), and future (i.e., predicted) queue metrics.  FIG.  40    is exemplary graph output  4000  from an analysis module  3007  illustrating the throughput of a queue during a half-hour timeframe. Over time machine learning could predict what throughput future timeframes may hold.  FIG.  41    is another exemplary graph output  4100  from an analysis module  3007  illustrating a 10-minute time-block analysis from 4:00 AM to 1:00 PM of wait-times experienced in a queue, represented as different shadings (simplified for illustrative purposes). Analysis reports may comprise metrics such as total parties, total people, average party size, average queue length, average throughput, average wait, and other comparable metrics. 
       FIG.  34    is a block diagram showing an exemplary use of a cloud-based queue service  3200 , according to one aspect. Using the scenario of air travel, a passenger or a group of passengers may approach a queue in an airport. At the beginning of the queue, a sign may be displayed such as the one  3700  illustrated in  FIG.  37   . Where the sign  3700  comprises a QR code  3401  that auto generates a text message on the user&#39;s end-device, and that text message is sent to a cloud-based queue service  3200  from the end-device and initializes the queueing service provided by the invention. According to one embodiment, this sign could be scanned by a single passenger or a group of passengers for a sequence of queues. According to one other embodiment, this sign could initialize the passenger or group of passengers for a just one queue. According to yet another embodiment, this sign may be scanned by just one passenger from a group of passengers for a single queue or a sequence of queues for the whole group. According to another embodiment, this sign may be scanned by each passenger in a group of passengers for a single queue or a sequence of queues. Once the QR code  3401  is scanned by an end-device  3400 , a text message  3402  may be automatically generated  3451  on the scanning device  3400 . The end-device  3400  sends  3452  the text message to a cloud-based virtual queuing platform  3100 . A cloud-based virtual queuing platform  3100  updates  3453  the queue  3403  based on the received message  3402  and sends  3454  a confirmation notification  3404  back to the end-device  3400 . As the reserved place in the queue approaches, further notifications  3405  are sent  3455  to the end-device  3400  based on a set of notification escalation rules. Once the queued person or persons arrive at the queue destination  3406 , and having checked-in (and authenticated their identity—or the end-device&#39;s, according to some embodiments) at their designated time  3456 , the queue  3403  may be updated accordingly  3457 . 
     Exemplary tables of notification escalation rules are illustrated in  FIG.  44    and  FIG.  45   .  FIG.  44    is a table diagram showing an exemplary and simplified rules-based notification escalation plan  4400 . The notification type may be configured by the user, or by an administrator, or some combination thereof, based on the desired operating business parameters. When a queued person is 20 minutes out, 10 minutes out, and due to show for a queued reservation, the person&#39;s end-device may be notified via their stored preferred communication method if present, or it may default to text-based notifications or some other communication means. According to one embodiment, push notifications may be sent via a browser or application. Should the queued person not show on time, the end-device may receive one last preferred reminder/notification. As time passes, an IVR system may call the end-device and present the user with a series of options such as extending the time to show up by a few minutes or to reschedule the time-slot. Should the IVR call fail, or according to some other parameter, an automated message may play over the intercom if available. As a last resort, a call center agent may place an outbound call to the queued person&#39;s end-device to try to resolve the tardiness issue. Call centers with call blending capabilities may make such outbound calls. This table is merely exemplary and meant to convey just one scenario of rules. Many configurations and implementations exist using various means of communication and feedback mechanisms. 
     For example,  FIG.  45    is a table diagram showing an exemplary and simplified rules-based notification escalation plan that further uses location data  4500 . Using one or a plurality of sensors, the location of a person may be known or predicted for some time in the future (i.e., using map and traffic data and the end-device&#39;s GPS as one example). If the position in the queue is held at some time X, and the estimate time of arrival—using location data—for said queued person is Y, then Time Δ=X−Y. Therefore, any negative value of Time Δ is a likely scenario that a queued person will not show up at the expected time. Similarly, should Time Δ be a positive value, i.e., a person or group will show up earlier than expected a queue load balancer  3303  may reorganize queued persons to facilitate the early arrival. A rule set  4500  may be created and applied for such situations. Other rule sets may be created for various aspects of the queuing procedure. Additionally, location data may be used by a prediction module  3304  to predict the future location of queued persons. 
       FIG.  35    is a method diagram illustrating the use of a cloud-based virtual queuing platform with an end-device, according to an embodiment. A cloud-based virtual queuing platform  3100  receives a request from an end device to join a queue  3500 . The request may also be to leave a queue if already slotted, or change places in a queue, or request more time to get to the queue destination, change the party size, transfer between queues, or to request the status of a queue. 
     A cloud-based virtual queuing platform  3100  updates one or more queues based on the type of request, i.e., based on at least one of the scenarios presented above  3501 . Configuration changes may occur within components  910 ,  920 , and  3301 - 3308  of a cloud-based virtual queuing platform  3100  based on certain request scenarios. For example, a request for more time to reach the destination if a person or persons is running late may cause a queue load balancer  3303  and/or a prediction module  3304  to adjust their algorithmic parameters, which in the end may still update the queues. 
     A confirmation will be sent back to the end-device to confirm a successful or failed request attempt  3502 . Requests may also be sent to the entity as desired or stored in a database or blockchain. Failed or suspicious for requests may activate alarms or trigger security sequences within a security module  3306 . 
     Periodic updates may be sent to the end device, entity, or some combination thereof  3503 . As described previously, notifications, i.e., periodic updates, maybe sent according to a rule set (e.g., notification escalation plan). Notifications may be sent over any type of communication means, any combinations of said communication means, and in any frequency as necessary. 
     Notifications may or may not adjust as the time nears when a queued person or persons should begin to move towards the queue destination  3504 . Adjustments may be as described above using notification escalation plans. According to one embodiment, alerts may be sent over devices that are not the end-device, such as an intercom or pager system. According to one embodiment, a prediction module  3304  uses routing algorithms and machine learning to determine the amount of time needed for a person or persons to get to the destination in time. The routing algorithms and machine learning not only considers the person who is currently at the front of the queue, but may consider any combination of persons across some or all queues and any combination of some or all persons in some or all queues. 
     A cloud-based virtual queuing platform  3100  is notified once the person or persons has checked in  3505 . A successful notification may depend on whether or not that person or persons have been successfully authenticated, according to one embodiment. Notification that the individual or individuals have checked-in may update queues or trigger other actions according to the embodiments set forth herein. 
     One such update to the queue may be to remove the queued individual or individuals, i.e., the individual&#39;s or individuals&#39; end-devices, from the queue  3506 . Should the individuals be in a sequential queue, then the individuals may be transferred to a different queue in addition to being removed from the queue they were previously in. 
       FIG.  36    is a method diagram illustrating another use of a cloud-based virtual queuing platform with an end-device, according to an embodiment. In this embodiment, follow-up text messages are sent to an end-device to request further information. The information may be required or not depending on the application. The information may be used to more accurately predict wait-times, slot the appropriate number of persons in a queue, or other queue-based parameters. 
     A group of travelers may scan a QR code  3401  as illustrated in the sign  3700  in  FIG.  37   , whereby after sending the automatically generated request  3402 / 3600 , the end-device receives a request for information in the form of a text, as one example, from a cloud-based virtual queuing platform  3100  as to the number of passengers in the group  3601 . A cloud-based virtual queuing platform  3100  may then accumulate the required number of slots  3603  from the reply  3602  in one or more queues as calculated by the queue load balancer  3303 . Like  FIG.  35    explains, a sequence of notifications  36043607  may then be sent to the end-device(s) and to the entity until the group has checked-in and has been removed from the queue  3608 . 
       FIG.  38    and  FIG.  39    are block diagrams illustrating an exemplary mobile application (or web-based/browser-based according to one embodiment) used in bi-directional communication between a cloud-based virtual queuing platform and an end-device, according to an embodiment.  FIG.  38    shows how a person may reserve a spot in a security checkpoint line for a group of 4 using a web-based or app-based mobile solution  3800 . While  FIG.  39    shows a confirmation screen following the reservation screen in  FIG.  38     3900 . 
       FIG.  42    is a flow diagram illustrating a web-based GPS aspect of a cloud-based virtual queuing platform, according to an embodiment. According to one aspect of various embodiments, GPS is used to track a queued person or persons and may also be used to predict estimated-time-of-arrivals and to then use that information to dynamically adjust one or more queues. This figure illustrates just one method of gaining access to and implementing GPS functionality. 
     According to this embodiment, a URL is sent  4200  to an end-device that directs the end-device to a webpage that asks for access to the end-device&#39;s location  4201 . The URL may be sent by any number of communication means (text, email, etc.). According to another embodiment, GPS access may be granted through a partnering application or a bespoke application. 
     The GPS data is then used at least by itself to determine the location of the queued person  4202 . If traveling in a group, an automated message could be sent to the tracked person asking if the whole group is present therefore providing location data for the whole group using one GPS. The locality data may be used with 3 rd  party data (such as map and traffic data, public transportation data, news, social media, and “Big data”) to make predictions and manage one or more queues. Predictions using the GPS and 3 rd  party data may estimate the time of arrival for a plurality of people  4202 . The plurality of data may be used to suggest specific travel routes or incentives for some individuals so that they arrive at a specific time in order to balance the queue load. For example, if the data shows a large influx of people are requesting or plan to arrive within a short time window, new route suggestions may be sent to some individuals to increase the total travel time and discounts for future events may be offered as an incentive. Continuing with this example, other individuals may be offered a coupon to a coffee shop which is on-route to the queue destination, in the expectation that some percentage will take advantage of the coupon thus better balancing the queue throughput for that high-influx time window. Other predictions and uses are anticipated using location data, sensors, 3 rd  party data, and combinations thereof in order to better manage and balance one or more queues. 
     In a first  4200  and second  4201  step, the URL is sent to an end-device  4200  which leads to a browser that requests permission for the GPS  4201 . The initial GPS reading skips steps  4202  and  4203  as they are “as necessary”, and checks if the queued person is going to arrive on time  4205 . If the person is predicted to be on-time, then notifications are sent as normal, set by the notification escalation plan  4207 . If the person is not to be on-time and has not departed for the queue destination  4206 , then notifications will be sent according to the notification escalation plan using those two parameters  4205 / 4205 . If the person will not be on-time but is in-route, then the queue may be updated  4203  and if a prediction module  3304  determines a new (may be shorter, longer, or the same based on load balancing) route, the new route is sent to the end-device  4204 . It may also be the case that the delay caused by the queued person requires some shifting of other queued persons, an incentive may be sent to one or more queued people  4204 . At some point in time, given the queued person makes it to the queue destination, he, she, or they will be checked-in  4208  and the queue may be update appropriately  4209 . 
     Referring now to  FIG.  43   , steps  4300 - 4308  reflect the previous figure&#39;s steps,  4200 - 4208  respectively, with the exception that in a set of sequential queues, a person or persons may be transferred to the next sequential queue  4309 , unless that queue was the last in the sequence. It is also correct to declare that  FIG.  42    may be applied to sequential queues as a person or persons would inherently be removed from a previous queue in a sequence of queues after being transferred to the next queue in the same sequence. 
       FIG.  46    is a message flow diagram illustrating the exchange of messages and data between components of a cloud-based virtual queuing platform used in sequential event queue management, according to an embodiment. An initial check-in message is received by a queue manager  3301 . Automated texting and callback technology may be triggered to ask one or more follow up questions to get more information. The follow up questions may be required or optional. A prediction module  3304  uses the available information to make the best check-in time recommendations, the amount of time needed to clear each queue, and so forth. The requesting party may then be placed in the first queue in the sequence based on the operating parameters (either by recommendations or explicit requests). The queue sequencer  3302  sends updated queue information to the queue manager  3301  which may trigger periodic notifications to be sent to the party. 
     A queue load balancer  3303  uses real-time queue information from a queue sequencer  3302  and predictions from a prediction module  3304  to keep the wait times to a minimum across the plurality of queues. The queue load balancer  3303  may be configured to prioritize other goals instead as disclosed elsewhere herein. As also disclosed elsewhere, the queue load balancer  3303  may use detours, incentivized delays, and coupons to adjust the flow of traffic through an event, both spatially and/or temporally. For example, a virtual event may not have spatial restrictions but network congestion restrictions, wherein queued persons may be presented with advertisements or media to control the flow of the queue. These aspects may be optional for queued persons with incentives to choose to wait longer than others, such as the airlines industry does when a flight is overbooked. 
     Once the party has checked-in to the first line successfully, the party is slotted into the next sequential queue. Throughout the whole sequential queue process, the queue load balancer  3303  is maintaining the optimal wait time configuration. This process of checking-in, maintaining bi-directional communication with the party (i.e., end-device), and maintaining optimal wait times is iterated through each line until the party clears the final queue. 
       FIG.  47    is a flow diagram illustrating a load-balancing aspect of a cloud-based virtual queuing platform, according to an embodiment. This figure illustrates only one algorithmic aspect of a load balancer  3303 . This aspect is the balancing of parallel queues, wherein parallel queues are queues all leading to the same outcome/destination. 
     In a first step  4700 , the average wait time (wait time(s) could also be measured against some other parameter, e.g., even if one queued person has to wait more than X minutes, etc.) is compared a set threshold limit. If the average wait time has not surpassed the limit, then the operation continues as normal  4701 . If the limit has been surpassed, then it is determined if a new queue is available  4702 . This may be accomplished by storing entity profiles in a database, having such information as how many queues (or check-in stations) may be established. This applies for many aspects of the entity. According to one embodiment, entities may be sent automatic messages requesting such information if it is not known. If it cannot be established that another queue is possible, or that another queue is not possible, then incentives may be sent out to a calculated set of queued persons  4704  if available  4703 . If not, then at least a notification is sent out to the effected parties, including the entity in some embodiments  4705 . 
     If a new queue may be established or an already existing parallel queue does not exist  4706 , then the entity is notified to establish (e.g., man a check-in counter, place or power on an automated check-in means) a parallel queue  4707 . That is unless the entity does not need to perform any actions to instantiate a parallel queue. According to one embodiment, a cloud-based virtual queuing platform may send an electronic signal instantiating a new check-in apparatus/destination/virtual or physical point. For example, the electronic signal may turn on a “lane open” sign and boot an NFC beacon within a turn-style. If it so happens that a turn of events in-fact does not lead to wait times under the threshold, the effected parties may be notified  4705 . However, it is likely that this algorithm combined with the other factors calculated by a cloud-based virtual queuing platform, i.e., the iterative queue simulations solutions, will provide a decreased average wait time. If it is determined that adding a new queue (or that an already existing queue is not at capacity)  4708  than notifications may be sent instructing individuals and groups to adjust accordingly  4709 . The cloud-based virtual queuing platform may be configured to allow an individual or group of individuals to book a time slot in a queue using a mobile device (e.g., smartphone, tablet, smart wearable, etc.); the individual or group can overflow into another queue if it has availability. For example, if two airlines use the same gate, but different security checkpoints, and there is availability at one checkpoint and not the other, the platform can automatically overflow the individual or group to the other checkpoint so they can still book the desired virtual queue time slot. In such a case, a notification may be sent to individuals who have been ‘overflowed’ via various communication channels including, but not limited to, SMS, email, and other messaging applications (e.g., WhatsApp, etc.). 
     Not shown in this diagram are other considerations such as the economic cost of operating additional queues, pandemic considerations such as separating persons by vaccination-status, and other queue-related considerations. According to some embodiments, the wait time threshold  4700  may be compared against the time decreased by adding additional queues  4708 , and if the wait time difference is significant enough, shuffle queued people around 4707 regardless if the new wait time  4708  is under the threshold  4700 . 
       FIG.  48    is a method diagram illustrating a one-time password aspect in a cloud-based virtual queuing platform, according to an embodiment. This figure illustrates one method of implementing an authentication feature. The authentication in this example is a one-time use password that is given to the end-device and to the entity. According to one aspect, the password is only given to the end-device after a successful biometric authentication. According to another aspect, the entity is a business device that a business user uses to manually verify the password with the queued person. For example, a one-time password may be sent to both the queued person and the entity via text, email, or the like. Upon arrival, the queued person reads the one-time password to the business employee. According to one other aspect, the business employee may send a reply message back to a cloud-based virtual queuing platform confirming the queued person checked-in successfully. According to one aspect, the entity is an electronic device that is capable of verifying the one-time password such as a kiosk. 
     According to a first step  4800 , a cloud-based virtual queuing platform receives a request for an appointment or a position in a queue. Requests may be other actions such as to leave a queue, etc. In a second step, the virtual queue may be updated based on the request  4801 . A third step comprises sending a notification of appointment confirmation with one-time password to both an entity and a queued end-device  4802 . Periodic updates may be sent to the end-device per a rule set  4803 . In a fourth step  4804 , an alert is sent to end-device (and the entity in some embodiments) to notify individual their turn is coming up or is up. Individuals are then authenticated using the onetime password via at least one of the implied or explicit methods disclosed herein. A notification of successful check-in may be automatically sent from an entity device or manually sent which is received by the cloud-based virtual queuing platform  4805 . In a sixth step  4806 , the end-device is removed from the virtual queue. 
       FIG.  49    is a block diagram illustrating an exemplary system architecture for a queue manager  4900  with task blending and accumulation, according to an embodiment. According to various embodiments, a queue manager  4900 , features the same functions as in previous embodiments disclosed herein, and further comprises a task blending service  4901  and an accumulation service  4902 . Task blending  4901  is an improved version of call blending found in some call centers. Call blending gives the ability to deliver both inbound and outbound calls seamlessly to an agent, regulating outbound call volume based on inbound traffic. When inbound traffic is low, outbound calls are automatically generated for a specified campaign. When inbound traffic picks up, the dialer dynamically slows the number of outgoing calls to meet the inbound service level. Task blending further improves upon this by additionally redirecting computational resources for other tasks when queue throughput is low. According to various embodiments, callback clouds and cloud-based virtual queuing platforms as disclosed herein may comprise components that employ call blending or task blending. 
     Referring now to  FIG.  50    and  FIG.  52   ), to begin the implementation of a task blending service, queue throughput should be modeled historically and/or predicted  5200 . For example,  FIG.  50    illustrates a queue throughput between the hours of 4 AM and 8 PM  5000 . With a future queue throughput modeled  5001 , low throughput times may be identified  5002 - 5004  and used for call blending and task blending. According to one aspect, queue throughput may be modeled in real time, using calculus to derive instantaneous rates of change that may show the queue flow rate is decreasing. 
     According to one embodiment, low throughput queue flow may be used to trigger the reallocation of computational resources that were previously used for real-time queue simulations—see at least prediction module  3304  features in previous figures—for queue-configuration optimization simulations  5201 . Real-time queue simulations refer to optimizing persons in a queue where the queue configuration is already established. Queue-configuration optimization simulations on the other hand uses queue theory to make recommendations physically and logistically for queues  5202 . It is possible to implement recommended queue reconfigurations while persons are in a queue, but this is not recommended from a customer service standpoint. Furthermore, queue-configuration optimization simulations employ queue theory as well as other considerations. For example, a consideration of the queue&#39;s physical layout in space, the queue&#39;s possible physical arrangements, and the queue&#39;s physical relations to other queues. According to one aspect, determining optimal placement and configurations of many queues may be computationally intensive because it is similar to approximating solutions for the traveling salesman dilemma. 
     Queue theory optimized simulations may make use of Little&#39;s rule which provides the following results: 
     
       
      
       L=λW; L 
       q 
       =λW 
       q  
      
     
     Where λ is the mean rate of arrival and equals 1/E[Inter-arrival-Time], and where E[.] denotes the expectation operator. W is the mean waiting time in the system. L q  is the mean number of customers in the queue. W q  is the mean waiting time in the queue. The first part of the above applies to the system and the second half to the queue, which is a part of the system. 
     Another useful relationship in the queue is: 
         W=W   q +μ
 
     Where μ is the mean service rate and equals 1=E[Service-Time]. The above provides the mean wait in the system which is the sum of the mean wait in the queue and the service time (1/μ). 
     Furthermore, queue theory makes use of at least 4 models as illustrated in  FIG.  51   . In  FIG.  51   , there are four models each with an arrow  5101  representing arrivals, a series of circles representing a queue  5102 , one or more service facilities  5103 - 5106 , and a departure arrow  5107 . The first model is a single-channel, single-phase system  5100   a . The second model is a single-channel, multi-phase system  5100   b . The third model is a multi-channel, single-phase system  5100   c . The fourth model is a multi-channel, multi-phase system  5100   d.    
     Queue theory (the equations and queue models), physical restrictions, physical relations, and other queue data is used to simulate the optimal configuration of one or more queues  5202 . The results of the plurality of simulation are analyzed for the optimal configurations  5203 . These simulations may be provided a set of parameters by host entities. Produced recommendations are delivered via the various communication methods disclosed herein, e.g., web-based, app-based, text, etc.  5204 . Queue-configuration optimization simulations may be computed at any time, not just during off-peak queue times. However, if resources are limited, task blending may be appropriate. 
     An accumulation service  4902  functions to supplement disclosed queue management processes by best reserving discrete positions in a queue for a group. For example, a group of 12 requests placement in a virtual queue, but the virtual queue may not have 12 continuous spots—for a variety of reasons. An accumulation service  4902  will reserve open spots, i.e., accumulate available positions in the queue, until the request is fulfilled. An accumulation service  4902  may call to a queue load balancer  3303  to rearrange persons to expedite the accumulation. This may mean adjusting select individuals for detours or incentives. Another method may be to increase or decrease the current wait time for persons queued back-to-back so that a new slot may be inserted between them. 
       FIG.  53    is a method diagram illustrating an accumulation service used in a cloud-based virtual queuing platform, according to an embodiment. An accumulation  4902  service receives a request to join waitlist from a group  5300  and sends a request acknowledgment to the group once received  5301 . Queue positions are accumulated until the group size is fulfilled  5302 . Accumulated positions are associated with a group object  5303  so the group object may be used in computational methods such as simulations of machine learning neural networks. A confirmation notification is sent to the group when all positions are finished accumulating  5304 . Periodic updates are sent to the group as outlined in similar embodiments disclosed herein  5305 . An alert is sent to notify the group their turn is up, or is coming up  5306 . Notifications of the group&#39;s check-in status may be sent at the initial check-in, during the check-in process (e.g., how many of the  12  how so far processed through), and upon completion of the check-in process  5307 . The group may now be removed from the virtual queue  5308 . 
     Hardware Architecture 
     Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (“ASIC”), or on a network interface card. 
     Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments). 
     Referring now to  FIG.  26   , there is shown a block diagram depicting an exemplary computing device  10  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  10  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  10  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one embodiment, computing device  10  includes one or more central processing units (CPU)  12 , one or more interfaces  15 , and one or more busses  14  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  12  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one embodiment, a computing device  10  may be configured or designed to function as a server system utilizing CPU  12 , local memory  11  and/or remote memory  16 , and interface(s)  15 . In at least one embodiment, CPU  12  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  12  may include one or more processors  13  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some embodiments, processors  13  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  10 . In a specific embodiment, a local memory  11  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  12 . However, there are many different ways in which memory may be coupled to system  10 . Memory  11  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  12  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit. 
     In one embodiment, interfaces  15  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  15  may for example support other peripherals used with computing device  10 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  15  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity AN hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     Although the system shown in  FIG.  26    illustrates one specific architecture for a computing device  10  for implementing one or more of the inventions described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors  13  may be used, and such processors  13  may be present in a single device or distributed among any number of devices. In one embodiment, a single processor  13  handles communications as well as routing computations, while in other embodiments a separate dedicated communications processor may be provided. In various embodiments, different types of features or functionalities may be implemented in a system according to the invention that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). 
     Regardless of network device configuration, the system of the present invention may employ one or more memories or memory modules (such as, for example, remote memory block  16  and local memory  11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the embodiments described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory  16  or memories  11 ,  16  may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein. 
     Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device embodiments may include non-transitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such non-transitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language). 
     In some embodiments, systems according to the present invention may be implemented on a standalone computing system. Referring now to  FIG.  27   , there is shown a block diagram depicting a typical exemplary architecture of one or more embodiments or components thereof on a standalone computing system. Computing device  20  includes processors  21  that may run software that carry out one or more functions or applications of embodiments of the invention, such as for example a client application  24 . Processors  21  may carry out computing instructions under control of an operating system  22  such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE OSX™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services  23  may be operable in system  20 , and may be useful for providing common services to client applications  24 . Services  23  may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system  21 . Input devices  28  may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices  27  may be of any type suitable for providing output to one or more users, whether remote or local to system  20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory  25  may be random-access memory having any structure and architecture known in the art, for use by processors  21 , for example to run software. Storage devices  26  may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to  FIG.  26   ). Examples of storage devices  26  include flash memory, magnetic hard drive, CD-ROM, and/or the like. 
     In some embodiments, systems of the present invention may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to  FIG.  28   , there is shown a block diagram depicting an exemplary architecture  30  for implementing at least a portion of a system according to an embodiment of the invention on a distributed computing network. According to the embodiment, any number of clients  33  may be provided. Each client  33  may run software for implementing client-side portions of the present invention; clients may comprise a system  20  such as that illustrated in  FIG.  27   . In addition, any number of servers  32  may be provided for handling requests received from one or more clients  33 . Clients  33  and servers  32  may communicate with one another via one or more electronic networks  31 , which may be in various embodiments any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the invention does not prefer any one network topology over any other). Networks  31  may be implemented using any known network protocols, including for example wired and/or wireless protocols. 
     In addition, in some embodiments, servers  32  may call external services  37  when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services  37  may take place, for example, via one or more networks  31 . In various embodiments, external services  37  may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in an embodiment where client applications  24  are implemented on a smartphone or other electronic device, client applications  24  may obtain information stored in a server system  32  in the cloud or on an external service  37  deployed on one or more of a particular enterprise&#39;s or user&#39;s premises. 
     In some embodiments of the invention, clients  33  or servers  32  (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks  31 . For example, one or more databases  34  may be used or referred to by one or more embodiments of the invention. It should be understood by one having ordinary skill in the art that databases  34  may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various embodiments one or more databases  34  may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some embodiments, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the invention. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular embodiment herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art. 
     Similarly, most embodiments of the invention may make use of one or more security systems  36  and configuration systems  35 . Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with embodiments of the invention without limitation, unless a specific security  36  or configuration system  35  or approach is specifically required by the description of any specific embodiment. 
       FIG.  29    shows an exemplary overview of a computer system  40  as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system  40  without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU)  41  is connected to bus  42 , to which bus is also connected memory  43 , nonvolatile memory  44 , display  47 , input/output (I/O) unit  48 , and network interface card (NIC)  53 . I/O unit  48  may, typically, be connected to keyboard  49 , pointing device  50 , hard disk  52 , and real-time clock  51 . NIC  53  connects to network  54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system  40  is power supply unit  45  connected, in this example, to a main alternating current (AC) supply  46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices). 
     In various embodiments, functionality for implementing systems or methods of the present invention may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the present invention, and such modules may be variously implemented to run on server and/or client components. 
     The skilled person will be aware of a range of possible modifications of the various embodiments described above. Accordingly, the present invention is defined by the claims and their equivalents.