Patent Publication Number: US-11659086-B2

Title: Tunable chatbots

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application Ser. No. 63/026,561, filed May 18, 2020, entitled “TUNABLE CHATBOTS”, which is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD 
     Embodiments of the present disclosure relate generally to communication methods and specifically to communication methods performed using automated communication resources. 
     BACKGROUND 
     A chatbot is a tool used to conduct real-time or near-real-time conversations via text or text-to-speech and speech-to-text. Chatbots are typically used in dialog systems or contact centers for purposes of providing customer service, request routing, and/or information gathering. The benefit to employing chatbots is that more expensive human agents can be reserved for less routine customer interactions. 
     BRIEF SUMMARY 
     An improved contact center is proposed to include an “agent”, which may be an automated agent or chatbot in a contact center. The contact center may be operated by an entity while the automated agent or chatbot may be provided by a third party other than the operator of the contact center. Furthermore, a contact center may have more than one supplier of automated agents or chatbots. In other words, a contact center is configured to utilize multiple different automated agents or chatbots from a number of different entities. In some embodiments, more than one “model/version” of an automated agent or chatbot may be available to the contact center operator. The contact center operator may want to decide when to use one version/model/supplier of an automated agent or chatbot vs. another version/model/supplier. Situational context, customer preferences, contact type, contact purpose, contact center state, and other contextual factors may be used by the contact center to determine which automated agent or chatbot to assign to a particular contact and/or deploy for a period of time. 
     When matching a caller with live agents, it&#39;s assumed that the agent&#39;s characteristics are generally set. Human agents can be coached to be more friendly, more succinct, more authoritarian-but the effectiveness of real-time coaching can be limited. In comparison, an automated agent or chatbot could expose a set of controls or “dials” where the level of different attributes could be specified in real-time to shape the characteristics of the automated agent for a given call, where a “call” should be understood to describe a voice-based interaction, a video-based interaction, and/or a text-based interaction between the person interacting and the agent. The adjustment of automated agent or chatbot attributes may be selected based on AI modeling by the contact center of what agent characteristic(s) are predicted to yield the best results (e.g., achieve an outcome that satisfies a particular optimization function). 
     One aspect of the present disclosure is to enable a contact center to monitor a customer&#39;s interaction experience in real-time with an automated agent or chatbot and, also in real-time, modify the automated agent or chatbot&#39;s properties/characteristics to improve the customer interaction. 
     Another aspect of the present disclosure enables the contact center to test automated agents or chatbots for a relationship between “dial settings” for characteristics and results observed by monitoring live interactions. The observations of an automated agent or chatbot&#39;s performance may result in defining certain parameters and settings with different agent personalities. For instance, an automated agent or chatbot may be assigned a ‘friendly’ setting at a first dial setting of 0.5 out of 0.0-1.0. As another example, a second dial setting may be used to adjust the automated agent or chatbot&#39;s empathy and the appropriate second dial setting to achieve the desired empathy may be a setting of something different than 0.5 and may also require a different value for the first dial setting. 
     Another aspect of the present disclosure is to provide a list of possible agent characteristics by an AI-agent provider (e.g., a provider and trainer of automated agents or chatbots), which may not necessarily be the same as other AI-agent providers. The difference in AI-agent provider&#39;s agent characteristics may be present by virtue of the AI-agent provider using different training data and/or different feedback responses to train their automated agents or chatbots as compared to other AI-agent providers. The differences between automated agents or chatbots from different AI-agent providers, while subtle, may provide the contact center with a variety of options for assigning different automated agents or chatbots to different interactions depending upon the desired outcome and the status of the interaction. Here, the contact center may use AI-based analysis to correlate the sensitivity of different AI-agents provided by different providers to create a more uniform view of the different AI-agents available from the different AI-agent providers. It should be appreciated that multiple teams (e.g., multiple different AI-agent providers) may work within a company (e.g., entity operating the contact center) to provide multiple automated agents or chatbots to the production system being implemented by the contact center. It would also be possible to configure the automated agents or chatbots to test one another from one team to another team or from within the same team. 
     Another aspect of the present disclosure is to enable a contact center to select particular automated agents or chatbots for use in particular interactions or for different conditions. The contact center may experience a different cost for operating different automated agents or chatbots from different AI-agent providers. This cost of operating information may be considered by the contact center when deciding which particular automated agent or chatbot should be assigned to a particular customer or interaction. It should be appreciated that the contact center may perform a cost-benefit analysis as part of deciding which automated agent or chatbot to assign to an interaction, which means that the best automated agent or chatbot may not always be selected if that also corresponds to the most expensive automated agent or chatbot and the likelihood of customer satisfaction is relatively high with use of a less expensive automated agent or chatbot. Said another way, the contact center may be allowed to perform the cost-benefit analysis as part of a contact routing process. In some embodiments, the contact center may consider whether an automated agent or chatbot corresponds to an in-house our outsourced resource. Automated agents or chatbots that are developed in-house may correspond to a more cost-effective option as compared to an outsourced automated agent or chatbot; however, an outsourced automated agent or chatbot may be better suited to handle a particular type of question raised or attitude shown by a contact. 
     The characteristics of an automated agent or chatbot are often thought of as deterministic. However, it is possible instead to create automated agents or chatbots with a range of a characteristic as a probability curve (e.g., a bell curve around a median value/median experience). Another aspect of the present disclosure is for the contact center to specify the width of the bell curve (e.g., wide vs. ‘tightly controlled’ experience). Another aspect is for the contact center to externally characterize the distribution of the characteristic(s) (e.g., the shape and median of the curve) in order to create a unique experience for the customer as well as to account for uncertainty in the contact center&#39;s characterization of the caller. 
     Another aspect of the present disclosure is to enable the contact center to pre-test contact center agents (e.g., automated agents or chatbots) immediately before selecting an agent for connecting to a live customer. With this capability, the contact center could exercise automated agents or chatbots prior to releasing the live customer to the automated agent or chatbot. Because the pre-test can be performed relatively quickly, the customer will not likely experience any significant delays in service or connection to the selected agent. Some reasons to utilize pre-testing would be to check the characteristics of the agent, to validate that recent optimizations or software updates have kept the agent acceptable, etc. If it seems wasteful or unacceptable to use one automated agent to test another automated agent or chatbot, it should be appreciated that there are possible contractual reasons to have third parties perform the automated agent or chatbot testing (e.g., revenue sharing, assuming liability, volume pricing, etc.) 
     In some embodiments, testing could be done faster than real-time (e.g., at “Hyperspeed”). Hyperspeed testing would be where the speech (or text chat) from the ‘customer’ could be delivered as a file or at a negotiated real-time rate that was higher than normal speech (e.g., 100× or 10 ms per live second). Delivering the content to the automated agent or chatbot in this way may enable hyperspeed testing to be performed without the customer ever realizing such testing was in progress during an interaction. 
     In some embodiments, AI-agent provider could be advised that a particular call is a test call (so don&#39;t include in training set), or could not be advised, so that contact center could shape or weight the supplier&#39;s view of distribution of caller characteristics. As an example, the contact center may plan to move more upsell customers to a particular type of automated agent or chatbot, and so pre-loads/trains the AI-agent provider with a mix of fake and real customer interactions. 
     Pretesting could be made faster for distribution processing by using quantum computing to create fake customer interactions, while quantum computing could be used by the automated agent or chatbot to more quickly provide automated agent or chatbot responses. A combination of quantum computing-powered pretest engagements with quantum computing customers and quantum computing agents could very quickly characterize the automated agent or chatbot by the contact center, or train the agent by the provider. 
     As used herein, automation of a contact center may refer to a behavior of the contact center whereby interactions are managed by an automated agent or chatbot, which may use directed dialog based on rules (e.g., expert systems) or Artificial Intelligence (AI). 
     It should be appreciated that conversations or interactions can be between one or multiple customers and one or multiple agents (e.g., one or multiple automated agents or chatbots and/or one or multiple human agents). In some embodiments, a digital communication channel may be used to facilitate the conversation or interaction. Non-limiting examples of digital communication channels include a communication modality that is configured to enable digital message communications between a customer&#39;s communication device (e.g., a client device, a mobile communication device, a smart phone, a tablet, a PC, a laptop, etc.) and an agent&#39;s communication device (e.g., a client device, a mobile communication device, a smart phone, a tablet, a PC, a laptop, a server executing a chatbot, etc.). In some embodiments, the digital communication channel may include or utilize one, some, or many different communication pathways, protocols, or messaging services. Non-limiting examples of messaging services that may be supported by a digital communication channel include direct digital messaging (e.g., direct chat, SMS text, email, etc.), social media messaging (e.g., social media posts, social media direct messaging, etc.), webchats, or any other communication protocol that enables conversations to occur and be memorialized in non-real-time or near-real-time. It should be appreciated that live voice or video conversations may also be facilitated with automated agents or chatbots without departing from the scope of the present disclosure. 
     Specific, but non-limiting examples of digital communication channels include: (1) “Over-the-Top” (OTT) channels, including. Facebook Messenger, Twitter DM, WeChat, WhatsApp, Apple Business Chat, LINE, KakaoTalk; (2) “Telco” channels, including: SMS and Google RCS; (3) outbound, App-to-Person (“A2P”) messages, that are used by enterprise customers to facilitate various enterprise-specific communications (e.g., notifications, alerts, promotions, etc.); and/or (4) “Real Time Messaging” (RTM) channels, including: In-App messaging (Android, iOS and W/eb), Push messaging (Android, iOS and Browser Notifications), etc. 
     According to some embodiments, automated components of a contact center (e.g., a chatbot engine) may have access to a set of “trained” intents that represent possible topics that a user would engage in with the contact center. Thus, it should be appreciated that different intents may be trained for different conditions as some conditions may support conversations on some topics that other conditions may not support. The training of the intents and construction of the chatbot engine are services contemplated by embodiments of the present disclosure. There may be additional intents that represent certain classes of response that are more generic including yes/no type confirmation intents and others like “banter” that represent polite off topic user messages that need a response, but don&#39;t advance the state of the dialog. There may be dialog structural components that control the scope of the dialog with the user depending on the chatbot engine being used. 
     Interactions or conversations, as used herein, may correspond to two or more message exchanges between two or more entities over a communication channel (e.g., a digital communication channel, a voice communication channel, a video communication channel, or combinations thereof). In some embodiments, an interaction may correspond to a question and response message pair, a question and question message pair, a statement and response message pair, or a set of messages. 
     As used herein, a conversation model may correspond to a data set that is useable in a neural network and that has been trained by one or more data sets that describe conversations or message exchanges between two or more entities. The conversation model may be stored as a model data file or any other data structure that is useable within a neural network or other style AI system. 
     It some embodiments, topics may eventually be uploaded into a Customer Relationship Management (CRM) system and/or customer journey application that is being executed within the contact center. The CRM system and/or customer journey application may be executed on a purpose-built server or may be executed by a server that is also implementing contact routing routines within the contact center. 
     In some embodiments, one or more machine learning models may be provided that encompass the likelihood of: abandon, abort, or requests for escalation to a supervisor. Such models may be used to train the neural networks utilized by the contact center or may be stored and used for comparison to current interactions to determine whether or not the current interaction has any similarities with the machine learning models. Alternatively or additionally, machine learning models may be utilized that can be used to measure “frustration”, “friction”, or “inertia.” If an interaction is determined to have a particular amount of similarity with any such model, then action may be taken within the contact center to avoid further possible frustration, friction, or inertia (e.g., invoking a supervisor assistance function or transferring the contact to a different agent). 
     As used herein, the phrases “at least one,” “one or more,” “or,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. 
     The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably. 
     The term “automatic” and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.” 
     The term “computer-readable medium” as used herein refers to any tangible storage and/or transmission medium that participate in providing instructions to a processor for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, NVRAM, or magnetic or optical disks. Volatile media includes dynamic memory, such as main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read. A digital file attachment to e-mail or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. When the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, the disclosure is considered to include a tangible storage medium or distribution medium and prior art-recognized equivalents and successor media, in which the software implementations of the present disclosure are stored. 
     A “computer readable signal” medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     The terms “determine,” “calculate,” and “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique. 
     It shall be understood that the term “means” as used herein shall be given its broadest possible interpretation in accordance with 35 U.S.C., Section 112, Paragraph 6. Accordingly, a claim incorporating the term “means” shall cover all structures, materials, or acts set forth herein, and all of the equivalents thereof. Further, the structures, materials or acts and the equivalents thereof shall include all those described in the summary of the disclosure, brief description of the drawings, detailed description, abstract, and claims themselves. 
     Aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Any combination of one or more computer readable medium(s) may be utilized. 
     In yet another embodiment, the systems and methods of this disclosure can be implemented in conjunction with a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element(s), an ASIC or other integrated circuit, a digital signal processor, a hard-wired electronic or logic circuit such as discrete element circuit, a programmable logic device or gate array such as PLD, PLA, FPGA, PAL, special purpose computer, any comparable means, or the like. In general, any device(s) or means capable of implementing the methodology illustrated herein can be used to implement the various aspects of this disclosure. Exemplary hardware that can be used for the disclosed embodiments, configurations, and aspects includes computers, handheld devices, telephones (e.g., cellular, Internet enabled, digital, analog, hybrids, and others), and other hardware known in the art. Some of these devices include processors (e.g., a single or multiple microprocessors), memory, nonvolatile storage, input devices, and output devices. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein. 
     Examples of the processors as described herein may include, but are not limited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm® Snapdragon® 610 and 615 with 4G LTE Integration and 64-bit computing, Apple® A7 processor with 64-bit architecture, Apple® M7 motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family of processors, the Intel® Xeon® family of processors, the Intel® Atom™ family of processors, the Intel Itanium® family of processors, Intel® Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nm Ivy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300, and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments® Jacinto C6000™ automotive infotainment processors, Texas Instruments® OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors, ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalent processors, and may perform computational functions using any known or future-developed standard, instruction set, libraries, and/or architecture. 
     In yet another embodiment, the disclosed methods may be readily implemented in conjunction with software using object or object-oriented software development environments that provide portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed system may be implemented partially or fully in hardware using standard logic circuits or VLSI design. Whether software or hardware is used to implement the systems in accordance with this disclosure is dependent on the speed and/or efficiency requirements of the system, the particular function, and the particular software or hardware systems or microprocessor or microcomputer systems being utilized. 
     In yet another embodiment, the disclosed methods may be partially implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like. In these instances, the systems and methods of this disclosure can be implemented as program embedded on personal computer such as an applet, JAVA® or CGI script, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated measurement system, system component, or the like. The system can also be implemented by physically incorporating the system and/or method into a software and/or hardware system. 
     Methods described or claimed herein can be performed with traditional executable instruction sets that are finite and operate on a fixed set of inputs to provide one or more defined outputs. Alternatively or additionally, methods described or claimed herein can be performed using AI, machine learning, neural networks, or the like. In other words, a system or contact center is contemplated to include finite instruction sets and/or artificial intelligence-based models/neural networks to perform some or all of the steps described herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating a communication system in accordance with at least some embodiments of the present disclosure; 
         FIG.  2    is a block diagram depicting additional details of a chatbot engine in accordance with at least some embodiments of the present disclosure; 
         FIG.  3   . Illustrates a number of chatbots and their associated characteristic ranges in accordance with at least some embodiments of the present disclosure; 
         FIG.  4    illustrates a tunable chatbot in accordance with at least some embodiments of the present disclosure; 
         FIG.  5    illustrates a number of different chatbots trained with different training data in accordance with at least some embodiments of the present disclosure; 
         FIG.  6    illustrates a number of chatbots being pre-tested for selection in accordance with at least some embodiments of the present disclosure; 
         FIG.  7    is a flow chart illustrating a first example communication method in accordance with at least some embodiments of the present disclosure; 
         FIG.  8    is a flow chart illustrating a second example communication method in accordance with at least some embodiments of the present disclosure; 
         FIG.  9    is a flow chart illustrating a third example communication method in accordance with at least some embodiments of the present disclosure; 
         FIG.  10    is a flow chart illustrating a fourth example communication method in accordance with at least some embodiments of the present disclosure; 
         FIG.  11    is a flow chart illustrating a fifth example communication method in accordance with at least some embodiments of the present disclosure; and 
         FIG.  12    is a flow chart illustrating a sixth example communication method in accordance with at least some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of various embodiments disclosed herein. It will be apparent, however, to one skilled in the art that various embodiments of the present disclosure may be practiced without some of these specific details. The ensuing description provides exemplary embodiments only, and is not intended to limit the scope or applicability of the disclosure. Furthermore, to avoid unnecessarily obscuring the present disclosure, the preceding description omits a number of known structures and devices. This omission is not to be construed as a limitation of the scopes of the claims. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should however be appreciated that the present disclosure may be practiced in a variety of ways beyond the specific detail set forth herein. 
     While the illustrative aspects, embodiments, and/or configurations illustrated herein show the various components of the system collocated, certain components of the system can be located remotely, at distant portions of a distributed network, such as a Local Area Network (LAN) and/or the Internet, or within a dedicated system. Thus, it should be appreciated, that the components of the system can be combined in to one or more devices or collocated on a particular node of a distributed network, such as an analog and/or digital telecommunications network, a packet-switched network, or a circuit-switched network. It will be appreciated from the following description, and for reasons of computational efficiency, that the components of the system can be arranged at any location within a distributed network of components without affecting the operation of the system. 
     Embodiments of the disclosure provide systems and methods for utilizing automated agents or chatbots (referred to herein after as “chatbots” for ease of discussion) to support interactions with human users/customers. A contact center may be provided with the ability to tune various chatbots in real-time to support desired outcomes in particular customer interactions. In some embodiments, the contact center may have access to multiple chatbots and may select a particular chatbot for assignment to an interaction based on known or tested characteristics of the chatbot as compared to other candidate chatbots that have different known or tested characteristics. 
     Various additional details of embodiments of the present disclosure will be described below with reference to the figures. While the flowcharts will be discussed and illustrated in relation to a particular sequence of events, it should be appreciated that changes, additions, and omissions to this sequence can occur without materially affecting the operation of the disclosed embodiments, configuration, and aspects. 
     Referring initially to  FIG.  1   , a communication system  100  will be described in accordance with at least some embodiments of the present disclosure. The communication system  100  is shown to include a communication network  104  that interconnects a contact center  108  and resources thereof with multiple customer communication devices  112 . As the name suggests, a customer communication device  112  may be owned and/or operated by a customer  116 . The customer  116  may correspond to a user or person that interacts with their customer communication device  112  to communicate with a resource of the contact center  108 . Specifically, the contact center  108  may include a number of resources that facilitate customer  116  interactions via one or multiple communication channels presented to and maintained for use by the customer  116  and one or more of their customer communication devices  112 . As shown in  FIG.  1   , a customer  116  may utilize one or multiple customer communication devices  112  to interact with the contact center  108 . Moreover, embodiments of the present disclosure contemplate that the customer  116  may use multiple different customer communication devices  112  to communicate via a single communication channel. As a non-limiting example, a customer  116  may login to a web-based portal or authenticate themselves with a particular chat channel and then utilize the web-based portal or chat channel to communicate with any one of their customer communication devices  112 . As another example, a customer  116  may utilize one communication device  112  for voice communications, but utilize another communication device  112  for text-based communications. 
     A customer communication device  112  may correspond to a computing device, a personal communication device, a portable communication device, a laptop, a smartphone, a personal computer, and/or any other device capable of running an operating system, a web browser, or the like. For instance, a customer communication device  112  may be configured to operate various versions of Microsoft Corp.&#39;s Windows® and/or Apple Corp.&#39;s Macintosh® operating systems, any of a variety of commercially-available UNIX® such as LINUX or other UNIX-like operating systems, iOS, Android®, etc. These customer communication devices  112  may also have any of a variety of applications, including for example, web browser applications, chat applications, social media applications, calling applications, etc. A customer communication device  112  may alternatively or additionally be any other electronic device, such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via communication network  104  and/or displaying and navigating web pages or other types of electronic documents. 
     The communication network  104  can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation SIP, TCP/IP, SNA, IPX, AppleTalk, and the like. Merely by way of example, the communication network  104  may correspond to a LAN, such as an Ethernet network, a Token-Ring network and/or the like; a wide-area network; a virtual network, including without limitation a virtual private network (“VPN”); the Internet; an intranet; an extranet; a public switched telephone network (“PSTN”); an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.9 suite of protocols, the IEEE 802.11 suite of protocols, the Bluetooth® protocol known in the art, and/or any other wireless protocol); and/or any combination of these and/or other networks. 
     The contact center  108  is shown to include one or more computing devices that enable a contact center agent  172  and/or chatbot  152  to interact with a customer  116  via a communication channel established between the customer communication device  112  and the contact center  108 . In particular, the contact center  108  is shown to include a network border device  120  and a number of servers  124 ,  128 ,  132  that enable functionality of the contact center  108 . The network border device  120  may correspond to one or a number of devices that establish and maintain information security for the contact center  108 . The network border device  120 , in some embodiments, may include a Session Border Controller (SBC), a firewall, a Network Address Translator (NAT) device, a protocol converter, or combinations thereof. Because the communication network  104  may be untrusted from the perspective of an operator of the contact center  108 , the network border device  120 , in some embodiments, may be configured to implement security policies or rules. When communications, messages, packets, or the like are received at the network border device  120 , components of the network border device  120  may analyze the received communications, messages, packets, etc. to determine if the contents of the received communications, messages, packets, etc. can be safely passed to other components of the contact center  108 . In some embodiments, all contents that safely pass through the network border device  120  may be transferred to the communication server  128  or routing engine  124  for further analysis and processing (e.g., for inclusion with a particular conversation, for assigning/forwarding to a particular contact center agent  172 , for assigning/forwarding to a particular chatbot  152 , etc.). 
     In some embodiments, each server of the contact center  108  may be configured to perform a particular task or a set of tasks specific to supporting functions of the contact center  108 . For instance, the routing engine  124  may correspond to a server or set of servers that are configured to receive messages from the network border device  120  and make routing decisions for the message(s) within the contact center  108 . The communication server  128  may correspond to a single server or a set of servers that are configured to establish and maintain a communication channel between customers  116  and the contact center  108 . In some embodiments, the routing engine  124  and communication server  128  may work in cooperation to ensure that an appropriate agent  172 , chatbot  152 , set of agents  172 , and/or set of chatbots  152  are assigned to a particular communication channel for purposes of servicing/addressing contacts initiated by customers  116  of the contact center  108 . Specifically, but without limitation, the routing engine  124  may be configured to determine which agent  172  or chatbot  152  should be assigned to a particular communication channel for purposes of answering a customer&#39;s  116  question and/or for purposes of providing a service to the customer  116 . As will be discussed in further detail herein, the routing engine  124  may receive inputs from the contact management server  132  that include information describing a selected chatbot  152  for assigning to a customer  116  contact. 
     The routing engine  124  may provide appropriate signaling to an agent&#39;s communication device  176  that enables the agent&#39;s communication device  176  to connect with the communication channel over which the customer  116  is communicating and to enable the agent  172  to view messages sent by the customer&#39;s communication device  112 , which are eventually assigned to and posted on the appropriate communication channel. As a more specific example, the communication server  128  may establish and maintain a digital chat communication channel that is presented to the customer&#39;s communication device  112  and which enables the customer  116  to send chat messages to the contact center  108  when desired. When messages are received from a customer communication device  112  and assigned to a particular chat communication channel, the routing engine  124  may determine which agent  172  or chatbot  152  will service the customer&#39;s  116  needs (e.g., answer a question, provide a service, etc.) and then connect the selected agent&#39;s communication device  176  and/or chatbot  152  to the same chat communication channel, thereby enabling the agent  172  and/or chatbot  152  to engage in a chat session with the customer  116 . Alternatively or additionally, as will be described in further detail herein, the routing engine  124  may perform a cost-benefit analysis as part of making a routing decision with respect to a chatbot  152 . For example, the routing engine  124  may be configured to consider a cost associated with using a particular chatbot  152  for servicing a customer  116  and weigh that cost against the potential benefit associated with successfully completing the interaction with the customer  116  (e.g., a sales or revenue benefit, a customer satisfaction rating, etc.). 
     It should be appreciated that the routing engine  124  may be configured to connect both a human agent  172  and one or multiple chatbots  152  to the communication channel if it is desirable to allow the automated agent respond to the customer&#39;s  116  messages in a semi-automated fashion (e.g., where the chatbot  152  generates a suggested reply to a message, but a human agent  172  is required to approve or edit the message prior to being transmitted/committed to the communication channel and delivered to the customer communication device  112 ). 
     Although described as a chat server, it should be appreciated that the communication server  128  may be configured to support any number of communication protocols or applications whether synchronous or asynchronous. Non-limiting examples of communication protocols or applications that may be supported by the communication server  128  include the Session Initiation Protocol (SIP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), HTTP secure (HTTPS), Transmission Control Protocol (TCP), Java, Hypertext Markup Language (HTML), Short Message Service (SMS), Internet Relay Chat (IRC), Web Application Messaging (WAMP), SOAP, MIME, Real-Time Messaging Protocol (RTP), Web Real-Time Communications (WebRTC), WebGL, XMPP, Skype protocol, AIM, Microsoft Notification Protocol, email, etc. In addition to supporting text-based communications, the communication server  128  may also be configured to support non-text-based communications such as voice communications, video communications, and the like. 
     Another server or set of servers that may be provided in the contact center  108  is a contact management server  132 . The contact management server  132  may be configured to manage the contacts or work items that exist within the contact center  108  and that represent tasks to be performed by a human agent  172  and/or chatbot  152  in connection with providing a service to a customer  116 . The contact management server  132  may be configured to maintain state information for some or all of the contacts in the contact center  108  at any given point in time. The contact management server  132  may also be configured to manage and analyze historical contacts as part of training and updating automated agents (e.g., one or multiple chatbots  152 ). In some embodiments, the contact management server  132  may maintain state information for human agents  172  in the contact center  108  and may further interact with the routing engine  124  to determine which agents  172  are currently available for servicing a contact and have the appropriate skills for servicing a contact. Likewise, the contact management server  132  may be configured to determine characteristics associated with various different chatbots  152  provided by a chatbot engine  148  and select one chatbot  152  from the plurality of chatbots  152  for assigning to a contact. Again, the assignment decision may be a skill-based assignment decision. Additional capabilities of the contact management server  132  will be described in further detail with respect to operation of a chatbot engine  148  the chatbot tuning  164  and chatbot selector  168 , which are both shown to be provided by the contact management server  132 . 
     While certain components are depicted as being included in the contact management server  132 , it should be appreciated that such components may be provided in any other server or set of servers in the contact center  108 . For instance, components of the contact management server  132  may be provided in a routing engine  124  and/or communication server  128 , or vice versa. Further still, embodiments of the present disclosure contemplate a single server that is provided with all capabilities of the routing engine  124 , the communication server  128 , and the contact management server  132 . 
     The contact management server  132  is shown to include a processor  136 , a network interface  140 , and memory  144 . The processor  136  may correspond to one or many computer processing devices. Non-limiting examples of a processor include a microprocessor, an Integrated Circuit (IC) chip, a General Processing Unit (GPU), a Central Processing Unit (CPU), or the like. Examples of the processor  136  as described herein may include, but are not limited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bit computing, Apple® A7 processor with 64-bit architecture, Apple® M7 motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family of processors, the Intel® Xeon® family of processors, the Intel® Atom™ family of processors, the Intel Itanium® family of processors, Intel® Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nm Ivy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300, and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments® Jacinto C6000™ automotive infotainment processors, Texas Instruments® OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors, ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalent processors, and may perform computational functions using any known or future-developed standard, instruction set, libraries, and/or architecture. 
     The network interface  140  may be configured to enable the contact management server  132  to communicate with other machines in the contact center  108  and/or to communicate with other machines connected with the communication network  104 . The network interface  140  may include, without limitation, a modem, a network card (wireless or wired), an infra-red communication device, etc. 
     The memory  144  may include one or multiple computer memory devices. The memory  144  may be configured to store program instructions that are executable by the processor  136  and that ultimately provide functionality of the communication management server  132  described herein. The memory  144  may also be configured to store data or information that is useable or capable of being called by the instructions stored in memory  144 . One example of data that may be stored in memory  144  for use by components thereof is training data  160 . 
     The memory  144  may include, for example, Random Access Memory (RAM) devices, Read Only Memory (ROM) devices, flash memory devices, magnetic disk storage media, optical storage media, solid-state storage devices, core memory, buffer memory devices, combinations thereof, and the like. The memory  144 , in some embodiments, corresponds to a computer-readable storage media and while the memory  144  is depicted as being internal to the contact management server  132 , it should be appreciated that the memory  144  may correspond to a memory device, database, or appliance that is external to the contact management server  132 . 
     Illustratively, the memory  144  is shown to store a chatbot engine  148 , which includes a plurality of different chatbots  152 . Each of the chatbots  152  may be configured for execution by the processor  136 . In some embodiments, one or more chatbots  152  may correspond to a set of processor-executable instructions (e.g., a finite instruction set with defined inputs, variables, and outputs). In some embodiments, a chatbot  152  may correspond to an Artificial Intelligence (AI) component of the contact management server  132  that is executed by the processor  136 . The chatbot engine  148 , in some embodiments, may utilize one or more conversation models  156 , which may be in the form of an artificial neural network, for recognizing and responding to messages transmitted by a customer  116  over a communication channel supported by the communication server  128 . In some embodiments, each chatbot  152  of the chatbot engine  148  may be trained with training data  160  and may be programmed to learn from additional conversations as such conversations occur or after conversations occur. In some embodiments, the chatbot engine  148  may update one or more of the conversation models  156  as different chatbots  152  learn from ongoing conversations. In some embodiments, each chatbot  152  provided by the chatbot engine  148  may be configured to provide different attributes, characteristics, or skills. In other words, every chatbot  152  may be different from all other chatbots  152  and may be selected for assigning to a conversation based on the particular skills possessed by each chatbot  152 . 
     In some embodiments, chatbots  152  may be trained or tuned to have a particular characteristic or skill (e.g., friendly, supportive, assertive, knowledgeable for a particular technology, using technical jargon, using layman&#39;s terms, etc.). Chatbots  152  and their characteristics may be tuned using a chatbot tuning  164  function. The chatbot tuning  164  may be adjustable by an operator of the contact center  108  or may be automatically adjusted based on certain contact center  108  conditions (e.g., call volume, customer level, etc.). In addition to providing chatbot  152  tuning capabilities, a chatbot selector  168  may also be provided to select one chatbot  152  from the plurality of chatbots  152  for assigning to a particular customer, to a particular interaction, or the like. Enabling a chatbot tuning  164  and chatbot selector  168  to work simultaneously may enable the contact center  108  to specifically tune a particular, selected, chatbot  152  for a particular customer or skill requirement. That is, a specific chatbot  152  may be selected for assigning to a customer  116  and then the chatbot tuning  164  may be used to appropriately tune the selected chatbot  152  prior to enabling the chatbot  152  to engage in the interaction with the customer  116 . 
     As mentioned above, the chatbot engine  148  and the chatbots  152  provided thereby may be configured to operate using a set of guidelines (e.g., as a set of static instructions) or by using machine learning. Further details of a chatbot engine  148  utilizing machine learning will now be described with reference to  FIG.  2   . Again, the chatbot engine  148  utilizing machine learning may have access to training data  160  to initially train behaviors of the chatbots  152 . The chatbot engine  148  may also be configured to learn from further conversations based on feedback, which may be provided in an automated fashion (e.g., via a recursive learning neural network) and/or a human-provided fashion (e.g., by a human agent  172  confirming or denying that a particular response prepared by the chatbot  152  was appropriate for a particular message received from a customer  116 ). 
     A learning/training module  204  of the chatbot engine  148  may have access to and use one or more conversation models  156 . The conversation models  156  may be built and updated by the training/learning module  204  based on the training data and feedback. The learning/training module  204  may also be configured to access information from a reporting database  212  for purposes of building a bot response database  216 , which effectively stores bot responses that have been previously provided by the chatbot engine  148  and have been identified as valid or appropriate under the circumstances (e.g., based on a positive response from a customer  116  and/or based on administrative user inputs). In some embodiments, each individual chatbot  152  may have its own bot response database  216  and may be trained with its own unique training data  160 . Responses within the bot response database  216  may constantly be updated, revised, edited, or deleted by the learning/training module  204  as a selected chatbot  152  engages in more conversations with customers  116 . 
     In some embodiments, the chatbot engine  148  may include a recommendation engine  208  for each selected chatbot  152  that has access to the bot response database  216  and selects appropriate response recommendations from the bot response database  216  based on dialog inputs  224 . Inputs  224  may include real-time chat data, voice data, video data, as well as outputs of the chatbot tuning  164 . The chatbot tuning  164  may be used to specifically tune or adjust characteristics of a selected chatbot  152 . 
     The real-time chat data may correspond to the content of a message received on a communication channel from a customer&#39;s communication device  112 . Using the inputs  224  and the bot response database  216 , the selected chatbot recommendation engine  208  may be configured to recommend one or multiple responses to a response generator  220 . The response generator  220  may be configured to provide a selected response as a chatbot output for sending on the appropriate communication channel. The interactions between the chatbot engine  148  (and the selected chatbot  152  operating therein) and customer  116  may represent a contact within the contact center  108  and the status of the contact may be determined by agent monitoring functionality, which updates the conversation state accordingly. In some embodiments, the chatbot engine  148  may be configured to interact with a customer  116  in such a way that the customer  116  is unaware they are interacting with an automated agent, but rather think themselves to be interacting with a human agent  172 . 
     To achieve this capability, the chatbot engine  148  and each of the chatbots  152  provided therein may constantly be provided training data from conversations between human agents  172  and customers  116  that have occurred over a particular communication channel. It may be possible to train a chatbot  152  to have a particular skill (e.g., by training the chatbot  152  with conversations related to a particular topic such as sales, product troubleshooting, billing, reservation assistance, etc.). Accordingly, it should be appreciated that the contact center  108  may include a plurality of chatbots  152  of different types without departing from the scope of the present disclosure and each of the chatbots  152  may be selected by the chatbot selector  168  for assigning to a particular customer  116  and interaction. 
     With reference now to  FIGS.  3 - 6   , various capabilities and further functionality of chatbots  152  will be described in accordance with at least some embodiments of the present disclosure. In some embodiments, a chatbot  152  may include a characteristic range  304  and a defined skill set  312 . When a chatbot  152  is selected and assigned to a customer interaction by the chatbot selector  168 , the chatbot tuning  164  may tune the selected chatbot  152  to a current characteristic selection  308 . The current characteristic selection  308  may correspond to a particular characteristic setting that is within the chatbot&#39;s  152  characteristic range  304 . In some embodiments, the characteristic range  304  and/or skill set  312  for each chatbot  152  may be created by training the chatbot  152  with different training data  504 . As shown in  FIG.  5   , each chatbot  152  provided by the chatbot engine  148  may be trained with different training data  504 , thereby enabling each chatbot  148  to exhibit different characteristic ranges  304 . 
     In some embodiments, the chatbot selector  168  may select a particular chatbot  152  for assignment to a customer  116  based on a perceived personality trait  320  that will match or satisfy a customer&#39;s  116  communication preferences. In some embodiments, the chatbot tuning  164  may further tune  404  different personality characteristics  316  of a selected chatbot  152  in an attempt to meet the customer&#39;s  116  desired personality trait  320 . Specifically, the chatbot selector  168  may be configured to determine a customer personality trait  320  that is more likely than not to be found desirable by the customer  116  that initiated the contact. The identified personality traits P 1 , P 2 , P 3 , . . . , etc. may be determined to have a particular value (e.g., a trait of polite may have a value of very polite, mildly polite, impolite, etc.) and a corresponding importance (e.g., very important, somewhat important, not important) for the personality trait. In the chart of  FIG.  3   , the position of each bar on a personality trait chart may represent the particular value of the personality trait whereas the magnitude/size of the bar represents the corresponding importance for the personality trait. 
     As shown in  FIGS.  3  and  4   , each personality characteristic  316  available to be provided by a chatbot  152  may be tunable  404  within the characteristic range  304 . In some embodiments, the characteristic range  304  of a chatbot  152  may first be established by training the chatbot  152  and then the characteristic range  304  may move or adjust over time as the chatbot  152  is further trained and updated while in use (e.g., based on training feedback). The characteristic range  304  of a chatbot  152  may be represented by a probability curve having a corresponding mean, median, standard deviation, width, etc. The ability of a chatbot  152  to provide a particular personality characteristic  316  may not be exact and, therefore, is better represented as a probability curve. In some embodiments, a width of the probability curve may represent a chatbot&#39;s  152  ability to provide a particular personality characteristic with a determined degree of confidence. For instance, the first chatbot  152  of  FIG.  3    is shown to have a relatively wide probability curve for the first personality characteristic  316 , but a narrower probability curve for the second personality characteristic  316 . This means that the first chatbot  152  is more likely to be able to provide the mean/average value of the second personality characteristic  316  than the mean/average value of the first personality characteristic  316 . A narrower probability curve for any personality characteristic  316  may also reflect that the chatbot  152  is not likely to be tunable  404  for that particular personality characteristic  316 . In other words, a wider probability curve for a personality characteristic  316  may suggest that the chatbot  152  can have that particular personality characteristic  316  tuned by the chatbot tuning  316  more than some other personality characteristic  316  with a narrower probability curve. 
     All of the parameters of a probability curve for each personality characteristic  316  may effectively represent the chatbot&#39;s  152  characteristic range  304 . Moreover, if a chatbot  152  has a large number of different personality characteristics  316  and each personality characteristic  316  has a corresponding characteristic range  304  with a wide probability curve, then the chatbot  152  may be considered a relatively flexible chatbot  152 , but the chatbot  152  may not necessarily provide any particular personality characteristic  316  with a high degree of confidence. On the other hand, a chatbot  152  having one or two particular personality characteristics  316  with a relatively narrow probability curve for the personality characteristics  316  may be more likely to provide the personality characteristic  316  during an interaction with a high degree of confidence. 
     In some embodiments, a personality characteristic  316  of a chatbot  152  may be matched to one or more personality traits  320  desired by a customer  116  and chatbots  152  may be selected by the chatbot selector  168  on this basis. Using the non-limiting example of  FIG.  3   , a customer  116  may be found to have six personality traits  320  that are found desirable with corresponding values and importance levels. Based on the magnitude of the bars, personality traits P 1 , P 3 , and P 5  are identified as the most important personality traits  320  for the customer  116  and the position of each bar represents the value of the personality traits  320 . The chatbot selector  168  may refer to personality characteristics of all chatbots  152  available for an interaction with the customer  116  and the personality characteristics  316  of each chatbot  152  to determine which chatbot  152  will be most likely to interact with the customer  116  in a satisfactory manner. Based on the importance of personality traits  320  identified for the customer  116 , the chatbot selector  168  will look for chatbots  152  capable of providing the desired personality trait  320  by analyzing the personality characteristics  316  for the chatbots  152 . A chatbot  152  not likely to provide a personality characteristic of P 1 , P 3 , and P 5  will not likely be chosen for the customer interaction if another chatbot  152  that is likely to provide one or more of the personality characteristics P 1 , P 3 , and P 5  is available. In the example of  FIG.  3   , the first chatbot  152  does not have the personality characteristic  316  for the fifth personality trait P 5  whereas the second chatbot  152  does not have the personality characteristic  316  for the third personality trait P 3 . However, the second chatbot  152  does have the ability (as represented by the characteristic range  304 ) to provide the first personality trait P 1  and fifth personality trait P 5  with a high degree of confidence (because of the narrow probability curve for those personality traits) whereas the first chatbot  152  does not have as narrow a probability curve for the first personality trait P 1 . This means that the chatbot selector  168  may select the second chatbot  152  for interacting with the customer  116  rather than the first chatbot  152  since the second chatbot  152  can more likely provide the desired personality characteristics  316  that satisfy the first and fifth personality traits, which are identified as important to the customer  116 . 
     As the customer  116  interacts with the selected chatbot  152 , the chatbot tuning  164  may analyze the customer&#39;s  116  reactions to the outputs provided by the chatbot  152  to determine if the interaction is progressing positively (e.g., moving from one state to the next toward resolution), negatively (e.g., the customer  116  is exhibiting frustration), or stalled (e.g., the interaction is not moving from one state to the next toward resolution). If the chatbot tuning  164  detects that the interaction is progressing negatively or stalled, then the chatbot tuning  164  may attempt to tune  404  one or more of the personality characteristics  316  of the selected chatbot  152 . As mentioned above, a chatbot  152  having a wider probability curve for a particular personality characteristic  316  may be more flexibly tuned to different values. On the other hand, a chatbot  152  having a narrower probability curve for a particular personality characteristic  316  may not be as flexibly tuned to provide different values. As shown in  FIG.  4   , certain chatbots  152  may not be capable of having their personality characteristics  316  tuned to provide a certain value of a personality trait  320  (e.g., the third personality trait P 3  at the desired value). 
     In some embodiments, each personality characteristic  316  may match a particular personality trait  320  desired by a customer  116 . A personality characteristic  316  of a chatbot  152  may meet the customer&#39;s  116  personality trait  320  in some instances where the probability curve of the corresponding personality characteristic  316  overlaps with the value defined for the customer&#39;s desired personality trait  320 . A personality characteristic  316  (or personality trait  320  if viewed from the perspective of the customer&#39;s  116  preference) may correspond to assertive, passive, quiet, permissive, conscientious, agreeable, extraverted, and/or introverted. Each personality trait  320  may have a corresponding personality characteristic  316  or may be mapped to a combination of different personality characteristics  316  provided by a chatbot  152 . 
     As shown in  FIGS.  5  and  6   , because each chatbot  152  may be trained with different training data  504 , each chatbot  152  may be configured to provide different chatbot responses  608  to a common input from a customer interaction  604 . With this information in mind, embodiments of the present disclosure contemplate the ability to test one or more chatbots  152  prior to assigning a chatbot  152  to a particular customer interaction. Specifically, the chatbot selector  168  may be configured to provide an initial input from a customer interaction  604  to a plurality of different chatbots  152 . Each chatbot  152  may provide a candidate chatbot response  608 , which has an anticipated benefit  612  associated therewith. The chatbot selector  168  may analyze the plurality of different chatbot responses  608 , associated benefits  612  of the chatbot response  608 , and a possible cost  616  of selecting a chatbot  152  for use in a customer interaction as part of selecting a chatbot  152 . The input from the customer interaction  604  may correspond to a text, voice, or video input (depending upon the communication channel used by the customer  116  to contact the contact center  108 ). The candidate chatbot response  608  may be in text, voice, or video format as well. 
     The chatbot selector  608  may be configured to apply an optimization function when selecting one chatbot  152  from the plurality of chatbots for a customer interaction. The chatbot selector  168  may use the optimization function to balance the anticipated benefit  612  of one candidate chatbot response  608  against anticipated benefits  612  of other chatbot responses  608 . In some embodiments, the chatbot selector  168  may select the chatbot  152  that provides a candidate chatbot response  608  with the greatest anticipated benefit  612 . The chatbot selector  168  may alternatively or additionally balance the cost  616  of using one chatbot  152  against the costs  616  of using other chatbots  152 . In some embodiments, the chatbot selector  168  may select the chatbot  152  that provides the least cost  616  of use for a given customer interaction. The chatbot selector  168  may alternatively or additionally weigh the costs and benefits associated with each chatbot  152  and may apply an optimization function that maximizes expected revenue, maximizes expected profit, minimizes cost, maximized customer satisfaction, etc. In other words, the optimization function applied by the chatbot selector  608  may consider any number of factors associated with using different chatbots  152  as part of selecting a chatbot  152  for assignment to a customer interaction. 
     In some embodiments, the candidate chatbot responses  608  may be received and analyzed by the chatbot selector  168  in real-time or near-real-time. As an example, if the input from the customer interaction  604  is received via a text-based communication channel (e.g., chat, social media post, etc.), then text-based candidate chatbot responses  608  may all be analyzed prior to the chatbot selector  168  selecting a particular chatbot  152  for assignment to the interaction and committing the selected chatbot&#39;s  152  response back to the customer  116 . The analysis performed by the chatbot selector  168  may be done in the background and quickly enough that the response is returned to the customer  116  without the customer observing any significant delay in response time. In other words, the chatbot selector  168  may select a chatbot  152  quickly enough to make it appear as though the chatbot  152  or a human agent represented by the chatbot  152  was selected prior to receiving the input from the customer interaction  604 . In another example, but relatedly, if the input from the customer interaction  604  is received via voice, then voice-based candidate chatbot responses  608  (or text versions of possible voice-based responses) may be analyzed by the chatbot selector  168  prior to selecting a particular chatbot  152  for assignment to a customer interaction. 
     With reference now to  FIGS.  7 - 12   , various communications methods will be described in accordance with at least some embodiments of the present disclosure. The communication methods described herein may be combined with one another or steps from one communication method may be used in another communication method without departing from the scope of the present disclosure. Moreover, while certain methods and steps of method will be described as being performed by particular system  100  components, it should be appreciated that any of the components described herein can be configured to perform some or all of the method steps depicted and described herein. Further still, while certain methods are described as having method steps performed in a certain order, it should be appreciated that the order of method steps may be changed or altered without departing from the scope of the present disclosure. 
     Referring now to  FIG.  7   , a first communication method will be described in accordance with at least some embodiments of the present disclosure. The method begins with a contact center  108  maintaining a plurality of chatbots  152  with different skill sets  312  and different personality characteristics (step  704 ). Each chatbot  152  may be trained with different training data  504 , thereby resulting in each chatbot  152  having different skill sets  312  and personality characteristics. The personality characteristic of a chatbot  152  may be represented by a personality characteristic range  304 , which means that a chatbot  152  is capable of providing a particular personality characteristic with a degree of confidence. 
     The method continues when a contact is received at the contact center  108  from a customer communication device  112  (step  708 ). The contact may be text-based, voice-based, video-based, web-based, social media-based, etc. The customer communication device  112  may send one or more messages to the contact center  108  via one or more different communication channels. Upon receiving the message(s), the contact center  108  may establish a customer interaction or workflow that facilitate resolution of the customer inquiry (e.g., the reason why the customer  116  contacted the contact center  108 ). In some embodiments, the contact center  108  may utilize the contact management server  132  to determine a skill or skills associated with the contact (step  712 ). The skill may be determined based on the content of the message(s) provided by the customer  116 , based on a knowledge of the customer  116  from a CRM database, based on a customer&#39;s  116  self-selection of a reason for the contact (e.g., service, sales, trouble shooting, technical support, billing, etc.). 
     The method may continue with the chatbot selector  168  comparing the skill(s) associated with/required by the contact with the skill set  312  of each chatbot  152  in the contact center  108  (step  716 ). The chatbot selector  168  may also determine, if possible, personality trait(s)  320  that are most likely to be desirable or enjoyed by the customer  116  (step  720 ). The determination of desirable personality traits  320  may be based upon the content of the contact, based on historical customer information obtained from a CRM database, based on customer  116  responses to questions posed during a customer survey, etc. 
     The chatbot selector  168  may then compare the personality trait(s)  320  determined to be possibly desirable by the customer  116  with personality characteristics  316  of the plurality of chatbots  152  (step  724 ). In some embodiments, the chatbot selector  168  may analyze the characteristic ranges  304  of each chatbot  152  as well as the various probability curves associated with different personality characteristics  316  to determine if any chatbot  152  is more likely than other chatbots  152  to provide the personality traits  320  desired by the customer  116 . 
     Based on the comparisons performed in steps  716  and  724 , the chatbot selector  168  may select a chatbot  152  from the plurality of chatbots (step  728 ). The selected chatbot  152  may then be allowed to engage in an interaction with the customer  116  by sending responses to the customer communication device  112 , by receiving further customer messages, and then responding to those further customer messages (step  732 ). In some embodiments, the chatbot  152  may engage in the customer interaction and attempt to progress the interaction through a number of states toward resolution of the customer&#39;s inquiry. It should be appreciated that the chatbot selector  168  may be configured to weigh skill-based matching between a chatbot  152  and customer contact over personality characteristic matching considerations. Such weighting may be achieved by enabling the chatbot selector  168  to apply an optimization function that simultaneously considers both skill-based matching and personality characteristic matching. If no chatbot  152  is available to provide the required skills for the customer  116 , then the chatbot selector  168  may only consider personality characteristic matching. Analogously, if two chatbots  152  possess the required skills to service a customer interaction, then the chatbot selector  168  may choose between the two chatbots  152  based on which chatbot  152  has personality characteristics  316  that more likely will match the customer&#39;s  116  desired personality traits  320 . 
     Referring now to  FIG.  8   , a second communication method will be described in accordance with at least some embodiments of the present disclosure. The method begins by determining a personality trait  320  that is more likely than not to be found desirable by a customer  116  during the customer&#39;s  116  interactions with the contact center  108  (step  804 ). The determination of a personality trait  320  may be based upon content of the customer&#39;s  116  message, based upon information received from a customer  116  response to a survey question, based upon information obtained from historical customer information (e.g., from a CRM database), or combinations thereof. 
     The method continues with the chatbot tuning  164  adjusting the current characteristic selection  308  provided by the chatbot  152  that is interacting with the customer  116  or that has been selected for assignment to the customer interaction (step  808 ). In some embodiments, the chatbot tuning  164  is configured to tune  404  one or multiple different personality characteristics  316  of a selected chatbot  152 . The tuning process may be performed in an attempt to have the chatbot  152  provide a personality characteristic or combination of personality characteristics that match the personality trait(s) identified in step  804 . 
     The chatbot tuning  164  may be configured to observe and analyze interactions between the selected chatbot  152  and customer  116  (step  812 ) to determine if an appropriate match between personality characteristics  316  and personality traits  320  is being achieved. Specifically, the method may include determining if further chatbot tuning is required (step  816 ). Further tuning may be required if the interactions are determined to be negative or stalled, both of which may result in customer  116  frustration. Further tuning may not be required if the interactions are progressing from one state to the next toward resolution. If further tuning is required, the method may revert back to step  808 . If no further tuning is required, possibly because the chatbot  152  is providing the appropriate type of personality characteristics  316  and the current characteristic selection  308  is satisfying the customer&#39;s  116  needs, then the method may continue by determining if the customer interactions are complete (step  820 ). If the customer interactions are not yet complete, then the method returns to step  816 . If the customer interactions are complete, then the chatbot tuning may be finalized and the method may proceed by providing any additional training or feedback to the chatbot  152  that was involved in the customer interactions (step  824 ). This additional step of updated chatbot training may correspond to an optional step. 
     Referring now to  FIG.  9   , a third communication method will be described in accordance with at least some embodiments of the present disclosure. The method begins with a chatbot selector  168  comparing one or more skills required to service a customer contact with a skill set  312  and/or personality characteristics of chatbots  152  available for use in a contact center  108  (step  904 ). The chatbot selector  168  may analyze, for each of the plurality of chatbots  152 , a characteristic range  304 , which may be represented by a probability curve (step  908 ). 
     The chatbot selector  168 , may further specify a parameter of the probability curve as a parameter of interest when analyzing the probability curves for each candidate chatbot  152  (step  912 ). Parameters of interest may depend upon the nature of the probability curve. A bell curve, for example, may have parameters that include a mean, median, mode, width, standard deviation, degree of symmetry, peak, etc. One of more of the parameters of interest may be used to analyze and select one chatbot  152  from among the plurality of chatbots  152  for assignment to a customer interaction. Specifically, the method may include determining a likelihood that a personality characteristic  316  can be provided by each of the chatbots  152  under analysis. The likelihood that a personality characteristic  316  can be provided by a chatbot  152  may depend upon the parameter of interest for the probability curve under analysis. Analyzing the same parameter of interest for each chatbot&#39;s  152  probability curve may enable the chatbot selector  168  to accurately and appropriately select a particular chatbot  152  for assigning to a customer interaction. The analysis performed in this method may also enable the chatbot selector  168  to more likely select a chatbot  152  that provides an appropriate set of personality characteristics to match personality traits desirable for the customer  116 . 
     Referring now to  FIG.  10   , a fourth communication method will be described in accordance with at least some embodiments of the present disclosure. This method starts by receiving a chatbot output from a chatbot  152  assigned to a customer interaction (step  1004 ). The output received from the chatbot  152  may be held (e.g., not provided to the customer  116 , transmitted to the customer communication device  112 , stored in buffer memory, etc.) until a further verification process is performed (step  1008 ). 
     The method may continue, while the chatbot  152  output is held, by providing a copy of the chatbot  152  output to a human agent  172  for analysis and verification (step  1012 ). In some embodiments, the human agent  172  may analyze some or all of the chatbot  152  output to determine if the chatbot  152  output sufficiently addresses or responds to the last customer  116  input received during the customer interaction. In some embodiments, the human agent  172  may analyze the chatbot  152  output for accuracy, relevancy, content, and any other subject or objective criteria. For instance, the human agent  172  may analyze the chatbot  152  output to determine if an appropriate personality characteristic is being applied to match a determined personality trait desired by the customer  116 . 
     The method will continue if the human agent  172  provides an input back to the system verifying that the output of the chatbot  152  is acceptable to transmit to the customer  116  (step  1016 ). In some embodiments, the human agent  172  may be allowed to edit or modify the output of the chatbot  152  and the edits provided by the human agent  172  may be used as feedback to the system to improve learning and the overall performance of the chatbot  152 . 
     After the human input has been received indicating that the chatbot  152  output is acceptable, the method may continue by transmitting the output of the chatbot  152  to the customer  116  (step  1020 ). In some embodiments, the output transmitted to the customer  116  may correspond to the edited version or to whatever version of the output that was approved by the human agent  172 . 
     Referring now to  FIG.  11   , a fifth communication method will be described in accordance with at least some embodiments of the present disclosure. This method begins by receiving a message from a customer communication device  112  (step  1104 ). The message may be received on any communication channel supported by the contact center  108  and may use any suitable communication modality (e.g., text, voice, video, social media, web collaboration, etc.). 
     Prior to selecting a chatbot  152  for responding to the message received from the customer communication device  112 , the chatbot selector  168  may provide some or all of the message contents to a plurality of chatbots  152  (step  1108 ). In some embodiments, the content provided to each chatbot  152  in the plurality of chatbots is the same and may correspond to the entire input from the customer interaction  604 . 
     Each chatbot  152  may produce a candidate output  608  based on the input received from the customer interaction  604  (step  1112 ). The chatbot selector  168  may be configured to analyze each candidate output  608  including anticipated benefits  612  associated with each candidate output  608  (step  1116 ). The chatbot selector  168  may also determine costs  616  associated with using each chatbot  152  and compare the anticipated benefits  612  against costs  616  for some or all of the chatbots  152  (step  1120 ). In some embodiments, the chatbot selector  168  may apply an optimization function to select a chatbot  152  from the plurality of chatbots (step  1124 ). As an example, the chatbot selector  168  may select the chatbot  152  that best satisfies the optimization function based on each chatbot&#39;s  152  anticipated benefit  612  and cost  616  of use. 
     The selected chatbot  152  may then be assigned to the customer interaction (step  1128 ). In some embodiments, assignment of the chatbot  152  to the customer interaction may enable the selected chatbot  152  to continue responding to additional messages received from the customer  116 . In some embodiments, each message received from the customer  116  may flow through the method of  FIG.  11    such that a different chatbot  152  is selected to provide an output to each message received from a customer  116 . In this way, no particular chatbot  152  is selected to handle an entire customer interaction, but rather candidate outputs  608  of multiple chatbots  152  are analyzed prior to providing a response to the customer&#39;s message. If desired, it may also be possible to apply appropriate human oversight for the selected chatbot during the customer interaction (step  1132 ). For example, a human agent  172  may review and verify some or all of the responses generated by a chatbot  152  (or different chatbots  152 ) prior to committing those responses to the customer  116 . The inputs from the human agent  172  may be provided back to the chatbot engine  148  as part of retraining or teaching the chatbots  152 . 
     Referring now to  FIG.  12   , a sixth communication method will be described in accordance with at least some embodiments of the present disclosure. The method is initiated when training for one or multiple different chatbots  152  is initiated by the chatbot engine  148  (step  1204 ). The training method may correspond to an initial training method for a new chatbot  152  or to a retraining of a chatbot  152  already in use by the contact center  108 . 
     The training method may continue by providing each chatbot  152  being trained with different training data  504  (step  1208 ). As an example, one chatbot  152  may receive first training data that is specific to a first skill, a first personality characteristic, etc. For instance, the first training data  504  may correspond to contact center interactions (text, voice, video, web collaboration, social media, etc.) related to customer support or technical support for a particular product. The chatbot  152  that is trained on the first training data  504  may be trained to develop a first skill (e.g., customer support skill or technical support skill). Second training data  504  may be applied to a different chatbot  152  to develop a second skill. As an example, the second training data  504  may correspond to contact center interactions related to billing inquiries. The chatbot  152  that is trained on the second training data  504  may be trained to develop a second skill (e.g., billing skill). As another example, yet another chatbot  152  may be trained on third training data  504 , which corresponds to contact center interactions for sales. The chatbot  152  trained with the third training data  504  may develop a third skill (e.g., a sales skill). In this way, different chatbots  152  may be trained to have different skill sets  312 , which may be analyzed during a chatbot selection process. 
     The training method may complete when the chatbot engine  148  confirms that all chatbots  152  have been satisfactorily trained on their respective training data (step  1212 ). Confirmation that training is complete may be based on the chatbot  152  receiving and ingesting all training data  504  and/or confirming that the chatbot  152  now possesses the desired skill and/or personality characteristic. 
     The method may then continue by providing each of the trained chatbots  152  to the contact center  108  for storage and selective use during contact center  108  operations (step  1216 ). In some embodiments, each chatbot  152  may be stored along with information describing the chatbot&#39;s  152  capabilities (e.g., characteristic range  304 , skill set  312 , etc.). The information describing the chatbot&#39;s  152  capabilities may be stored such that the chatbot selector  168  can easily and quickly access the information to make a chatbot selection for customer interactions, newly-received customer messages, and the like. 
     The present disclosure, in various aspects, embodiments, and/or configurations, includes components, methods, processes, systems, and/or apparatus substantially as depicted and described herein, including various aspects, embodiments, configurations embodiments, subcombinations, and/or subsets thereof. Those of skill in the art will understand how to make and use the disclosed aspects, embodiments, and/or configurations after understanding the present disclosure. The present disclosure, in various aspects, embodiments, and/or configurations, includes providing devices and processes in the absence of items not depicted and/or described herein or in various aspects, embodiments, and/or configurations hereof, including in the absence of such items as may have been used in previous devices or processes, e.g., for improving performance, achieving ease and\or reducing cost of implementation. 
     The foregoing discussion has been presented for purposes of illustration and description. The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description for example, various features of the disclosure are grouped together in one or more aspects, embodiments, and/or configurations for the purpose of streamlining the disclosure. The features of the aspects, embodiments, and/or configurations of the disclosure may be combined in alternate aspects, embodiments, and/or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, embodiment, and/or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure. 
     Moreover, though the description has included description of one or more aspects, embodiments, and/or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, embodiments, and/or configurations to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.