TECHNOLOGIES FOR ANALYSIS AND BROADCAST STREAMING IN CONTACT CENTERS TO REDUCE SYSTEM LOADS IN HIGH VOLUME CONTEXTS

A method of analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts according to an embodiment includes analyzing social media content using an artificial intelligence model to identify issues encountered by clients of a contact center, ranking the issues encountered by the clients of the contact center based on a priority of resolving each of the issues, selecting an issue ranked as a highest priority issue, identifying agents best suited to handle the selected issue based on agent skills related to the selected issue, determining a next best available time for a live agent session with a group of agents selected based on respective real-time interaction loads of the agents, and broadcasting a stream of the live agent session at the next best available time through at least one social media channel to the clients of the contact center.

BACKGROUND

In today's social media landscape, users often express their frustration over delayed or inadequate responses by various enterprises and platforms to their concerns. Additionally, during critical periods, contact centers can become overwhelmed with a high volume of clients seeking assistance for similar issues within a short timeframe. This can lead to agent fatigue, as agents struggle to provide repetitive responses and manage the workload, resulting in a poor client experience and increasing the potential for client attrition. For example, if a particular enterprise experiences a system crash resulting in downtime, clients in the affected region may contact the contact center for assistance with workarounds or solutions to resume operations. Even if there is a workaround available, the agents are nonetheless required to attend to each client individually, with each having unique issues that need to be addressed on their end.

SUMMARY

One embodiment is directed to a unique system, components, and methods for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts. Other embodiments are directed to apparatuses, systems, devices, hardware, methods, and combinations thereof for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts.

According to an embodiment, a method of analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts may include analyzing, by a computing system, social media content using an artificial intelligence model to identify a plurality of issues encountered by clients of a contact center, ranking, by the computing system, the plurality of issues encountered by the clients of the contact center based on a priority of resolving each of the plurality of issues, selecting, by the computing system, an issue of the plurality of issues ranked as a highest priority issue, identifying, by the computing system, a plurality of agents best suited to handle the selected issue based on agent skills related to the selected issue, determining, by the computing system, a next best available time for a live agent session with a group of agents selected from the identified plurality of agents based on respective real-time interaction loads of the identified plurality of agents, and broadcasting, by the computing system, a stream of the live agent session at the next best available time through at least one social media channel to the clients of the contact center.

In some embodiments, analyzing the social media content may include analyzing social media content from a plurality of social media systems.

In some embodiments, the method may further include analyzing, by the computing system, at least one of social media comments and reactions of the clients of the contact center to the live agent session, and generating, by the computing system, one or more suggestions for the group of agents based on the analysis of the at least one of social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, generating the one or more suggestions for the group of agents may include retrieving content from a knowledgebase responsive to the at least one of the social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, generating the one or more suggestions for the group of agents may include ranking a priority of addressing the at least one of the social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, the method may further include transmitting, by the computing system, an invitation to the live agent session to clients of the contact center potentially affected by the selected issue in response to determining the next best available time for the live agent session, and broadcasting the stream of the live agent session to the clients of the contact center may include broadcasting the stream of the live agent session to the clients of the contact center potentially affected by the selected issue.

In some embodiments, ranking the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include determining a respective number of the clients of the contact center affected by each of the plurality of issues.

In some embodiments, ranking the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include determining a respective importance of the clients of the contact center affected by each of the plurality of issues.

In some embodiments, ranking the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include determining a respective potential impact of each of the plurality of issues on non-reporting clients of the contact center.

In some embodiments, broadcasting the stream of the live agent session through the at least one social media channel may include receiving, for each social media channel, a respective streaming uniform resource locator (URL) and key of a respective social media system, and relaying a real-time media player (RTMP) stream from an agent desktop of the group of agents to a respective streaming server of the respective social media system using the respective streaming URL and key.

According to another embodiment, a system for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts may include at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to analyze social media content using an artificial intelligence model to identify a plurality of issues encountered by clients of a contact center, rank the plurality of issues encountered by the clients of the contact center based on a priority of resolving each of the plurality of issues, select an issue of the plurality of issues ranked as a highest priority issue, identify a plurality of agents best suited to handle the selected issue based on agent skills related to the selected issue, determine a next best available time for a live agent session with a group of agents selected from the identified plurality of agents based on respective real-time interaction loads of the identified plurality of agents, and broadcast a stream of the live agent session at the next best available time through at least one social media channel to the clients of the contact center.

In some embodiments, to analyze the social media content may include to analyze social media content from a plurality of social media systems.

In some embodiments, the plurality of instructions may further cause the system to analyze at least one of social media comments and reactions of the clients of the contact center to the live agent session, and generate one or more suggestions for the group of agents based on the analysis of the at least one of social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, to generate the one or more suggestions for the group of agents may include to retrieve content from a knowledgebase responsive to the at least one of the social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, to generate the one or more suggestions for the group of agents may include to rank a priority of addressing the at least one of the social media comments and reactions of the clients of the contact center to the live agent session.

In some embodiments, the plurality of instructions may further cause the system to transmit an invitation to the live agent session to clients of the contact center potentially affected by the selected issue in response to a determination of the next best available time for the live agent session, and to broadcast the stream of the live agent session to the clients of the contact center may include to broadcast the stream of the live agent session to the clients of the contact center potentially affected by the selected issue.

In some embodiments, to rank the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include to determine a respective number of the clients of the contact center affected by each of the plurality of issues.

In some embodiments, to rank the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include to determine a respective importance of the clients of the contact center affected by each of the plurality of issues.

In some embodiments, to rank the plurality of issues encountered by the clients of the contact center based on the priority of resolving each of the plurality of issues may include to determine a respective potential impact of each of the plurality of issues on non-reporting clients of the contact center.

In some embodiments, to broadcast the stream of the live agent session through the at least one social media channel may include to receive, for each social media channel, a respective streaming uniform resource locator (URL) and key of a respective social media system, and relay a real-time media player (RTMP) stream from an agent desktop of the group of agents to a respective streaming server of the respective social media system using the respective streaming URL and key.

This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. Further embodiments, forms, features, and aspects of the present application shall become apparent from the description and figures provided herewith.

DETAILED DESCRIPTION

References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. It should be further appreciated that although reference to a “preferred” component or feature may indicate the desirability of a particular component or feature with respect to an embodiment, the disclosure is not so limiting with respect to other embodiments, which may omit such a component or feature. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Further, particular features, structures, or characteristics may be combined in any suitable combinations and/or sub-combinations in various embodiments.

Additionally, it should be appreciated that items included in a list in the form of “at least one of A, B, and C” can mean (A); (B); (C); (A and B); (B and C); (A and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (B and C); (A and C); or (A, B, and C). Further, with respect to the claims, the use of words and phrases such as “a,” “an,” “at least one,” and/or “at least one portion” should not be interpreted so as to be limiting to only one such element unless specifically stated to the contrary, and the use of phrases such as “at least a portion” and/or “a portion” should be interpreted as encompassing both embodiments including only a portion of such element and embodiments including the entirety of such element unless specifically stated to the contrary.

Referring now toFIG.1, a system for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts includes a cloud-based system102, a network104, a contact center system106, a user device108, and a social media system110. Additionally, the illustrative cloud-based system102includes an analysis system112, a scheduler system114, and a broadcasting system116, and the illustrative contact center system106includes an agent device118. Although only one cloud-based system102, one network104, one contact center system106, one user device108, one social media system110, one analysis system112, one scheduler system114, one broadcasting system116, and one agent device118are shown in the illustrative embodiment ofFIG.1, the system100may include multiple cloud-based systems102, networks104, contact center systems106, user devices108, social media systems110, analysis systems112, scheduler systems114, broadcasting systems116, and/or agent devices118in other embodiments. For example, in some embodiments, multiple cloud-based systems102(e.g., related or unrelated systems) may be used to perform the various functions described herein. Further, in some embodiments, one or more of the systems described herein may be excluded from the system100, one or more of the systems described as being independent may form a portion of another system, and/or one or more of the systems described as forming a portion of another system may be independent.

It should be appreciated that the technologies described herein efficiently manage a high volume of contact center clients who may be experiencing similar issues and/or outbound contact center support. In particular, as described in greater detail below, the system100may leverage an artificial intelligence model to scan and rank the social media trending issues, select the agents best suited for handling the trending issues, identify the next best available time at which some of those agents could participate in a live agent session with potentially affected clients, and broadcast a live agent stream through multiple social media channels through various integration hooks to address the trending issues, while allowing for real-time feedback and analysis of the feedback.

The cloud-based system102may be embodied as any one or more types of devices/systems capable of performing the functions described herein. For example, in the illustrative embodiment, the cloud-based system102may be leveraged by the contact center system106or agent thereof in order to provide support for resolving contact center client issues. As described herein, the analysis system112may scan multiple social media channels for a particular organization or enterprise and rank the issues raised on the social media channels based on a priority at which the issues should be addressed. The scheduler system114may schedule a live agent session across social media platforms and organization web channels with agents having the appropriate skills based on the ranked issues. The broadcasting system116may broadcast a live agent session across the social media platforms. It should be appreciated that an agent supervisor and/or AI bot may guide agents with tips/recommendations during the live agent session based on the agent actions and/or client comments and reactions.

Although the cloud-based system102is described herein in the singular, it should be appreciated that the cloud-based system102may be embodied as or include multiple servers/systems in some embodiments. Further, although the cloud-based system102is described herein as a cloud-based system, it should be appreciated that the system102may be embodied as one or more servers/systems residing outside of a cloud computing environment in other embodiments. In cloud-based embodiments, the cloud-based system102may be embodied as a server-ambiguous computing solution similar to that described below.

In some embodiments, one or more of the analysis system112, the scheduler system114, and/or the broadcasting system116may be embodied as or include an independent module or sub-system of the cloud-based system102. Further, in some embodiments, one or more of the analysis system112, the scheduler system114, and/or the broadcasting system116may be integrated with one or more components or sub-systems of the cloud-based system102.

The network104may be embodied as any one or more types of communication networks that are capable of facilitating communication between the various devices communicatively connected via the network104. As such, the network104may include one or more networks, routers, switches, access points, hubs, computers, and/or other intervening network devices. For example, the network104may be embodied as or otherwise include one or more cellular networks, telephone networks, local or wide area networks, publicly available global networks (e.g., the Internet), ad hoc networks, short-range communication links, or a combination thereof. In some embodiments, the network104may include a circuit-switched voice or data network, a packet-switched voice or data network, and/or any other network able to carry voice and/or data. In particular, in some embodiments, the network104may include Internet Protocol (IP)-based and/or asynchronous transfer mode (ATM)-based networks. In some embodiments, the network104may handle voice traffic (e.g., via a Voice over IP (VOIP) network), web traffic (e.g., such as hypertext transfer protocol (HTTP) traffic and hypertext markup language (HTML) traffic), and/or other network traffic depending on the particular embodiment and/or devices of the system100in communication with one another. In various embodiments, the network104may include analog or digital wired and wireless networks (e.g., IEEE 802.11 networks, Public Switched Telephone Network (PSTN), Integrated Services Digital Network (ISDN), and Digital Subscriber Line (xDSL)), Third Generation (3G) mobile telecommunications networks, Fourth Generation (4G) mobile telecommunications networks, Fifth Generation (5G) mobile telecommunications networks, a wired Ethernet network, a private network (e.g., such as an intranet), radio, television, cable, satellite, and/or any other delivery or tunneling mechanism for carrying data, or any appropriate combination of such networks. The network104may enable connections between the various devices/systems102,106,108,110,112,114,116,118of the system100. It should be appreciated that the various devices/systems102,106,108,110,112,114,116,118may communicate with one another via different networks104depending on the source and/or destination devices/systems102,106,108,110,112,114,116,118.

The contact center system106may be embodied as any system capable of providing contact center services (e.g., call center services) to an end user and otherwise performing the functions described herein. Depending on the particular embodiment, it should be appreciated that the contact center system106may be located on the premises/campus of the organization utilizing the contact center system106and/or located remotely relative to the organization (e.g., in a cloud-based computing environment). In some embodiments, a portion of the contact center system106may be located on the organization's premises/campus while other portions of the contact center system106are located remotely relative to the organization's premises/campus. As such, it should be appreciated that the contact center system106may be deployed in equipment dedicated to the organization or third-party service provider thereof and/or deployed in a remote computing environment such as, for example, a private or public cloud environment with infrastructure for supporting multiple contact centers for multiple enterprises. In some embodiments, the contact center system106includes resources (e.g., personnel, computers, and telecommunication equipment) to enable delivery of services via telephone and/or other communication mechanisms. Such services may include, for example, technical support, help desk support, emergency response, and/or other contact center services depending on the particular type of contact center. In some embodiments, the contact center system200may be a contact center system similar to the contact center system200described in reference toFIG.2.

The agent device118may be embodied as any type of device or system of the contact center system106that may be used by an agent of the contact center for communication with the user device108(e.g., of a contact center client), the cloud-based system102, and/or otherwise capable of performing the functions described herein. In some embodiments, the agent device118may be embodied as an agent device similar to the agent devices230described in reference to the contact center system200ofFIG.2. It should be appreciated that agent supervisors may utilize a device similar to the agent device118.

The user device108may be embodied as any type of device (e.g., of a contact center client) capable of executing an application and otherwise performing the functions described herein. For example, in some embodiments, the user device108is configured to execute an application to participate in a conversation with a human agent (e.g., via the agent device118), personal bot, automated agent, chat bot, or other automated system. As such, the user device108may have various input/output devices with which a user may interact to provide and receive audio, text, video, and/or other forms of data. It should be appreciated that the application may be embodied as any type of application suitable for performing the functions described herein. In particular, in some embodiments, the application may be embodied as a mobile application (e.g., a smartphone application), a cloud-based application, a web application, a thin-client application, and/or another type of application. For example, in some embodiments, application may serve as a client-side interface (e.g., via a web browser) for a web-based application or service.

The social media system110may be embodied as any one or more types of devices/systems capable of performing the functions described herein. In some embodiments, the social media system110may include one or more social media servers that allow for externals devices (e.g., the agent device118) to stream content accessible to users of a corresponding social media platform. In particular, the agent device118may execute streaming software that obtains a streaming URL and key for the social media system110, capture the agent desktop to get a real-time media player (RTMP) stream, and send the RTMP stream to the social media servers using the streaming URL and key. Additionally, the social media system110may include APIs that allow for the posting and retrieving of actions in and from social media.

It should be appreciated that each of the cloud-based system102, the network104, the contact center system106, the user device108, the social media system110, the analysis system112, the scheduler system114, the broadcasting system116, and the agent device118may be embodied as, executed by, form a portion of, or associated with any type of device/system, collection of devices/systems, and/or portion(s) thereof suitable for performing the functions described herein (e.g., the computing device400ofFIG.4). In various embodiments, it should be appreciated that the contact center system106may form a portion of, constitute a feature/device superset of, or involve a contact center system similar to the contact center system200ofFIG.2. Additionally, the cloud-based system102may form a portion of, constitute a feature/device superset of, or involve a cloud-based system similar to the cloud-based system300ofFIG.3. In some embodiments, it should be appreciated that the cloud-based system102may be communicatively coupled to the contact center system106, form a portion of the contact center system106, and/or be otherwise used in conjunction with the contact center system106.

Referring now toFIG.2, a simplified block diagram of at least one embodiment of a communications infrastructure and/or content center system, which may be used in conjunction with one or more of the embodiments described herein, is shown. The contact center system200may be embodied as any system capable of providing contact center services (e.g., call center services, chat center services, SMS center services, etc.) to an end user and otherwise performing the functions described herein. The illustrative contact center system200includes a customer device205, a network210, a switch/media gateway212, a call controller214, an interactive media response (IMR) server216, a routing server218, a storage device220, a statistics server226, agent devices230A,230B,230C, a media server234, a knowledge management server236, a knowledge system238, chat server240, web servers242, an interaction (iXn) server244, a universal contact server246, a reporting server248, a media services server249, and an analytics module250. Although only one customer device205, one network210, one switch/media gateway212, one call controller214, one IMR server216, one routing server218, one storage device220, one statistics server226, one media server234, one knowledge management server236, one knowledge system238, one chat server240, one iXn server244, one universal contact server246, one reporting server248, one media services server249, and one analytics module250are shown in the illustrative embodiment ofFIG.2, the contact center system200may include multiple customer devices205, networks210, switch/media gateways212, call controllers214, IMR servers216, routing servers218, storage devices220, statistics servers226, media servers234, knowledge management servers236, knowledge systems238, chat servers240, iXn servers244, universal contact servers246, reporting servers248, media services servers249, and/or analytics modules250in other embodiments. Further, in some embodiments, one or more of the components described herein may be excluded from the system200, one or more of the components described as being independent may form a portion of another component, and/or one or more of the component described as forming a portion of another component may be independent.

It should be understood that the term “contact center system” is used herein to refer to the system depicted inFIG.2and/or the components thereof, while the term “contact center” is used more generally to refer to contact center systems, customer service providers operating those systems, and/or the organizations or enterprises associated therewith. Thus, unless otherwise specifically limited, the term “contact center” refers generally to a contact center system (such as the contact center system200), the associated customer service provider (such as a particular customer service provider/agent providing customer services through the contact center system200), as well as the organization or enterprise on behalf of which those customer services are being provided.

By way of background, customer service providers may offer many types of services through contact centers. Such contact centers may be staffed with employees or customer service agents (or simply “agents”), with the agents serving as an interface between a company, enterprise, government agency, or organization (hereinafter referred to interchangeably as an “organization” or “enterprise”) and persons, such as users, individuals, or customers (hereinafter referred to interchangeably as “individuals,” “customers,” or “contact center clients”). For example, the agents at a contact center may assist customers in making purchasing decisions, receiving orders, or solving problems with products or services already received. Within a contact center, such interactions between contact center agents and outside entities or customers may be conducted over a variety of communication channels, such as, for example, via voice (e.g., telephone calls or voice over IP or VOIP calls), video (e.g., video conferencing), text (e.g., emails and text chat), screen sharing, co-browsing, and/or other communication channels.

Operationally, contact centers generally strive to provide quality services to customers while minimizing costs. For example, one way for a contact center to operate is to handle every customer interaction with a live agent. While this approach may score well in terms of the service quality, it likely would also be prohibitively expensive due to the high cost of agent labor. Because of this, most contact centers utilize some level of automated processes in place of live agents, such as, for example, interactive voice response (IVR) systems, interactive media response (IMR) systems, internet robots or “bots”, automated chat modules or “chatbots”, and/or other automated processed. In many cases, this has proven to be a successful strategy, as automated processes can be highly efficient in handling certain types of interactions and effective at decreasing the need for live agents. Such automation allows contact centers to target the use of human agents for the more difficult customer interactions, while the automated processes handle the more repetitive or routine tasks. Further, automated processes can be structured in a way that optimizes efficiency and promotes repeatability. Whereas a human or live agent may forget to ask certain questions or follow-up on particular details, such mistakes are typically avoided through the use of automated processes. While customer service providers are increasingly relying on automated processes to interact with customers, the use of such technologies by customers remains far less developed. Thus, while IVR systems, IMR systems, and/or bots are used to automate portions of the interaction on the contact center-side of an interaction, the actions on the customer-side remain for the customer to perform manually.

It should be appreciated that the contact center system200may be used by a customer service provider to provide various types of services to customers. For example, the contact center system200may be used to engage and manage interactions in which automated processes (or bots) or human agents communicate with customers. As should be understood, the contact center system200may be an in-house facility to a business or enterprise for performing the functions of sales and customer service relative to products and services available through the enterprise. In another embodiment, the contact center system200may be operated by a third-party service provider that contracts to provide services for another organization. Further, the contact center system200may be deployed on equipment dedicated to the enterprise or third-party service provider, and/or deployed in a remote computing environment such as, for example, a private or public cloud environment with infrastructure for supporting multiple contact centers for multiple enterprises. The contact center system200may include software applications or programs, which may be executed on premises or remotely or some combination thereof. It should further be appreciated that the various components of the contact center system200may be distributed across various geographic locations and not necessarily contained in a single location or computing environment.

It should further be understood that, unless otherwise specifically limited, any of the computing elements of the present invention may be implemented in cloud-based or cloud computing environments. As used herein and further described below in reference to the computing device400, “cloud computing”—or, simply, the “cloud”—is defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned via virtualization and released with minimal management effort or service provider interaction, and then scaled accordingly. Cloud computing can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.). Often referred to as a “serverless architecture,” a cloud execution model generally includes a service provider dynamically managing an allocation and provisioning of remote servers for achieving a desired functionality.

It should be understood that any of the computer-implemented components, modules, or servers described in relation toFIG.2may be implemented via one or more types of computing devices, such as, for example, the computing device400ofFIG.4. As will be seen, the contact center system200generally manages resources (e.g., personnel, computers, telecommunication equipment, etc.) to enable delivery of services via telephone, email, chat, or other communication mechanisms. Such services may vary depending on the type of contact center and, for example, may include customer service, help desk functionality, emergency response, telemarketing, order taking, and/or other characteristics.

Customers desiring to receive services from the contact center system200may initiate inbound communications (e.g., telephone calls, emails, chats, etc.) to the contact center system200via a customer device205. WhileFIG.2shows one such customer device—i.e., customer device205—it should be understood that any number of customer devices205may be present. The customer devices205, for example, may be a communication device, such as a telephone, smart phone, computer, tablet, or laptop. In accordance with functionality described herein, customers may generally use the customer devices205to initiate, manage, and conduct communications with the contact center system200, such as telephone calls, emails, chats, text messages, web-browsing sessions, and other multi-media transactions.

Inbound and outbound communications from and to the customer devices205may traverse the network210, with the nature of the network typically depending on the type of customer device being used and the form of communication. As an example, the network210may include a communication network of telephone, cellular, and/or data services. The network210may be a private or public switched telephone network (PSTN), local area network (LAN), private wide area network (WAN), and/or public WAN such as the Internet. Further, the network210may include a wireless carrier network including a code division multiple access (CDMA) network, global system for mobile communications (GSM) network, or any wireless network/technology conventional in the art, including but not limited to 3G, 4G, LTE, 5G, etc.

The switch/media gateway212may be coupled to the network210for receiving and transmitting telephone calls between customers and the contact center system200. The switch/media gateway212may include a telephone or communication switch configured to function as a central switch for agent level routing within the center. The switch may be a hardware switching system or implemented via software. For example, the switch212may include an automatic call distributor, a private branch exchange (PBX), an IP-based software switch, and/or any other switch with specialized hardware and software configured to receive Internet-sourced interactions and/or telephone network-sourced interactions from a customer, and route those interactions to, for example, one of the agent devices230. Thus, in general, the switch/media gateway212establishes a voice connection between the customer and the agent by establishing a connection between the customer device205and agent device230.

As further shown, the switch/media gateway212may be coupled to the call controller214which, for example, serves as an adapter or interface between the switch and the other routing, monitoring, and communication-handling components of the contact center system200. The call controller214may be configured to process PSTN calls, VoIP calls, and/or other types of calls. For example, the call controller214may include computer-telephone integration (CTI) software for interfacing with the switch/media gateway and other components. The call controller214may include a session initiation protocol (SIP) server for processing SIP calls. The call controller214may also extract data about an incoming interaction, such as the customer's telephone number, IP address, or email address, and then communicate these with other contact center components in processing the interaction.

The interactive media response (IMR) server216may be configured to enable self-help or virtual assistant functionality. Specifically, the IMR server216may be similar to an interactive voice response (IVR) server, except that the IMR server216is not restricted to voice and may also cover a variety of media channels. In an example illustrating voice, the IMR server216may be configured with an IMR script for querying customers on their needs. For example, a contact center for a bank may instruct customers via the IMR script to “press 1” if they wish to retrieve their account balance. Through continued interaction with the IMR server216, customers may receive service without needing to speak with an agent. The IMR server216may also be configured to ascertain why a customer is contacting the contact center so that the communication may be routed to the appropriate resource. The IMR configuration may be performed through the use of a self-service and/or assisted service tool which comprises a web-based tool for developing IVR applications and routing applications running in the contact center environment.

The routing server218may function to route incoming interactions. For example, once it is determined that an inbound communication should be handled by a human agent, functionality within the routing server218may select the most appropriate agent and route the communication thereto. This agent selection may be based on which available agent is best suited for handling the communication. More specifically, the selection of appropriate agent may be based on a routing strategy or algorithm that is implemented by the routing server218. In doing this, the routing server218may query data that is relevant to the incoming interaction, for example, data relating to the particular customer, available agents, and the type of interaction, which, as described herein, may be stored in particular databases. Once the agent is selected, the routing server218may interact with the call controller214to route (i.e., connect) the incoming interaction to the corresponding agent device230. As part of this connection, information about the customer may be provided to the selected agent via their agent device230. This information is intended to enhance the service the agent is able to provide to the customer.

It should be appreciated that the contact center system200may include one or more mass storage devices—represented generally by the storage device220—for storing data in one or more databases relevant to the functioning of the contact center. For example, the storage device220may store customer data that is maintained in a customer database. Such customer data may include, for example, customer profiles, contact information, service level agreement (SLA), and interaction history (e.g., details of previous interactions with a particular customer, including the nature of previous interactions, disposition data, wait time, handle time, and actions taken by the contact center to resolve customer issues). As another example, the storage device220may store agent data in an agent database. Agent data maintained by the contact center system200may include, for example, agent availability and agent profiles, schedules, skills, handle time, and/or other relevant data. As another example, the storage device220may store interaction data in an interaction database. Interaction data may include, for example, data relating to numerous past interactions between customers and contact centers. More generally, it should be understood that, unless otherwise specified, the storage device220may be configured to include databases and/or store data related to any of the types of information described herein, with those databases and/or data being accessible to the other modules or servers of the contact center system200in ways that facilitate the functionality described herein. For example, the servers or modules of the contact center system200may query such databases to retrieve data stored therein or transmit data thereto for storage. The storage device220, for example, may take the form of any conventional storage medium and may be locally housed or operated from a remote location. As an example, the databases may be Cassandra database, NoSQL database, or a SQL database and managed by a database management system, such as, Oracle, IBM DB2, Microsoft SQL server, or Microsoft Access, PostgreSQL.

The statistics server226may be configured to record and aggregate data relating to the performance and operational aspects of the contact center system200. Such information may be compiled by the statistics server226and made available to other servers and modules, such as the reporting server248, which then may use the data to produce reports that are used to manage operational aspects of the contact center and execute automated actions in accordance with functionality described herein. Such data may relate to the state of contact center resources, e.g., average wait time, abandonment rate, agent occupancy, and others as functionality described herein would require.

The agent devices230of the contact center system200may be communication devices configured to interact with the various components and modules of the contact center system200in ways that facilitate functionality described herein. An agent device230, for example, may include a telephone adapted for regular telephone calls or VoIP calls. An agent device230may further include a computing device configured to communicate with the servers of the contact center system200, perform data processing associated with operations, and interface with customers via voice, chat, email, and other multimedia communication mechanisms according to functionality described herein. AlthoughFIG.2shows three such agent devices230—i.e., agent devices230A,230B and230C—it should be understood that any number of agent devices230may be present in a particular embodiment.

The multimedia/social media server234may be configured to facilitate media interactions (other than voice) with the customer devices205and/or the servers242. Such media interactions may be related, for example, to email, voice mail, chat, video, text-messaging, web, social media, co-browsing, etc. The multi-media/social media server234may take the form of any IP router conventional in the art with specialized hardware and software for receiving, processing, and forwarding multi-media events and communications.

The knowledge management server236may be configured to facilitate interactions between customers and the knowledge system238. In general, the knowledge system238may be a computer system capable of receiving questions or queries and providing answers in response. The knowledge system238may be included as part of the contact center system200or operated remotely by a third party. The knowledge system238may include an artificially intelligent computer system capable of answering questions posed in natural language by retrieving information from information sources such as encyclopedias, dictionaries, newswire articles, literary works, or other documents submitted to the knowledge system238as reference materials. As an example, the knowledge system238may be embodied as IBM Watson or a similar system.

The chat server240, it may be configured to conduct, orchestrate, and manage electronic chat communications with customers. In general, the chat server240is configured to implement and maintain chat conversations and generate chat transcripts. Such chat communications may be conducted by the chat server240in such a way that a customer communicates with automated chatbots, human agents, or both. In exemplary embodiments, the chat server240may perform as a chat orchestration server that dispatches chat conversations among the chatbots and available human agents. In such cases, the processing logic of the chat server240may be rules driven so to leverage an intelligent workload distribution among available chat resources. The chat server240further may implement, manage, and facilitate user interfaces (UIs) associated with the chat feature, including those UIs generated at either the customer device205or the agent device230. The chat server240may be configured to transfer chats within a single chat session with a particular customer between automated and human sources such that, for example, a chat session transfers from a chatbot to a human agent or from a human agent to a chatbot. The chat server240may also be coupled to the knowledge management server236and the knowledge systems238for receiving suggestions and answers to queries posed by customers during a chat so that, for example, links to relevant articles can be provided.

The web servers242may be included to provide site hosts for a variety of social interaction sites to which customers subscribe, such as Facebook, Twitter, Instagram, etc. Though depicted as part of the contact center system200, it should be understood that the web servers242may be provided by third parties and/or maintained remotely. The web servers242may also provide webpages for the enterprise or organization being supported by the contact center system200. For example, customers may browse the webpages and receive information about the products and services of a particular enterprise. Within such enterprise webpages, mechanisms may be provided for initiating an interaction with the contact center system200, for example, via web chat, voice, or email. An example of such a mechanism is a widget, which can be deployed on the webpages or websites hosted on the web servers242. As used herein, a widget refers to a user interface component that performs a particular function. In some implementations, a widget may include a graphical user interface control that can be overlaid on a webpage displayed to a customer via the Internet. The widget may show information, such as in a window or text box, or include buttons or other controls that allow the customer to access certain functionalities, such as sharing or opening a file or initiating a communication. In some implementations, a widget includes a user interface component having a portable portion of code that can be installed and executed within a separate webpage without compilation. Some widgets can include corresponding or additional user interfaces and be configured to access a variety of local resources (e.g., a calendar or contact information on the customer device) or remote resources via network (e.g., instant messaging, electronic mail, or social networking updates).

The interaction (iXn) server244may be configured to manage deferrable activities of the contact center and the routing thereof to human agents for completion. As used herein, deferrable activities may include back-office work that can be performed off-line, e.g., responding to emails, attending training, and other activities that do not entail real-time communication with a customer. As an example, the interaction (iXn) server244may be configured to interact with the routing server218for selecting an appropriate agent to handle each of the deferrable activities. Once assigned to a particular agent, the deferrable activity is pushed to that agent so that it appears on the agent device230of the selected agent. The deferrable activity may appear in a workbin as a task for the selected agent to complete. The functionality of the workbin may be implemented via any conventional data structure, such as, for example, a linked list, array, and/or other suitable data structure. Each of the agent devices230may include a workbin. As an example, a workbin may be maintained in the buffer memory of the corresponding agent device230.

The universal contact server (UCS)246may be configured to retrieve information stored in the customer database and/or transmit information thereto for storage therein. For example, the UCS246may be utilized as part of the chat feature to facilitate maintaining a history on how chats with a particular customer were handled, which then may be used as a reference for how future chats should be handled. More generally, the UCS246may be configured to facilitate maintaining a history of customer preferences, such as preferred media channels and best times to contact. To do this, the UCS246may be configured to identify data pertinent to the interaction history for each customer such as, for example, data related to comments from agents, customer communication history, and the like. Each of these data types then may be stored in the customer database222or on other modules and retrieved as functionality described herein requires.

The reporting server248may be configured to generate reports from data compiled and aggregated by the statistics server226or other sources. Such reports may include near real-time reports or historical reports and concern the state of contact center resources and performance characteristics, such as, for example, average wait time, abandonment rate, and/or agent occupancy. The reports may be generated automatically or in response to specific requests from a requestor (e.g., agent, administrator, contact center application, etc.). The reports then may be used toward managing the contact center operations in accordance with functionality described herein.

The media services server249may be configured to provide audio and/or video services to support contact center features. In accordance with functionality described herein, such features may include prompts for an IVR or IMR system (e.g., playback of audio files), hold music, voicemails/single party recordings, multi-party recordings (e.g., of audio and/or video calls), speech recognition, dual tone multi frequency (DTMF) recognition, faxes, audio and video transcoding, secure real-time transport protocol (SRTP), audio conferencing, video conferencing, coaching (e.g., support for a coach to listen in on an interaction between a customer and an agent and for the coach to provide comments to the agent without the customer hearing the comments), call analysis, keyword spotting, and/or other relevant features.

The analytics module250may be configured to provide systems and methods for performing analytics on data received from a plurality of different data sources as functionality described herein may require. In accordance with example embodiments, the analytics module250also may generate, update, train, and modify predictors or models based on collected data, such as, for example, customer data, agent data, and interaction data. The models may include behavior models of customers or agents. The behavior models may be used to predict behaviors of, for example, customers or agents, in a variety of situations, thereby allowing embodiments of the present invention to tailor interactions based on such predictions or to allocate resources in preparation for predicted characteristics of future interactions, thereby improving overall contact center performance and the customer experience. It will be appreciated that, while the analytics module is described as being part of a contact center, such behavior models also may be implemented on customer systems (or, as also used herein, on the “customer-side” of the interaction) and used for the benefit of customers.

According to exemplary embodiments, the analytics module250may have access to the data stored in the storage device220, including the customer database and agent database. The analytics module250also may have access to the interaction database, which stores data related to interactions and interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories), and the application setting (e.g., the interaction path through the contact center). Further, the analytic module250may be configured to retrieve data stored within the storage device220for use in developing and training algorithms and models, for example, by applying machine learning techniques.

One or more of the included models may be configured to predict customer or agent behavior and/or aspects related to contact center operation and performance. Further, one or more of the models may be used in natural language processing and, for example, include intent recognition and the like. The models may be developed based upon known first principle equations describing a system; data, resulting in an empirical model; or a combination of known first principle equations and data. In developing a model for use with present embodiments, because first principles equations are often not available or easily derived, it may be generally preferred to build an empirical model based upon collected and stored data. To properly capture the relationship between the manipulated/disturbance variables and the controlled variables of complex systems, in some embodiments, it may be preferable that the models are nonlinear. This is because nonlinear models can represent curved rather than straight-line relationships between manipulated/disturbance variables and controlled variables, which are common to complex systems such as those discussed herein. Given the foregoing requirements, a machine learning or neural network-based approach may be a preferred embodiment for implementing the models. Neural networks, for example, may be developed based upon empirical data using advanced regression algorithms.

The analytics module250may further include an optimizer. As will be appreciated, an optimizer may be used to minimize a “cost function” subject to a set of constraints, where the cost function is a mathematical representation of desired objectives or system operation. Because the models may be non-linear, the optimizer may be a nonlinear programming optimizer. It is contemplated, however, that the technologies described herein may be implemented by using, individually or in combination, a variety of different types of optimization approaches, including, but not limited to, linear programming, quadratic programming, mixed integer non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, particle/swarm techniques, and the like.

According to some embodiments, the models and the optimizer may together be used within an optimization system. For example, the analytics module250may utilize the optimization system as part of an optimization process by which aspects of contact center performance and operation are optimized or, at least, enhanced. This, for example, may include features related to the customer experience, agent experience, interaction routing, natural language processing, intent recognition, or other functionality related to automated processes.

The various components, modules, and/or servers ofFIG.2(as well as the other figures included herein) may each include one or more processors executing computer program instructions and interacting with other system components for performing the various functionalities described herein. Such computer program instructions may be stored in a memory implemented using a standard memory device, such as, for example, a random-access memory (RAM), or stored in other non-transitory computer readable media such as, for example, a CD-ROM, flash drive, etc. Although the functionality of each of the servers is described as being provided by the particular server, a person of skill in the art should recognize that the functionality of various servers may be combined or integrated into a single server, or the functionality of a particular server may be distributed across one or more other servers without departing from the scope of the present invention. Further, the terms “interaction” and “communication” are used interchangeably, and generally refer to any real-time and non-real-time interaction that uses any communication channel including, without limitation, telephone calls (PSTN or VOIP calls), emails, vmails, video, chat, screen-sharing, text messages, social media messages, WebRTC calls, etc. Access to and control of the components of the contact system200may be affected through user interfaces (UIs) which may be generated on the customer devices205and/or the agent devices230. As already noted, the contact center system200may operate as a hybrid system in which some or all components are hosted remotely, such as in a cloud-based or cloud computing environment. It should be appreciated that each of the devices of the call center system200may be embodied as, include, or form a portion of one or more computing devices similar to the computing device400described below in reference toFIG.4.

Referring now toFIG.3, a simplified block diagram of at least one embodiment cloud-based system300is shown. The illustrative cloud-based system300includes a border communication device302, a SIP server304, a resource manager306, a media control platform308, a speech/text analytics system310, a voice generator312, a voice gateway314, a media augmentation system316, a chatbot318, voice data storage320, an analysis system322, a scheduler system324, and a broadcasting system326. Although only one border communication device302, one SIP server304, one resource manager306, one media control platform308, one speech/text analytics system310, one voice generator312, one voice gateway314, one media augmentation system316, one chatbot318, one voice data storage320, one analysis system322, one scheduler system324, and one broadcasting system326are shown in the illustrative embodiment ofFIG.3, the cloud-based system300may include multiple border communication devices302, SIP servers304, resource managers306, media control platforms308, speech/text analytics systems310, voice generators312, voice gateways314, media augmentation systems316, chatbots318, voice data storages320, analysis systems322, scheduler systems324, and/or broadcasting systems326in other embodiments. For example, in some embodiments, multiple chatbots318may be used to communicate regarding different subject matters handled by the same cloud-based system300. Further, in some embodiments, one or more of the components described herein may be excluded from the system300, one or more of the components described as being independent may form a portion of another component, and/or one or more of the component described as forming a portion of another component may be independent.

The border communication device302may be embodied as any one or more types of devices/systems that are capable of performing the functions described herein. For example, in some embodiments, the border communication device302may be configured to control signaling and media streams involved in setting up, conducting, and tearing down voice conversations and other media communications between, for example, an end user and contact center system. In some embodiments, the border communication device302may be a session border controller (SBC) controlling the signaling and media exchanged during a media session (also referred to as a “call,” “telephony call,” or “communication session”) between the end user and contact center system. In some embodiments, the signaling exchanged during a media session may include SIP, H.323, Media Gateway Control Protocol (MGCP), and/or any other voice-over IP (VOIP) call signaling protocols. The media exchanged during a media session may include media streams that carry the call's audio, video, or other data along with information of call statistics and quality.

In some embodiments, the border communication device302may operate according to a standard SIP back-to-back user agent (B2BUA) configuration. In this regard, the border communication device302may be inserted in the signaling and media paths established between a calling and called parties in a VoIP call. In some embodiments, it should be understood that other intermediary software and/or hardware devices may be invoked in establishing the signaling and/or media paths between the calling and called parties.

In some embodiments, the border communication device302may exert control over signaling (e.g., SIP messages) and media streams (e.g., RTP data) routed to and from a contact center system (e.g., the contact center system106) and other devices (e.g., a customer/client device such as the user device108, the cloud-based system102, and/or other devices) that traverse the network (e.g., the network104). In this regard, the border communication device302may be coupled to trunks that carry signals and media for calls to and from the user device over the network, and to trunks that carry signals and media to and from the contact center system over the network.

The SIP server304may be embodied as any one or more types of devices/systems that are capable of performing the functions described herein. For example, in some embodiments, the SIP server204may act as a SIP B2UBA and may control the flow of SIP requests and responses between SIP endpoints. Any other controller configured to set up and tear down VoIP communication sessions may be contemplated in addition to or in lieu of the SIP server304in other embodiments. The SIP server304may be a separate logical component or may be combined with the resource manager306. In some embodiments, the SIP server304may be hosted at a contact center system (e.g., the contact center system106). Although a SIP server304is used in the illustrative embodiment, another call server configured with another VoIP protocol may be used in addition to or in lieu of SIP, such as, for example, H.232 protocol, Media Gateway Control Protocol, Skype protocol, and/or other suitable technologies in other embodiments.

The resource manager306may be embodied as any one or more types of devices/systems that are capable of performing the functions described herein. In the illustrative embodiment, the resource manager306may be configured to allocate and monitor a pool of media control platforms for providing load balancing and high availability for each resource type. In some embodiments, the resource manager306may monitor and may select a media control platform308from a cluster of available platforms. The selection of the media control platform308may be dynamic, for example, based on identification of a location of a calling end user, type of media services to be rendered, detected quality of a current media service, and/or other factors.

In some embodiments, the resource manager306may be configured to process requests for media services, and interact with, for example, a configuration server having a configuration database, to determine an interactive voice response (IVR) profile, voice application (e.g. Voice Extensible Markup Language (Voice XML) application), announcement, and conference application, resource, and service profile that can deliver the service, such as, for example, a media control platform. According to some embodiments, the resource manager may provide hierarchical multi-tenant configurations for service providers, enabling them to apportion a select number of resources for each tenant.

In some embodiments, the resource manager306may be configured to act as a SIP proxy, a SIP registrar, and/or a SIP notifier. In this regard, the resource manager306may act as a proxy for SIP traffic between two SIP components. As a SIP registrar, the resource manager306may accept registration of various resources via, for example, SIP REGISTER messages. In this manner, the cloud-based system300may support transparent relocation of call-processing components. In some embodiments, components such as the media control platform308do not register with the resource manager306at startup. The resource manager306may detect instances of the media control platform308through configuration information retrieved from the configuration database. If the media control platform308has been configured for monitoring, the resource manager306may monitor resource health by using, for example, SIP OPTIONS messages. In some embodiments, to determine whether the resources in the group are alive, the resource manager306may periodically send SIP OPTIONS messages to each media control platform308resource in the group. If the resource manager306receives an OK response, the resources are considered alive. It should be appreciated that the resource manager306may be configured to perform other various functions, which have been omitted for brevity of the description. The resource manager306and the media control platform308may collectively be referred to as a media controller.

In some embodiments, the resource manager306may act as a SIP notifier by accepting, for example, SIP SUBSCRIBE requests from the SIP server304and maintaining multiple independent subscriptions for the same or different SIP devices. The subscription notices are targeted for the tenants that are managed by the resource manager306. In this role, the resource manager306may periodically generate SIP NOTIFY requests to subscribers (or tenants) about port usage and the number of available ports. The resource manager306may support multi-tenancy by sending notifications that contain the tenant name and the current status (in- or out-of-service) of the media control platform308that is associated with the tenant, as well as current capacity for the tenant.

The media control platform308may be embodied as any service or system capable of providing media services and otherwise performing the functions described herein. For example, in some embodiments, the media control platform308may be configured to provide call and media services upon request from a service user. Such services may include, without limitation, initiating outbound calls, playing music or providing other media while a call is placed on hold, call recording, conferencing, call progress detection, playing audio/video prompts during a customer self-service session, and/or other call and media services. One or more of the services may be defined by voice applications (e.g. VoiceXML applications) that are executed as part of the process of establishing a media session between the media control platform308and the end user.

The speech/text analytics system (STAS)310may be embodied as any service or system capable of providing various speech analytics and text processing functionalities (e.g., text-to-speech) as will be understood by a person of skill in the art and otherwise performing the functions described herein. The speech/text analytics system310may perform automatic speech and/or text recognition and grammar matching for end user communications sessions that are handled by the cloud-based system300. The speech/text analytics system310may include one or more processors and instructions stored in machine-readable media that are executed by the processors to perform various operations. In some embodiments, the machine-readable media may include non-transitory storage media, such as hard disks and hardware memory systems.

The voice generator312may be embodied as any service or system capable of generating a voice communication and otherwise performing the functions described herein. In some embodiments, the voice generator312may generate the voice communication based on a particular voice signature.

The voice gateway314may be embodied as any service or system capable of performing the functions described herein. In the illustrative embodiment, the voice gateway314receives end user calls from or places calls to voice communications devices, such as an end user device, and responds to the calls in accordance with a voice program that corresponds to a communication routing configuration of the contact center system. In some embodiments, the voice program may include a voice avatar. The voice program may be accessed from local memory within the voice gateway314or from other storage media in the cloud-based system300. In some embodiments, the voice gateway314may process voice programs that are script-based voice applications. The voice program, therefore, may be a script written in a scripting language, such as voice extensible markup language (VoiceXML) or speech application language tags (SALT). The cloud-based system300may also communicate with the voice data storage320to read and/or write user interaction data (e.g., state variables for a data communications session) in a shared memory space.

The media augmentation system316may be embodied as any service or system capable of specifying how the portions of the cloud-based system300(e.g., one or more of the border communications device302, the SIP server304, the resource manager306, the media control platform308, the speech/text analytics system310, the voice generator312, the voice gateway314, the media augmentation system316, the chatbot318, the voice data storage320, the analysis system322, the scheduler system324, the broadcasting system326, and/or one or more portions thereof) interact with each other and otherwise performing the functions described herein. In some embodiments, the media augmentation system316may be embodied as or include an application program interface (API). In some embodiments, the media augmentation system316enables integration of differing parameters and/or protocols that are used with various planned application and media types utilized within the cloud-based system300.

The chatbot318may be embodied as any automated service or system capable of using automation to engage with end users and otherwise performing the functions described herein. For example, in some embodiments, the chatbot318may operate, for example, as an executable program that can be launched according to demand for the particular chatbot. In some embodiments, the chatbot318simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if the humans were communicating with another human. In some embodiments, the chatbot318may be as simple as rudimentary programs that answer a simple query with a single-line response, or as sophisticated as digital assistants that learn and evolve to deliver increasing levels of personalization as they gather and process information. In some embodiments, the chatbot318includes and/or leverages artificial intelligence, adaptive learning, bots, cognitive computing, and/or other automation technologies. Chatbot318may also be referred to herein as one or more chat robots, AI chatbots, automated chat robot, chatterbots, dialog systems, conversational agents, automated chat resources, and/or bots.

A benefit of utilizing automated chat robots for engaging in chat conversations with end users may be that it helps contact centers to more efficiently use valuable and costly resources like human resources, while maintaining end user satisfaction. For example, chat robots may be invoked to initially handle chat conversations without a human end user knowing that it is conversing with a robot. The chat conversation may be escalated to a human resource if and when appropriate. Thus, human resources need not be unnecessarily tied up in handling simple requests and may instead be more effectively used to handle more complex requests or to monitor the progress of many different automated communications at the same time.

The voice data storage320may be embodied as one or more databases, data structures, and/or data storage devices capable of storing data in the cloud-based system300or otherwise facilitating the storage of such data for the cloud-based system300. For example, in some embodiments, the voice data storage320may include one or more cloud storage buckets. In other embodiments, it should be appreciated that the voice data storage320may, additionally or alternatively, include other types of voice data storage mechanisms that allow for dynamic scaling of the amount of data storage available to the cloud-based system300. In some embodiments, the voice data storage320may store scripts (e.g., pre-programmed scripts or otherwise). Although the voice data storage320is described herein as data storages and databases, it should be appreciated that the voice data storage320may include both a database (or other type of organized collection of data and structures) and data storage for the actual storage of the underlying data. The voice data storage320may store various data useful for performing the functions described herein.

Each of the analysis system322, the scheduler system324, and the broadcasting system326may be embodied as any device or collection of devices capable of performing the functions described herein. For example, in some embodiments, the analysis system324may be embodied as a system similar to the analysis system112described above in reference to the system100ofFIG.1. In some embodiments, the scheduler system324may be embodied as a system similar to the scheduler system114described above in reference to the system100ofFIG.1. In some embodiments, the broadcasting system326may be embodied as a system similar to the broadcasting system116described above in reference to the system100ofFIG.1.

Referring now toFIG.4, a simplified block diagram of at least one embodiment of a computing device400is shown. The illustrative computing device400depicts at least one embodiment of each of the computing devices, systems, servicers, controllers, switches, gateways, engines, modules, and/or computing components described herein (e.g., which collectively may be referred to interchangeably as computing devices, servers, or modules for brevity of the description). For example, the various computing devices may be a process or thread running on one or more processors of one or more computing devices400, which may be executing computer program instructions and interacting with other system modules in order to perform the various functionalities described herein. Unless otherwise specifically limited, the functionality described in relation to a plurality of computing devices may be integrated into a single computing device, or the various functionalities described in relation to a single computing device may be distributed across several computing devices. Further, in relation to the computing systems described herein-such as the contact center system200ofFIG.2and/or the cloud-based system300ofFIG.3—the various servers and computer devices thereof may be located on local computing devices400(e.g., on-site at the same physical location as the agents of the contact center), remote computing devices400(e.g., off-site or in a cloud-based or cloud computing environment, for example, in a remote data center connected via a network), or some combination thereof. In some embodiments, functionality provided by servers located on computing devices off-site may be accessed and provided over a virtual private network (VPN), as if such servers were on-site, or the functionality may be provided using a software as a service (SaaS) accessed over the Internet using various protocols, such as by exchanging data via extensible markup language (XML), JSON, and/or the functionality may be otherwise accessed/leveraged.

In some embodiments, the computing device400may be embodied as a server, desktop computer, laptop computer, tablet computer, notebook, netbook, Ultrabook™, cellular phone, mobile computing device, smartphone, wearable computing device, personal digital assistant, Internet of Things (IoT) device, processing system, wireless access point, router, gateway, and/or any other computing, processing, and/or communication device capable of performing the functions described herein.

The computing device400includes a processing device402that executes algorithms and/or processes data in accordance with operating logic408, an input/output device404that enables communication between the computing device400and one or more external devices410, and memory406which stores, for example, data received from the external device410via the input/output device404.

The input/output device404allows the computing device400to communicate with the external device410. For example, the input/output device404may include a transceiver, a network adapter, a network card, an interface, one or more communication ports (e.g., a USB port, serial port, parallel port, an analog port, a digital port, VGA, DVI, HDMI, FireWire, CAT 5, or any other type of communication port or interface), and/or other communication circuitry. Communication circuitry of the computing device400may be configured to use any one or more communication technologies (e.g., wireless or wired communications) and associated protocols (e.g., Ethernet, Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication depending on the particular computing device400. The input/output device404may include hardware, software, and/or firmware suitable for performing the techniques described herein.

The external device410may be any type of device that allows data to be inputted or outputted from the computing device400. For example, in various embodiments, the external device410may be embodied as one or more of the devices/systems described herein, and/or a portion thereof. Further, in some embodiments, the external device410may be embodied as another computing device, switch, diagnostic tool, controller, printer, display, alarm, peripheral device (e.g., keyboard, mouse, touch screen display, etc.), and/or any other computing, processing, and/or communication device capable of performing the functions described herein. Furthermore, in some embodiments, it should be appreciated that the external device410may be integrated into the computing device400.

The processing device402may be embodied as any type of processor(s) capable of performing the functions described herein. In particular, the processing device402may be embodied as one or more single or multi-core processors, microcontrollers, or other processor or processing/controlling circuits. For example, in some embodiments, the processing device402may include or be embodied as an arithmetic logic unit (ALU), central processing unit (CPU), digital signal processor (DSP), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), and/or another suitable processor(s). The processing device402may be a programmable type, a dedicated hardwired state machine, or a combination thereof. Processing devices402with multiple processing units may utilize distributed, pipelined, and/or parallel processing in various embodiments. Further, the processing device402may be dedicated to performance of just the operations described herein, or may be utilized in one or more additional applications. In the illustrative embodiment, the processing device402is programmable and executes algorithms and/or processes data in accordance with operating logic408as defined by programming instructions (such as software or firmware) stored in memory406. Additionally or alternatively, the operating logic408for processing device402may be at least partially defined by hardwired logic or other hardware. Further, the processing device402may include one or more components of any type suitable to process the signals received from input/output device404or from other components or devices and to provide desired output signals. Such components may include digital circuitry, analog circuitry, or a combination thereof.

The memory406may be of one or more types of non-transitory computer-readable media, such as a solid-state memory, electromagnetic memory, optical memory, or a combination thereof. Furthermore, the memory406may be volatile and/or nonvolatile and, in some embodiments, some or all of the memory406may be of a portable type, such as a disk, tape, memory stick, cartridge, and/or other suitable portable memory. In operation, the memory406may store various data and software used during operation of the computing device400such as operating systems, applications, programs, libraries, and drivers. It should be appreciated that the memory406may store data that is manipulated by the operating logic408of processing device402, such as, for example, data representative of signals received from and/or sent to the input/output device404in addition to or in lieu of storing programming instructions defining operating logic408. As shown inFIG.4, the memory406may be included with the processing device402and/or coupled to the processing device402depending on the particular embodiment. For example, in some embodiments, the processing device402, the memory406, and/or other components of the computing device400may form a portion of a system-on-a-chip (SoC) and be incorporated on a single integrated circuit chip.

In some embodiments, various components of the computing device400(e.g., the processing device402and the memory406) may be communicatively coupled via an input/output subsystem, which may be embodied as circuitry and/or components to facilitate input/output operations with the processing device402, the memory406, and other components of the computing device400. For example, the input/output subsystem may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and/or other components and subsystems to facilitate the input/output operations.

The computing device400may include other or additional components, such as those commonly found in a typical computing device (e.g., various input/output devices and/or other components), in other embodiments. It should be further appreciated that one or more of the components of the computing device400described herein may be distributed across multiple computing devices. In other words, the techniques described herein may be employed by a computing system that includes one or more computing devices. Additionally, although only a single processing device402, I/O device404, and memory406are illustratively shown inFIG.4, it should be appreciated that a particular computing device400may include multiple processing devices402, I/O devices404, and/or memories406in other embodiments. Further, in some embodiments, more than one external device410may be in communication with the computing device400.

The computing device400may be one of a plurality of devices connected by a network or connected to other systems/resources via a network. The network may be embodied as any one or more types of communication networks that are capable of facilitating communication between the various devices communicatively connected via the network. As such, the network may include one or more networks, routers, switches, access points, hubs, computers, client devices, endpoints, nodes, and/or other intervening network devices. For example, the network may be embodied as or otherwise include one or more cellular networks, telephone networks, local or wide area networks, publicly available global networks (e.g., the Internet), ad hoc networks, short-range communication links, or a combination thereof. In some embodiments, the network may include a circuit-switched voice or data network, a packet-switched voice or data network, and/or any other network able to carry voice and/or data. In particular, in some embodiments, the network may include Internet Protocol (IP)-based and/or asynchronous transfer mode (ATM)-based networks. In some embodiments, the network may handle voice traffic (e.g., via a Voice over IP (VOIP) network), web traffic, and/or other network traffic depending on the particular embodiment and/or devices of the system in communication with one another. In various embodiments, the network may include analog or digital wired and wireless networks (e.g., IEEE 802.11 networks, Public Switched Telephone Network (PSTN), Integrated Services Digital Network (ISDN), and Digital Subscriber Line (xDSL)), Third Generation (3G) mobile telecommunications networks, Fourth Generation (4G) mobile telecommunications networks, Fifth Generation (5G) mobile telecommunications networks, a wired Ethernet network, a private network (e.g., such as an intranet), radio, television, cable, satellite, and/or any other delivery or tunneling mechanism for carrying data, or any appropriate combination of such networks. It should be appreciated that the various devices/systems may communicate with one another via different networks depending on the source and/or destination devices/systems.

It should be appreciated that the computing device400may communicate with other computing devices400via any type of gateway or tunneling protocol such as secure socket layer or transport layer security. The network interface may include a built-in network adapter, such as a network interface card, suitable for interfacing the computing device to any type of network capable of performing the operations described herein. Further, the network environment may be a virtual network environment where the various network components are virtualized. For example, the various machines may be virtual machines implemented as a software-based computer running on a physical machine. The virtual machines may share the same operating system, or, in other embodiments, different operating system may be run on each virtual machine instance. For example, a “hypervisor” type of virtualizing is used where multiple virtual machines run on the same host physical machine, each acting as if it has its own dedicated box. Other types of virtualization may be employed in other embodiments, such as, for example, the network (e.g., via software defined networking) or functions (e.g., via network functions virtualization).

Accordingly, one or more of the computing devices400described herein may be embodied as, or form a portion of, one or more cloud-based systems. In cloud-based embodiments, the cloud-based system may be embodied as a server-ambiguous computing solution, for example, that executes a plurality of instructions on-demand, contains logic to execute instructions only when prompted by a particular activity/trigger, and does not consume computing resources when not in use. That is, system may be embodied as a virtual computing environment residing “on” a computing system (e.g., a distributed network of devices) in which various virtual functions (e.g., Lambda functions, Azure functions, Google cloud functions, and/or other suitable virtual functions) may be executed corresponding with the functions of the system described herein. For example, when an event occurs (e.g., data is transferred to the system for handling), the virtual computing environment may be communicated with (e.g., via a request to an API of the virtual computing environment), whereby the API may route the request to the correct virtual function (e.g., a particular server-ambiguous computing resource) based on a set of rules. As such, when a request for the transmission of data is made by a user (e.g., via an appropriate user interface to the system), the appropriate virtual function(s) may be executed to perform the actions before eliminating the instance of the virtual function(s).

Referring now toFIG.5, a simplified embodiment of an architecture500for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts depicts an analytics engine502, an agent desktop504, social media application programming interfaces (APIs)506, streaming software508, and a live stream510. The analytics engine502may perform the analyses described herein with respect to the social media content and comments, reactions, and/or other data posted by clients related to an issue and/or the live stream. Further, in some embodiments, the analytics engine502may also determine which agents should handle the various issues and the best time at which a live stream should occur between agents and clients. Accordingly, in some embodiments, the analytics engine502may execute functions similar to those of the analysis system112and/or the scheduler system114described in reference to the system100ofFIG.1.

In order to broadcast the live stream510of the agent desktop504through a particular social media system/platform, the system may obtain a streaming URL and key via the social media APIs506for that social media system/platform, which are fed into the streaming software508. The streaming software508may relay an RTMP stream (e.g., audio/video) from the agent desktop504to the streaming servers, which will then be broadcasted. The streaming software508may act as a gateway between the agent desktop504and the social media system/platform wherein the RTMP stream from the agent desktop504is relayed. Additionally, the social media APIs506may be used to post and/or retrieve actions in and from the social media system/platform. Although the agent desktop504is described as being broadcasted, it should be appreciated that the streaming software508may broadcast the agent's desktop, browsers, applications, webcams, and/or other components of the agent device118via the live stream510depending on the particular embodiment.

Referring now toFIGS.6-7, in use, a computing system (e.g., the system100, the cloud-based system102, the contact center system106, and/or other computing devices described herein) may execute a method600for analysis and broadcast streaming in contact centers to reduce system loads in high volume contexts. It should be appreciated that the particular blocks of the method600are illustrated by way of example, and such blocks may be combined or divided, added or removed, and/or reordered in whole or in part depending on the particular embodiment, unless stated to the contrary.

The illustrative method600begins with block602ofFIG.6in which the computing system analyzes social media content of one or more social media systems/platforms (e.g., Facebook, YouTube, WhatsApp, Instagram, TikTok, Pinterest, Reddit, LinkedIn, X/Twitter, blogs, and/or other social media systems/platforms) using one or more artificial intelligence models and/or machine learning. In particular, in block604, the computing system analyzes the social media content to identify issues encountered by clients of the contact center. It should be appreciated that the particular social media content analyzed by the computing system may vary depending on the particular embodiment. For example, in various embodiments, the analyzed content may include posts, comments, likes/dislikes, reactions, tags/hashtags, views, and/or other content engagement data/metrics. In some embodiments, the computing system may group various social media content (e.g., posts, comments, etc.) into categories based on issues encountered by the clients of the contact center. For example, all posts and/or other content related to a system outage in a particular zone may be grouped together.

It should be appreciated that, in performing the various AI-related functions described herein, the computing system may utilize large language models (LLMs), neural network algorithms, regression algorithms, instance-based algorithms, regularization algorithms, decision tree algorithms, Bayesian algorithms, clustering algorithms, association rule learning algorithms, deep learning algorithms, dimensionality reduction algorithms, and/or other suitable machine learning (ML) algorithms, models, techniques, and/or artificial intelligence technologies. Additionally, the number of AI/ML models used by the computing system may vary depending on the particular embodiment. For example, in some embodiments, a single AI/ML model may be used, whereas in other embodiments, multiple AI/ML models may be used.

In block606, the computing system ranks the issues encountered by the clients of the contact center based on a priority of resolving each of the issues. In doing so, in block608, the computing system may determine a number of clients of the contact center affected by each of the issues. For example, the number of clients affected by an issue may be determined or estimated based on the number of relevant posts, number of likes/up-votes, number of comments, number of relevant tags/hashtags, and/or other engagement data/metrics. In block610, the computing system may determine the importance of the clients of the contact center affected by each of the issues. For example, a particular contact center or organization supported by the contact center may define multiple classes of clients based on their level of importance (e.g., prime/non-prime clients, gold/silver/bronze clients, etc.). It should be appreciated that the number of classes/categories of clients may vary depending on the particular embodiment. In block612, the computing system may determine the potential impact of each of the issues on non-reporting clients of the contact center. For example, the computing system may determine that a greater number of non-reporting clients are potentially affected by the issue than those who have already commented/reported the issue due to various factors. Suppose a service has been reported as being down via social media by approximately 10 clients, and the service is known to be used by 500 clients. The computing system may infer that there is significant likelihood that some or all of the non-reporting 490 clients are also affected by the issue, some of which may even comment in the future regarding the service downtime.

In some embodiments, the computing system (e.g., the analytics system112) may assign a weight to each comment based on various parameters such as number of reactions from other clients, comments made to the comments by other clients, and/or other parameters. The computing system may also analyze how influential the user who commented is based, for example, on the number of followers of his account. Although described herein as “client” comments and reactions, it should be appreciated that such characterization is for simplicity and brevity of the description; in various embodiments, the computing system may consider both client and non-client comments and reactions. For example, a non-client user may be an influencer with a substantial social media following, in which case the computing system may prioritize comments by such a non-client user (e.g., even over non-influential client comments).

In block614, the computing system selects the highest priority issue to be resolved by the agents. Although described as selecting a single highest priority issue, it should be appreciated that, in some embodiments, multiple issues may be resolved in conjunction with one another and/or the issues may be resolved in parallel using different groups of agents.

In block616, the computing system identifies agents that are best suited to handle the selected issue based, for example, on agents skills related to the selected issue. In doing so, in block618, the computing system evaluates the agents' skills related to handling similar issues. In various embodiments, the agents may be evaluated and identified as those best suited to handle the selected issue based on the agent profiles, historical expertise of the agents (e.g., how many similar issues the agent solved in the past along with client ratings for those interactions, etc.), and/or other factors. The skill parameters/scores for each agent may be predefined in the agent's profile, and may include skills such as language proficiency (in various languages), domain-specific skills (e.g., credit card matters, loan matters, etc. in the banking context), soft skills, and/or other skills. The manner in which agents handled similar issues in the past may be evaluated, for example, based on the time taken to resolve the issue, the number of clients affected, the effectiveness of the agent's provided solution to the issue, the first call resolution rate for the client, and/or other key performance indicators (KPIs) of the agent. In some embodiments, the client rating for each of the agents may also be considered. It should be appreciated that the number of agents selected as those being the best suited to address a particular issue may vary depending on the particular embodiment; however, in the illustrative embodiment, the number exceeds the number of agents configured/required to be in an agent group as described below.

In block620, based on the list/set of agents best suited to handle the selected issue, the computing system determines the next best available time for a live agent session with a group of agents selected from that list/set of agents based on the real-time interaction loads of those agents. The computing system selects the determined next best available time as the time at which the live agent session will occur and/or the time at which the live agent session will become available for the agents to start. Suppose the computing system determines that three agents are required to address a particular issue in a live agent stream. Agent 1 and Agent 2 both have a skill level of 9 for the particular issue, and they are available in the next 10 minutes, whereas Agent 3 has a skill level of 9 with availability in the next 30 minutes. The computing system may determine that Agent 4, who has a skill level of 8.5 and availability in the next 12 minutes, should be selected along with Agent 1 and Agent 2 as the group of agents (and not Agent 3) to present the live agent stream, because the time availability of Agent 4 is substantially sooner than Agent 3 with only a negligible reduction in the agent skill. It should be appreciated that the computing system may leverage an artificial intelligence model to balance the relevant factors and make determinations related to the next best available time and agent group selections. In some embodiments, the number of agents required to be a part of the group presenting the live agent stream may be predefined and/or configurable (e.g., 5 agents for every 100 clients). Further, the computing system may leverage an AI/ML model to refine the agent group size and/or other characteristics described herein. For example, supervised learning may be leveraged at the outset (e.g., based on configuration parameters), and then unsupervised learning may be used to further refine the parameters.

In some embodiments, the agents in the agent group and/or the corresponding agent supervisors may be periodically notified of information related to the live agent session. Additionally, details about the live agent stream, such as the timing, topics addressed, and/or other information may be published on the social media systems/platforms (e.g., the social media systems/platforms through which the live agent stream is to be broadcasted). The computing system may also periodically notify (e.g., through multiple channels) the clients who are facing the issue and/or likely to encounter the issue information regarding the live agent session.

In block622, the computing system determines which clients of the contact center are potentially affected by the issue, and the computing system transmits an invitation to a live agent session to those potentially affected clients. In other embodiments, it should be appreciated that the computing system may determine which clients to invite to the live agent session based on additional and/or alternative criteria. The invitation, or an agent-specific variation thereof, may also be transmitted to the group of agents selected to participate.

In block624ofFIG.7, the computing system determines whether the time for the live agent session has arrived. If so, the method600advances to block626in which the computing system broadcasts a stream of the live agent session at that session time through the one or more social media channels to the clients of the contact center. As described above, in some embodiments, in order to broadcast the live stream, the computing system may relay an RTMP stream from an agent desktop (of one of the group of agents) to the respective social media streaming servers using the streaming URLs and keys for those streaming servers. In some embodiments, when the time for the live agent session has arrived, the dashboard of the agent who will be sharing his screen will activate a “go live” button, which will begin broadcasting the live agent session when it is clicked. It should be appreciated that, in some embodiments, one agent shares his screen while the other agents in the live stream group respond to or otherwise address live comments and reactions. Further, the agents may dynamically transfer control of the shared screen to one another as needed.

In block628, the computing system analyzes social media comments and reactions of the clients of the contact center to the live session. In block630, the computing system generates one or more suggestions for the live agents based on the analysis of the social media comments and reactions. For example, in block632, the computing system may identify (e.g., using an artificial intelligence model) and retrieve content from a knowledgebase that is responsive to the social media comments and/or reactions of the contact center clients. Further, in block634, the computing system may rank the priority of addressing the social media comments and reactions of the contact center clients, and provide those rankings to the agents involved in the live agent stream (e.g., for use during the live agent stream and/or subsequent to the live agent stream).

In some embodiments, one or more agent supervisors may be able to view the current live stream from their desktops, which may also show the list of top comments, reactions, and/or questions that the agent will have to address at a later point. Accordingly, in some embodiments, the supervisor may provide suggestions to the agent in real time from his dashboard, and those suggestions may be displayed on the agent's dashboard. Further, in some embodiments, an artificial intelligence bot (e.g., a blended AI bot) may be available to perform tone and speech analytics to give feedback to the agent to improve/modulate his tone and/or his speech, for example, if negative. At the end of the live session, the AI bot may also provide the top questions based on a weighting along with guidelines and tips from knowledgebase documents and/or FAQs, or other agent suggestions. In some embodiments, the agent may respond to those queries at the end of the live session to further enhance the client experience in real time.

It should be appreciated that the method600may return to block626, as the computing system may continue to broadcast the live agent stream and analyze the relevant social media comments and reactions.

Although the blocks602-634are described in a relatively serial manner, it should be appreciated that various blocks of the method600may be performed in parallel in some embodiments.

Suppose there is a scenario in which a cloud-based service provider has an outage and is down in the us-west-1 region and a client has reported the issue on a social media Facebook post. The post is liked and commented on by thousands of users who use the managed service and the client has started to contact the center. The computing system analyzes the social media platform and ranks this issue as the first priority to be addressed, as many clients are affected (e.g., including influential clients who have a lot of followers) and even more clients are inferred to be likely affected. The data may be passed, for example, asynchronously from the analytics system112to the scheduler system114. The scheduler system114may find the appropriate skilled agents along with supervisors for the issue using the artificial intelligence model based on historical data (e.g., skill level, knowledge of agents previously handling similar issues, KPIs, etc.), and may find the next best available time for a live agent session. For example, the computing system may determine that the relevant agents are available in 10 minutes along with their supervisors and schedule the live session accordingly, sending an invitation to the selected agents/supervisors and clients affected (or likely to be affected) by the issue. Once the time approaches, the agent sharing his screen may “go live,” and the agents may start to give workarounds for the issues. It should be appreciated that there may be multiple categories of users/clients in which case the agents may provide different options and/or workarounds in some circumstances. The supervisor and/or AI bot can provide the agents with suggestions throughout the live agent session.

It should be appreciated that technologies described herein allow for a win-win situation. During critical hours, clients do not need to wait in the queue to reach the contact center agent for a solution, and agents avoid providing repeated workarounds/solutions to different clients facing similar issues. Additionally, it should also be appreciated that the technologies described herein are not limited to resolving issues encountered by contact center clients. For example, the technologies may be used for outbound campaigns and/or product launches. Suppose an organization is launching a new product. The organization can announce the new product on the social media page, such that interested social media users can provide comments, questions, and reactions with respect to the post. Based on that data, the computing system (e.g., the analysis system112) may analyze the social media content in a manner similar to that described above, and similarly find and invite the appropriate agents and users/clients to a live agent session to discuss the new product.