Patent Description:
In <CIT>, in a system for executing an interaction flow for a customer contact center, the system includes: a processor; and a memory coupled to the processor, wherein the memory has stored thereon, instructions that, when executed by the processor, cause the processor to: route an interaction conducted in a first communication channel to an electronic device; monitor the interaction conducted in the first communication channel; store data associated with the interaction conducted in the first communication channel in the memory; conduct the interaction in a second communication channel; in response to conducting the interaction to the second communication channel, retrieve the data associated with the interaction conducted in the first communication channel; and output the retrieved data to the electronic device.

In <CIT> a method and apparatus provides for multiparty participation and management in text messaging sessions. The method includes a server establishing a text messaging session, which establishes a media path for exchanging text messages between a caller device and a single device having a device identifier known to the caller device, wherein the session is established in response to a call from the caller device. The server further joins multiple responding devices to the text messaging session, receives text messages from the multiple responding devices, and sends, over the media path to the caller device, a set of text messages based on the text messages received from the multiple responding devices.

In <CIT>, in a system for managing chat automation, the system includes: a processor; and a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to: receive a request for interaction from an end user operating an end user device; receive information from the end user device regarding the interaction; select a first automated chat profile based on the information regarding the interaction; route the request to an automated chat resource associated with the first automated chat profile; monitor a chat communication session between the end user device and the automated chat resource; detect a trigger event during the monitoring of the chat communication; and transition the chat communication session to a contact center resource other than the automated chat resource in response to the detected trigger event.

The present invention may include a computer-implemented method for providing a contextual summary of an interaction between a first party and a second party over a media channel in an interaction transfer to a third party according to claim <NUM> and a system for providing a contextual summary of an interaction between a first party and a second party over a media channel in an interaction transfer to a third party according to claim <NUM>.

A more complete appreciation of the present invention, and many of the attendant features and aspects thereof, will become more readily apparent as the invention becomes better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings in which like reference symbols indicate like components, wherein:.

For the purposes of promoting an understanding of the principles of the invention, reference will now be made to the exemplary embodiments illustrated in the drawings and specific language will be used to describe the same. It will be apparent, however, to one having ordinary skill in the art that the detailed material provided in the examples may not be needed to practice the present invention. In other instances, well-known materials or methods have not been described in detail in order to avoid obscuring the present invention. Additionally, further modification in the provided examples or application of the principles of the invention, as presented herein, are contemplated as would normally occur to those skilled in the art.

As used herein, language designating nonlimiting examples and illustrations includes "e.g.", "i.e.", "for example", "for instance" and the like. Further, reference throughout this specification to "an embodiment", "one embodiment", "present embodiments", "exemplary embodiments", "certain embodiments" and the like means that a particular feature, structure or characteristic described in connection with the given example may be included in at least one embodiment of the present invention. Thus, appearances of the phrases "an embodiment", "one embodiment", "present embodiments", "exemplary embodiments", "certain embodiments" and the like are not necessarily referring to the same embodiment or example. Further, particular features, structures or characteristics may be combined in any suitable combinations and/or sub-combinations in one or more embodiments or examples.

Embodiments of the present invention may be implemented as an apparatus, method, or computer program product. Accordingly, example embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects. In each case, the example embodiment may be generally referred to as a "module" or "system" or "method". Further, example embodiments may take the form of a computer program product embodied in any tangible medium of expression having computer-usable program code embodied in the medium.

It will be further appreciated that the flowchart and block diagrams provided in the figures illustrate architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to example embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

In a contact center setting, automated interactions between chatbots and customers can result in the transfer of the interaction to a human agent. This typically occurs during a chat conversation when a bot does not recognize a customer query or when the customer insists on talking with a human agent. When the interaction is transferred, it is common to provide the agent with a complete chat conversation between the customer and the bot. The customer is placed on hold while the agent reviews the entire chat conversation to understand the customer's requirement. This holding time is directly proportional to the length of the customer/bot interaction. As a result, hold time increases with the length of the chat conversation. Similarly, for a voicebot transfer to an agent, the agent often asks repetitive questions of the customer to understand their requirement. The interaction handle time increases as a result. In another example, the cold transfer of an interaction between a first human agent and a customer to a second human agent and the same customer will also result in a longer handle time. The second agent must read the entire chat conversation or ask repetitive questions to a customer in order to understand the customer query. In the event that the conversation again has to be transferred to another agent, the handle time will again increase as that agent also has to understand the entire interaction in order to understand the customer query. Thus, whenever there is a handoff from a bot to an agent, or an interaction transfer between at least two human agents, customers are put on hold or requested to answer repetitive questions to have their query addressed. The automatic creation of summaries of the interaction is to make the handoffs between the bot and agent or agent to agent more seamless and effective. This is achieved by providing an actionable summary to the transferred agent which captures enough information from the interaction to allow the agent to immediately act instead of having to review the entirety of the interaction history. According to the invention, the contextual summary and metadata comprises a list of suggested actions for the third party. In an embodiment, the actionable summary might include: a summary of the conversation between the parties of the interaction, sentiment indicators, churn prediction indicators, interaction reference indicators, and queue priority rank. Thus, the agent receiving the transferred interaction can understand the background of the issue within a short span of time, allowing for faster resolution. Customer wait time is drastically reduced with increased customer satisfaction through the avoidance of the agent asking repetitive questions. A generated summary is provided to a new agent in the example of bot to agent interaction transfers and also for agent to agent consultations or transfers. Along with the generated summary, other parameters (e.g., customer sentiment score, churn predictability, interaction reference, etc.) are provided that allow the agent to provide proactive solutions for the customer problem.

Turning now to <FIG>, a schematic block diagram of an exemplary computing device <NUM> is shown in accordance with embodiments of the present invention and/or with which exemplary embodiments of the present invention may be enabled or practiced. Those skilled in the art will recognize that the various systems and methods disclosed herein may be computer implemented using many different forms of data processing equipment, for example, digital microprocessors and associated memory executing appropriate software programs. It should therefore be appreciated that <FIG> is provided as a non-limiting example.

The computing device <NUM> may be implemented via firmware (e.g., an application-specific integrated circuit), hardware, or a combination of software, firmware, and hardware. It will be appreciated that each of the servers, controllers, switches, gateways, engines, and/or modules in the following figures (which collectively may be referred to as servers or modules) may be implemented via one or more of the computing devices <NUM>. For example, the various servers may be a process or thread running on one or more processors of one or more computing devices <NUM>, 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 system <NUM> of <FIG>-the various servers and computer devices thereof may be located on local computing devices <NUM> (i.e., on-site at the same physical location as the agents of the contact center), remote computing devices <NUM> (i.e., 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 exemplary 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, or the like.

As shown in the illustrated example, the computing device <NUM> may include a central processing unit (CPU) or processor <NUM> and a main memory <NUM>. The computing device <NUM> may also include a storage device <NUM>, removable media interface <NUM>, network interface <NUM>, and one or more input/output (I/O) devices <NUM>, which as depicted may include an I/O controller <NUM>, display device 135A, keyboard 135B, and pointing device 135C. The computing device <NUM> further may include additional elements, such as a memory port <NUM>, a bridge <NUM>, I/O ports, one or more additional input/output devices 135D, 135E, 135F, and a cache memory <NUM> in communication with the processor <NUM>.

The processor <NUM> may be any logic circuitry that responds to and processes instructions fetched from the main memory <NUM>. For example, the process <NUM> may be implemented by an integrated circuit, e.g., a microprocessor, microcontroller, or graphics processing unit, or in a field-programmable gate array or application-specific integrated circuit. As depicted, the processor <NUM> may communicate directly with the cache memory <NUM> via a secondary bus or backside bus. The cache memory <NUM> typically has a faster response time than main memory <NUM>. The main memory <NUM> may be one or more memory chips capable of storing data and allowing stored data to be directly accessed by the central processing unit <NUM>. The storage device <NUM> may provide storage for an operating system and software that run on the computing device <NUM>. The operating system may control scheduling tasks and access to system resources. Unless otherwise limited, the operating system and software may include any capable of performing the operations described herein, as would be appreciated by one of ordinary skill in the art.

As shown in the illustrated example, the computing device 100A may include a wide variety of I/O devices <NUM>. As shown, a I/O controller <NUM> may be used to control one or more I/O devices. As shown, Input devices may include the keyboard 135B and pointing device 135C, which, for example, may be a mouse or optical pen. Output devices, for example, may include video display devices, speakers and printers. The I/O devices <NUM> and/or the I/O controller <NUM> may include suitable hardware and/or software for enabling for the use of multiple display devices. The computing device <NUM> may also support one or more removable media interfaces <NUM>, such as a disk drive, USB port, or any other device suitable for reading data from or writing data to any type of computer readable media. The removable media interface <NUM>, for example, may be used for installing software and programs.

The computing device <NUM> may be any workstation, desktop computer, laptop or notebook computer, server machine, virtual device, mobile telephone, smart phone, portable telecommunication device, media playing device, gaming system, mobile computing device, or any other type of computing, telecommunications or media device, without limitation, capable of performing the operations described herein. The computing device <NUM> may have several input devices with each having different processors and operating systems. The computing device <NUM> may include a mobile device that combines several devices, such as a mobile phone having a digital audio player or portable media player.

The computing device <NUM> may be one of a plurality of devices connected by a network or connect to other systems and resources via a network. As used herein, a network includes one or more computing devices, machines, clients, client nodes, client machines, client computers, client devices, endpoints, or endpoint nodes in communication with one or more other computing devices, machines, clients, client nodes, client machines, client computers, client devices, endpoints, or endpoint nodes. As an example, a local machine may have the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients. The network may be LAN or WAN links, broadband connections, wireless connections, or some combination thereof, with connections being established using appropriate communication protocols. The computing device <NUM> may communicate with other computing devices <NUM> via 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 are also contemplated, such as, for example, the network (e.g., via software defined networking) or functions (e.g., via network functions virtualization).

With reference now to <FIG>, a communications infrastructure or contact center system <NUM> is shown in accordance with exemplary embodiments of the present invention and/or with which exemplary embodiments of the present invention may be enabled or practiced. It should be understood that the term "contact center system" is used herein to refer to the system depicted in <FIG> and/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 system <NUM>), the associated customer service provider (such as a particular customer service provider providing customer services through the contact center system <NUM>), as well as the organization or enterprise on behalf of which those customer services are being provided.

By way of background, customer service providers generally 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" or "customers"). 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, or the like.

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 the like. 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.

Referring specifically to <FIG>, the contact center system <NUM> may be used by a customer service provider to provide various types of services to customers. For example, the contact center system <NUM> may 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 system <NUM> may 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 aspect, the contact center system <NUM> may be operated by a third-party service provider that contracts to provide services for another organization. Further, the contact center system <NUM> may 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 system <NUM> may 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 system <NUM> may 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, "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.

In accordance with the illustrated example of <FIG>, the components or modules of the contact center system <NUM> may include: a plurality of customer devices 205A, 205B, 205C; communications network (or simply "network") <NUM>; switch/media gateway <NUM>; call controller <NUM>; interactive media response (IMR) server <NUM>; routing server <NUM>; storage device <NUM>; statistics (or "stat") server <NUM>; plurality of agent devices 230A, 230B, 230C that include workbins 232A, 232B, 232C, respectively; multimedia/social media server <NUM>; knowledge management server <NUM> coupled to a knowledge system <NUM>; chat server <NUM>; web servers <NUM>; interaction (or "iXn") server <NUM>; universal contact server (or "UCS") <NUM>; reporting server <NUM>; media services server <NUM>; and analytics module <NUM>. It should be understood that any of the computer-implemented components, modules, or servers described in relation to <FIG> or in any of the following figures may be implemented via types of computing devices, such as, for example, the computing device <NUM> of <FIG>. As will be seen, the contact center system <NUM> generally 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 the like.

Customers desiring to receive services from the contact center system <NUM> may initiate inbound communications (e.g., telephone calls, emails, chats, etc.) to the contact center system <NUM> via a customer device <NUM>. While <FIG> shows three such customer devices-i.e., customer devices 205A, 205B, and 205C-it should be understood that any number may be present. The customer devices <NUM>, 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 devices <NUM> to initiate, manage, and conduct communications with the contact center system <NUM>, 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 devices <NUM> may traverse the network <NUM>, with the nature of network typically depending on the type of customer device being used and form of communication. As an example, the network <NUM> may include a communication network of telephone, cellular, and/or data services. The network <NUM> may 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 network <NUM> may 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 <NUM>, <NUM>, LTE, <NUM>, etc..

In regard to the switch/media gateway <NUM>, it may be coupled to the network <NUM> for receiving and transmitting telephone calls between customers and the contact center system <NUM>. The switch/media gateway <NUM> may 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 switch <NUM> may 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 devices <NUM>. Thus, in general, the switch/media gateway <NUM> establishes a voice connection between the customer and the agent by establishing a connection between the customer device <NUM> and agent device <NUM>.

As further shown, the switch/media gateway <NUM> may be coupled to the call controller <NUM> which, for example, serves as an adapter or interface between the switch and the other routing, monitoring, and communication-handling components of the contact center system <NUM>. The call controller <NUM> may be configured to process PSTN calls, VoIP calls, etc. For example, the call controller <NUM> may include computer-telephone integration (CTI) software for interfacing with the switch/media gateway and other components. The call controller <NUM> may include a session initiation protocol (SIP) server for processing SIP calls. The call controller <NUM> may 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.

In regard to the interactive media response (IMR) server <NUM>, it may be configured to enable self-help or virtual assistant functionality. Specifically, the IMR server <NUM> may be similar to an interactive voice response (IVR) server, except that the IMR server <NUM> is not restricted to voice and may also cover a variety of media channels. In an example illustrating voice, the IMR server <NUM> may be configured with an IMR script for querying customers on their needs. For example, a contact center for a bank may tell customers via the IMR script to "press <NUM>" if they wish to retrieve their account balance. Through continued interaction with the IMR server <NUM>, customers may receive service without needing to speak with an agent. The IMR server <NUM> may 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 (referred to herein as "designer tool") which comprises a web-based tool for developing IVR applications and routing applications running in the contact center environment (e.g. Genesys ® Designer).

In regard to the routing server <NUM>, it may 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 server <NUM> may 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 server <NUM>. In doing this, the routing server <NUM> may 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 more below, may be stored in particular databases. Once the agent is selected, the routing server <NUM> may interact with the call controller <NUM> to route (i.e., connect) the incoming interaction to the corresponding agent device <NUM>. As part of this connection, information about the customer may be provided to the selected agent via their agent device <NUM>. This information is intended to enhance the service the agent is able to provide to the customer.

Regarding data storage, the contact center system <NUM> may include one or more mass storage devices-represented generally by the storage device <NUM>-for storing data in one or more databases relevant to the functioning of the contact center. For example, the storage device <NUM> may store customer data that is maintained in a customer database <NUM>. Such customer data may include 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 device <NUM> may store agent data in an agent database <NUM>. Agent data maintained by the contact center system <NUM> may include agent availability and agent profiles, schedules, skills, handle time, etc. As another example, the storage device <NUM> may store interaction data in an interaction database <NUM>. Interaction data may include data relating to numerous past interactions between customers and contact centers. More generally, it should be understood that, unless otherwise specified, the storage device <NUM> may 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 system <NUM> in ways that facilitate the functionality described herein. For example, the servers or modules of the contact center system <NUM> may query such databases to retrieve data stored therewithin or transmit data thereto for storage. The storage device <NUM>, 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.

In regard to the stat server <NUM>, it may be configured to record and aggregate data relating to the performance and operational aspects of the contact center system <NUM>. Such information may be compiled by the stat server <NUM> and made available to other servers and modules, such as the reporting server <NUM>, 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 devices <NUM> of the contact center <NUM> may be communication devices configured to interact with the various components and modules of the contact center system <NUM> in ways that facilitate functionality described herein. An agent device <NUM>, for example, may include a telephone adapted for regular telephone calls or VoIP calls. An agent device <NUM> may further include a computing device configured to communicate with the servers of the contact center system <NUM>, perform data processing associated with operations, and interface with customers via voice, chat, email, and other multimedia communication mechanisms according to functionality described herein. While <FIG> shows three such agent devices-i.e., agent devices 230A, 230B and 230C-it should be understood that any number may be present.

In regard to the multimedia/social media server <NUM>, it may be configured to facilitate media interactions (other than voice) with the customer devices <NUM> and/or the servers <NUM>. 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 server <NUM> may 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.

In regard to the knowledge management server <NUM>, it may be configured facilitate interactions between customers and the knowledge system <NUM>. In general, the knowledge system <NUM> may be a computer system capable of receiving questions or queries and providing answers in response. The knowledge system <NUM> may be included as part of the contact center system <NUM> or operated remotely by a third party. The knowledge system <NUM> may 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 system <NUM> as reference materials, as is known in the art. As an example, the knowledge system <NUM> may be embodied as IBM Watson or a like system.

In regard to the chat server <NUM>, it may be configured to conduct, orchestrate, and manage electronic chat communications with customers. In general, the chat server <NUM> is configured to implement and maintain chat conversations and generate chat transcripts. Such chat communications may be conducted by the chat server <NUM> in such a way that a customer communicates with automated chatbots, human agents, or both. In exemplary embodiments, the chat server <NUM> may 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 server <NUM> may be rules driven so to leverage an intelligent workload distribution among available chat resources. The chat server <NUM> further may implement, manage and facilitate user interfaces (also UIs) associated with the chat feature, including those UIs generated at either the customer device <NUM> or the agent device <NUM>. The chat server <NUM> may 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 server <NUM> may also be coupled to the knowledge management server <NUM> and the knowledge systems <NUM> for receiving suggestions and answers to queries posed by customers during a chat so that, for example, links to relevant articles can be provided.

In regard to the web servers <NUM>, such servers may 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 system <NUM>, it should be understood that the web servers <NUM> may be provided by third parties and/or maintained remotely. The web servers <NUM> may also provide webpages for the enterprise or organization being supported by the contact center system <NUM>. 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 system <NUM>, 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 servers <NUM>. 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).

In regard to the interaction (iXn) server <NUM>, it may be configured to manage deferrable activities of the contact center and the routing thereof to human agents for completion. As used herein, deferrable activities 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) server <NUM> may be configured to interact with the routing server <NUM> for selecting an appropriate agent to handle each of the deferable activities. Once assigned to a particular agent, the deferable activity is pushed to that agent so that it appears on the agent device <NUM> of the selected agent. The deferable activity may appear in a workbin <NUM> as a task for the selected agent to complete. The functionality of the workbin <NUM> may be implemented via any conventional data structure, such as, for example, a linked list, array, etc. Each of the agent devices <NUM> may include a workbin <NUM>, with the workbins 232A, 232B, and 232C being maintained in the agent devices 230A, 230B, and 230C, respectively. As an example, a workbin <NUM> may be maintained in the buffer memory of the corresponding agent device <NUM>.

In regard to the universal contact server (UCS) <NUM>, it may be configured to retrieve information stored in the customer database <NUM> and/or transmit information thereto for storage therein. For example, the UCS <NUM> may 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 UCS <NUM> may be configured to facilitate maintaining a history of customer preferences, such as preferred media channels and best times to contact. To do this, the UCS <NUM> may 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 database <NUM> or on other modules and retrieved as functionality described herein requires.

In regard to the reporting server <NUM>, it may be configured to generate reports from data compiled and aggregated by the statistics server <NUM> or 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, 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.

In regard to the media services server <NUM>, it may 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 the like.

In regard to the analytics module <NUM>, it may 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 module <NUM> also may generate, update, train, and modify predictors or models <NUM> based on collected data, such as, for example, customer data, agent data, and interaction data. The models <NUM> 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 <NUM> is depicted 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 module <NUM> may have access to the data stored in the storage device <NUM>, including the customer database <NUM> and agent database <NUM>. The analytics module <NUM> also may have access to the interaction database <NUM>, 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, as discussed more below, the analytic module <NUM> may be configured to retrieve data stored within the storage device <NUM> for use in developing and training algorithms and models <NUM>, for example, by applying machine learning techniques.

One or more of the included models <NUM> 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 <NUM> may be used in natural language processing and, for example, include intent recognition and the like. The models <NUM> may be developed based upon <NUM>) known first principle equations describing a system, <NUM>) data, resulting in an empirical model, or <NUM>) 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, it may be preferable that the models <NUM> 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 is presently a preferred embodiment for implementing the models <NUM>. Neural networks, for example, may be developed based upon empirical data using advanced regression algorithms.

The analytics module <NUM> may further include an optimizer <NUM>. 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 <NUM> may be non-linear, the optimizer <NUM> may be a nonlinear programming optimizer. It is contemplated, however, that the present invention 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 exemplary embodiments, the models <NUM> and the optimizer <NUM> may together be used within an optimization system <NUM>. For example, the analytics module <NUM> may utilize the optimization system <NUM> 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 aspects 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 of <FIG> (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 as set out in the appended claims. 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 system <NUM> may be affected through user interfaces (UIs) which may be generated on the customer devices <NUM> and/or the agent devices <NUM>. As already noted, the contact center system <NUM> may operate as a hybrid system in which some or all components are hosted remotely, such as in a cloud-based or cloud computing environment.

Turning to <FIG>, various aspects of chat systems and chatbots are shown. As will be seen, present embodiments may include or be enabled by such chat features, which, in general, enable the exchange of text messages between different parties. Those parties may include live persons, such as customers and agents, as well as automated processes, such as bots or chatbots.

By way of background, a bot (also known as an "Internet bot") is a software application that runs automated tasks or scripts over the Internet. Typically, bots perform tasks that are both simple and structurally repetitive at a much higher rate than would be possible for a person. A chatbot is a particular type of bot and, as used herein, is defined as a piece of software and/or hardware that conducts a conversation via auditory or textual methods. As will be appreciated, chatbots are often designed to convincingly simulate how a human would behave as a conversational partner. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatbots use sophisticated natural language processing systems, while simpler ones scan for keywords within the input and then select a reply from a database based on matching keywords or wording pattern.

Before proceeding further with the description of the present invention, an explanatory note will be provided in regard to referencing system components-e.g., modules, servers, and other components-that have already been introduced in any previous figure. Whether or not the subsequent reference includes the corresponding numerical identifiers used in the previous figures, it should be understood that the reference incorporates the example described in the previous figures and, unless otherwise specifically limited, may be implemented in accordance with either that examples or other conventional technology capable of fulfilling the desired functionality, as would be understood by one of ordinary skill in the art. Thus, for example, subsequent mention of a "contact center system" should be understood as referring to the exemplary "contact center system <NUM>" of <FIG> and/or other conventional technologies for implementing a contact center system. As additional examples, a subsequent mention below to a "customer device", "agent device", "chat server", or "computing device" should be understood as referring to the exemplary "customer device <NUM>", "agent device <NUM>", "chat server <NUM>", or "computing device <NUM>", respectively, of <FIG>, as well as conventional technology for fulfilling the same functionality.

Chat features and chatbots will now be discussed in greater specificity with reference to the exemplary embodiments of a chat server, chatbot, and chat interface depicted, respectively, in <FIG>. While these examples are provided with respect to chat systems implemented on the contact center-side, such chat systems may be used on the customer-side of an interaction. Thus, it should be understood that the exemplary chat systems of <FIG> may be modified for analogous customer-side implementation, including the use of customer-side chatbots configured to interact with agents and chatbots of contact centers on a customer's behalf. It should further be understood that chat features may be utilized by voice communications via converting text-to-speech and/or speech-to-text.

Referring specifically now to <FIG>, a more detailed block diagram is provided of a chat server <NUM>, which may be used to implement chat systems and features. The chat server <NUM> may be coupled to (i.e., in electronic communication with) a customer device <NUM> operated by the customer over a data communications network <NUM>. The chat server <NUM>, for example, may be operated by an enterprise as part of a contact center for implementing and orchestrating chat conversations with the customers, including both automated chats and chats with human agents. In regard to automated chats, the chat server <NUM> may host chat automation modules or chatbots 260A-260C (collectively referenced as <NUM>), which are configured with computer program instructions for engaging in chat conversations. Thus, generally, the chat server <NUM> implements chat functionality, including the exchange of text-based or chat communications between a customer device <NUM> and an agent device <NUM> or a chatbot <NUM>. As discussed more below, the chat server <NUM> may include a customer interface module <NUM> and agent interface module <NUM> for generating particular UIs at the customer device <NUM> and the agent device <NUM>, respectively, that facilitate chat functionality.

In regard to the chatbots <NUM>, each can operate as an executable program that is launched according to demand. For example, the chat server <NUM> may operate as an execution engine for the chatbots <NUM>, analogous to loading VoiceXML files to a media server for interactive voice response (IVR) functionality. Loading and unloading may be controlled by the chat server <NUM>, analogous to how a VoiceXML script may be controlled in the context of an interactive voice response. The chat server <NUM> may further provide a means for capturing and collecting customer data in a unified way, similar to customer data capturing in the context of IVR. Such data can be stored, shared, and utilized in a subsequent conversation, whether with the same chatbot, a different chatbot, an agent chat, or even a different media type. In example embodiments, the chat server <NUM> is configured to orchestrate the sharing of data among the various chatbots <NUM> as interactions are transferred or transitioned over from one chatbot to another or from one chatbot to a human agent. The data captured during interaction with a particular chatbot may be transferred along with a request to invoke a second chatbot or human agent.

In exemplary embodiments, the number of chatbots <NUM> may vary according to the design and function of the chat server <NUM> and is not limited to the number illustrated in <FIG>. Further, different chatbots may be created to have different profiles, which can then be selected between to match the subject matter of a particular chat or a particular customer. For example, the profile of a particular chatbot may include expertise for helping a customer on a particular subject or communication style aimed at a certain customer preference. More specifically, one chatbot may be designed to engage in a first topic of communication (e.g., opening a new account with the business), while another chatbot may be designed to engage in a second topic of communication (e.g., technical support for a product or service provided by the business). Or, chatbots may be configured to utilize different dialects or slang or have different personality traits or characteristics. Engaging chatbots with profiles that are catered to specific types of customers may enable more effective communication and results. The chatbot profiles may be selected based on information known about the other party, such as demographic information, interaction history, or data available on social media. The chat server <NUM> may host a default chatbot that is invoked if there is insufficient information about the customer to invoke a more specialized chatbot. Optionally, the different chatbots may be customer selectable. In exemplary embodiments, profiles of chatbots <NUM> may be stored in a profile database hosted in the storage device <NUM>. Such profiles may include the chatbot's personality, demographics, areas of expertise, and the like.

The customer interface module <NUM> and agent interface module <NUM> may be configured to generate user interfaces (UIs) for display on the customer device <NUM> that facilitate chat communications between the customer and a chatbot <NUM> or human agent. Likewise, an agent interface module <NUM> may generate particular UIs on the agent device <NUM> that facilitate chat communications between an agent operating an agent device <NUM> and the customer. The agent interface module <NUM> may also generate UIs on an agent device <NUM> that allow an agent to monitor aspects of an ongoing chat between a chatbot <NUM> and a customer. For example, the customer interface module <NUM> may transmit signals to the customer device <NUM> during a chat session that are configured to generated particular UIs on the customer device <NUM>, which may include the display of the text messages being sent from the chatbot <NUM> or human agent as well as other non-text graphics that are intended to accompany the text messages, such as emoticons or animations. Similarly, the agent interface module <NUM> may transmit signals to the agent device <NUM> during a chat session that are configured to generated UIs on the agent device <NUM>. Such UIs may include an interface that facilitates the agent selection of non-text graphics for accompanying outgoing text messages to customers.

In exemplary embodiments, the chat server <NUM> may be implemented in a layered architecture, with a media layer, a media control layer, and the chatbots executed by way of the IMR server <NUM> (similar to executing a VoiceXML on an IVR media server). As described above, the chat server <NUM> may be configured to interact with the knowledge management server <NUM> to query the server for knowledge information. The query, for example, may be based on a question received from the customer during a chat. Responses received from the knowledge management server <NUM> may then be provided to the customer as part of a chat response.

Referring specifically now to <FIG>, a block diagram is provided of an exemplary chat automation module or chatbot <NUM>. As illustrated, the chatbot <NUM> may include several modules, including a text analytics module <NUM>, dialog manager <NUM>, and output generator <NUM>. It will be appreciated that, in a more detailed discussion of chatbot operability, other subsystems or modules may be described, including, for examples, modules related to intent recognition, text-to-speech or speech-to-text modules, as well as modules related to script storage, retrieval, and data field processing in accordance with information stored in agent or customer profiles. Such topics, however, are covered more completely in other areas of this disclosure-for example, in relation to <FIG>-and so will not be repeated here. It should nevertheless be understood that the disclosures made in these areas may be used in analogous ways toward chatbot operability in accordance with functionality described herein.

The text analytics module <NUM> may be configured to analyze and understand natural language. In this regard, the text analytics module may be configured with a lexicon of the language, syntactic/semantic parser, and grammar rules for breaking a phrase provided by the customer device <NUM> into an internal syntactic and semantic representation. The configuration of the text analytics module depends on the particular profile associated with the chatbot. For example, certain words may be included in the lexicon for one chatbot but excluded that of another.

The dialog manager <NUM> receives the syntactic and semantic representation from the text analytics module <NUM> and manages the general flow of the conversation based on a set of decision rules. In this regard, the dialog manager <NUM> maintains a history and state of the conversation and, based on those, generates an outbound communication. The communication may follow the script of a particular conversation path selected by the dialog manager <NUM>. As described in further detail below, the conversation path may be selected based on an understanding of a particular purpose or topic of the conversation. The script for the conversation path may be generated using any of various languages and frameworks conventional in the art, such as, for example, artificial intelligence markup language (AIML), SCXML, or the like.

During the chat conversation, the dialog manager <NUM> selects a response deemed to be appropriate at the particular point of the conversation flow/script and outputs the response to the output generator <NUM>. In exemplary embodiments, the dialog manager <NUM> may also be configured to compute a confidence level for the selected response and provide the confidence level to the agent device <NUM>. Every segment, step, or input in a chat communication may have a corresponding list of possible responses. Responses may be categorized based on topics (determined using a suitable text analytics and topic detection scheme) and suggested next actions are assigned. Actions may include, for example, responses with answers, additional questions, transfer to a human agent to assist, and the like. The confidence level may be utilized to assist the system with deciding whether the detection, analysis, and response to the customer input is appropriate or whether a human agent should be involved. For example, a threshold confidence level may be assigned to invoke human agent intervention based on one or more business rules. In exemplary embodiments, confidence level may be determined based on customer feedback. As described, the response selected by the dialog manager <NUM> may include information provided by the knowledge management server <NUM>.

In exemplary embodiments, the output generator <NUM> takes the semantic representation of the response provided by the dialog manager <NUM>, maps the response to a chatbot profile or personality (e.g., by adjusting the language of the response according to the dialect, vocabulary, or personality of the chatbot), and outputs an output text to be displayed at the customer device <NUM>. The output text may be intentionally presented such that the customer interacting with a chatbot is unaware that it is interacting with an automated process as opposed to a human agent. As will be seen, in accordance with other embodiments, the output text may be linked with visual representations, such as emoticons or animations, integrated into the customer's user interface.

Reference will now be made to <FIG>, in which a webpage <NUM> having an exemplary implementation of a chat feature <NUM> is presented. The webpage <NUM>, for example, may be associated with a enterprise website and intended to initiate interaction between prospective or current customers visiting the webpage and a contact center associated with the enterprise. As will be appreciated, the chat feature <NUM> may be generated on any type of customer device <NUM>, including personal computing devices such as laptops, tablet devices, or smart phones. Further, the chat feature <NUM> may be generated as a window within a webpage or implemented as a full-screen interface. As in the example shown, the chat feature <NUM> may be contained within a defined portion of the webpage <NUM> and, for example, may be implemented as a widget via the systems and components described above and/or any other conventional means. In general, the chat feature <NUM> may include an exemplary way for customers to enter text messages for delivery to a contact center.

As an example, the webpage <NUM> may be accessed by a customer via a customer device, such as the customer device, which provides a communication channel for chatting with chatbots or live agents. In exemplary embodiments, as shown, the chat feature <NUM> includes generating a user interface, which is referred to herein as a customer chat interface <NUM>, on a display of the customer device. The customer chat interface <NUM>, for example, may be generated by the customer interface module of a chat server, such as the chat server, as already described. As described, the customer interface module <NUM> may send signals to the customer device <NUM> that are configured to generate the desired customer chat interface <NUM>, for example, in accordance with the content of a chat message issued by a chat source, which, in the example, is a chatbot or agent named "Kate". The customer chat interface <NUM> may be contained within a designated area or window, with that window covering a designated portion of the webpage <NUM>. The customer chat interface <NUM> also may include a text display area <NUM>, which is the area dedicated to the chronological display of received and sent text messages. The customer chat interface <NUM> further includes a text input area <NUM>, which is the designated area in which the customer inputs the text of their next message. As will be appreciated, other configurations are also possible.

This specification includes systems and methods for automating and augmenting customer actions during various stages of interaction with a customer service provider or contact center. These are described to aid understanding of the invention. As will be seen, those various stages of interaction may be classified as pre-contact, during-contact, and post-contact stages (or, respectively, pre-interaction, during-interaction, and post-interaction stages). With specific reference now to <FIG>, an exemplary customer automation system <NUM> is shown that may be used with embodiments of the present invention. To better explain how the customer automation system <NUM> functions, reference will also be made to <FIG>, which provides a flowchart <NUM> of an exemplary method for automating customer actions when, for example, the customer interacts with a contact center. Additional information related to customer automation are provided in U. Application Ser. No. <CIT>, and published on January <NUM>, <NUM> as <CIT> entitled "System and Method for Customer Experience Automation".

The customer automation system <NUM> of <FIG> represents a system that may be generally used for customer-side automations, which, as used herein, refers to the automation of actions taken on behalf of a customer in interactions with customer service providers or contact centers. Such interactions may also be referred to as "customer-contact center interactions" or simply "customer interactions". Further, in discussing such customer-contact center interactions, it should be appreciated that reference to a "contact center" or "customer service provider" is intended to generally refer to any customer service department or other service provider associated with an organization or enterprise (such as, for example, a business, governmental agency, non-profit, school, etc.) with which a user or customer has business, transactions, affairs or other interests.

In exemplary embodiments, the customer automation system <NUM> may be implemented as a software program or application running on a mobile device or other computing device, cloud computing devices (e.g., computer servers connected to the customer device <NUM> over a network), or combinations thereof (e.g., some modules of the system are implemented in the local application while other modules are implemented in the cloud. For the sake of convenience, embodiments are primarily described in the context of implementation via an application running on the customer device <NUM>. However, it should be understood that present embodiments are not limited thereto.

The customer automation system <NUM> may include several components or modules. In the illustrated example of <FIG>, the customer automation system <NUM> includes a user interface <NUM>, natural language processing (NLP) module <NUM>, intent inference module <NUM>, script storage module <NUM>, script processing module <NUM>, customer profile database or module (or simply "customer profile") <NUM>, communication manager module <NUM>, text-to-speech module <NUM>, speech-to-text module <NUM>, and application programming interface (API) <NUM>, each of which will be described with more particularity with reference also to flowchart <NUM> of <FIG>. It will be appreciated that some of the components of and functionalities associated with the customer automations system <NUM> may overlap with the chatbot systems described above in relation to <FIG>. In cases where the customer automation system <NUM> and such chatbot systems are employed together as part of a customer-side implementation, such overlap may include the sharing of resources between the two systems.

In an example of operation, with specific reference now to the flowchart <NUM> of <FIG>, the customer automation system <NUM> may receive input at an initial step or operation <NUM>. Such input may come from several sources. For example, a primary source of input may be the customer, where such input is received via the customer device. The input also may include data received from other parties, particularly parties interacting with the customer through the customer device. For example, information or communications sent to the customer from the contact center may provide aspects of the input. In either case, the input may be provided in the form of free speech or text (e.g., unstructured, natural language input). Input also may include other forms of data received or stored on the customer device.

Continuing with the flow diagram <NUM>, at an operation <NUM>, the customer automation system <NUM> parses the natural language of the input using the NLP module <NUM> and, therefrom, infers an intent using the intent inference module <NUM>. For example, where the input is provided as speech from the customer, the speech may be transcribed into text by a speech-to-text system (such as a large vocabulary continuous speech recognition or LVCSR system) as part of the parsing by the NLP module <NUM>. The transcription may be performed locally on the customer device <NUM> or the speech may be transmitted over a network for conversion to text by a cloud-based server. In certain embodiments, for example, the intent inference module <NUM> may automatically infer the customer's intent from the text of the provided input using artificial intelligence or machine learning techniques. Such artificial intelligence techniques may include, for example, identifying one or more keywords from the customer input and searching a database of potential intents corresponding to the given keywords. The database of potential intents and the keywords corresponding to the intents may be automatically mined from a collection of historical interaction recordings. In cases where the customer automation system <NUM> fails to understand the intent from the input, a selection of several intents may be provided to the customer in the user interface <NUM>. The customer may then clarify their intent by selecting one of the alternatives or may request that other alternatives be provided.

After the customer's intent is determined, the flowchart <NUM> proceeds to an operation <NUM> where the customer automation system <NUM> loads a script associated with the given intent. Such scripts, for example, may be stored and retrieved from the script storage module <NUM>. Such scripts may include a set of commands or operations, pre-written speech or text, and/or fields of parameters or data (also "data fields"), which represent data that is required to automate an action for the customer. For example, the script may include commands, text, and data fields that will be needed in order to resolve the issue specified by the customer's intent. Scripts may be specific to a particular contact center and tailored to resolve particular issues. Scripts may be organized in a number of ways, for example, in a hierarchical fashion, such as where all scripts pertaining to a particular organization are derived from a common "parent" script that defines common features. The scripts may be produced via mining data, actions, and dialogue from previous customer interactions. Specifically, the sequences of statements made during a request for resolution of a particular issue may be automatically mined from a collection of historical interactions between customers and customer service providers. Systems and methods may be employed for automatically mining effective sequences of statements and comments, as described from the contact center agent side, are described in <CIT> and published as on July <NUM>, <NUM> as <CIT>.

With the script retrieved, the flowchart <NUM> proceeds to an operation <NUM> where the customer automation system <NUM> processes or "loads" the script. This action may be performed by the script processing module <NUM>, which performs it by filling in the data fields of the script with appropriate data pertaining to the customer. More specifically, the script processing module <NUM> may extract customer data that is relevant to the anticipated interaction, with that relevance being predetermined by the script selected as corresponding to the customer's intent. The data for many of the data fields within the script may be automatically loaded with data retrieved from data stored within the customer profile <NUM>. As will be appreciated, the customer profile <NUM> may store particular data related to the customer, for example, the customer's name, birth date, address, account numbers, authentication information, and other types of information relevant to customer service interactions. The data selected for storage within the customer profile <NUM> may be based on data the customer has used in previous interactions and/or include data values obtained directly by the customer. In case of any ambiguity regarding the data fields or missing information within a script, the script processing module <NUM> may include functionality that prompts and allows the customer to manually input the needed information.

Referring again to the flowchart <NUM>, at an operation <NUM>, the loaded script may be transmitted to the customer service provider or contact center. As discussed more below, the loaded script may include commands and customer data necessary to automate at least a part of an interaction with the contact center on the customer's behalf. In exemplary embodiments, an API <NUM> is used so to interact with the contact center directly. Contact centers may define a protocol for making commonplace requests to their systems, which the API <NUM> is configured to do. Such APIs may be implemented over a variety of standard protocols such as Simple Object Access Protocol (SOAP) using Extensible Markup Language (XML), a Representational State Transfer (REST) API with messages formatted using XML or JavaScript Object Notation (JSON), and the like. Accordingly, the customer automation system <NUM> may automatically generate a formatted message in accordance with a defined protocol for communication with a contact center, where the message contains the information specified by the script in appropriate portions of the formatted message.

<FIG> is a block diagram of an embodiment of the channel architecture. A voice channel <NUM> and a chat channel <NUM> are illustrated for simplicity, however it is within the scope of the embodiments for there to be a plurality of channels (omnichannel) of any media type the contact center is capable of handling, such as voice, chat, SMS, email, etc. The scope of the invention is defined by the appended claims.

For voice interactions, the interaction is received at a SIP server located at the call controller <NUM> of <FIG>. The SIP server <NUM> sends a request to the Orchestration Server (ORS) through a T-lib interface. The ORS functions in connection with the URS <NUM> as an orchestration platform for interactions. The Automatic Speech Recognition (ASR) connector component, as part of the media services <NUM> from the contact center system in <FIG>, converts the voice interaction <NUM> into text. In an embodiment, the voice interaction may be a call between a customer and an agent. In another embodiment, the voice interaction may comprise a voice interaction between a customer and a bot. The converted text is sent to the Designer <NUM> and to the Contextual Summarizer <NUM>. The Contextual Summarizer is comprised of a plurality of APIs that may be customized by a contact center. For example, the Contextual Summarizer might comprise: a text summarizer API, a churn predictor API, a sentiment analysis API, a next best action API, and an interaction reference detector API. The Text Summarizer API receives the text transcript of the conversation that happened prior to the transfer as input. It processes the transcript to produce a summary using summarization techniques known in the art (e.g. Extractive Summarization and Abstractive Summarization). A transfer may be triggered, or initiated, by either party to the interaction. For example, a chat bot may determine that the customer needs to speak with a human agent based on the dialog. The customer may also make the request. An agent may decide they aren't able to successfully handle an interaction and offer to transfer the interaction to a more specialized agent or a manager.

Extractive Summarization involves identifying the important sentences or excerpts from the text and reproducing them verbatim as part of the summary. No new text is generated; only existing text is used in the summarization process. For example, the original text transcript of the conversation might be: "I opted for the cell provider SIM and recharged with the $<NUM><NUM> plan. While I am traveling, in non-<NUM> coverage areas, I am not able to browse data. At least, I want to be able to use <NUM> service in the area. Blocking data is not acceptable. If it continues, I will switch to other networks. " An extractive summary might read: "I opted cell provider, recharged $<NUM><NUM> plan. In non-<NUM> areas, I am not able to browse data. I want to use <NUM> service in the area. If continues, I will switch to other networks. " Abstractive summarization employs more powerful natural language processing techniques to interpret text and generate new summary text, as opposed to selecting the most representative existing excerpts to perform the summarization. For the same original text transcript example above, the abstractive summary might read as: "I recharged with $<NUM><NUM> cell provider plan and unable to browse data in non <NUM> areas. If it persists, will switch to other networks.

The Churn Predicter API receives the voice/chat transcript as input and analyzes the transcript for churn indication. If the churn indicator level is high, this API indicates the churn as high in the summary.

The sentiment analysis API receives the voice/chat transcript as input and searches for the customer sentiments in the transcript (e.g., positive, negative, and neutral). The information on the customer's sentiment is passed to the agent along with the summary after this API is triggered.

The next best action API provides suggested responses and actions to be performed by an agent to assist the customer in an efficient way. For example, a transcript might read "I opted for the cell provider SIM and recharged with the $<NUM><NUM> plan. While I am traveling, in non-<NUM> coverage areas, I am not able to browse data. At the very least, I want to use <NUM> service in the area. Blocking data is not acceptable. If it continues, I will switch to other networks. " Suggested actions may appear to the agent based on the interaction in order to achieve a desired outcome, such as "Offer free <NUM> pack" or "Advise Roaming instructions".

The interaction reference detector API may look for information in the chat/call history, and in the current chat transcript, that the customer has previously reached out to the contact center for the same issue. The API may trigger an indication in the summary whether the customer already interacted with the contact center for the same query not through metadata.

The above is not an exhaustive list of APIs that may be included in the contextual summarizer. While a few examples have been provided, other APIs may also be included or deleted specific to the contact center and depending on the desired information.

The Contextual Summarizer <NUM> is operably connected with the Designer tool <NUM> through a REST API. The Designer tool <NUM> communicates with the media services <NUM> through an HTTP connection and the UCS <NUM> through a Transmission Control Protocol (TCP) connection. The Contextual Summarizer <NUM> is operably connected to the contact center system <NUM> and provides the summaries of the interaction to the agent device(s) <NUM>.

For chat interactions, the interaction is received at a widget located at the web servers <NUM> of <FIG>. Widget is operatively connected to a gateway <NUM> for the bot <NUM>. The gateway <NUM> is operably connected with the designer (via HTTP, for example), which connects with the Universal Contact Server <NUM>.

An example conversation in an interaction between a bot and a customer prior to transfer to a human agent is provided as follows:.

When there is a transfer from a Bot to an agent, the chat transcript is retrieved for the current interaction from the UCS <NUM> database by the Designer <NUM> through a network connection (e.g. TCP). The transcript is provided to the Contextual Summarizer <NUM> (via a connection such as a REST API) in a web request. The Contextual Summarizer <NUM> will process the transcript and send the final Summary and detected Metadata as a response back to the web request. The summary and metadata as sent as 'Attach data' to the agent <NUM> in the contact center system <NUM>. In an embodiment, the following summary might be provided to an agent:
"Customer wants to change his plan with more data. Bot offered Freedom-<NUM> plan. Customer feels that suggested plan is too costly and another provider offers more data for much less.

Metadata may also be provided in the summary, such as:.

The agent <NUM> is able to view the Summary and Metadata in a designated section of their UI at their workstation. The agent can selection the best action from a list of suggested actions and use the associated suggested response to form a quick reply. Once the interaction has completed, the notes section in the Agent desktop may also be automatically updated with the summary of the full conversation (the conversation between the bot and the customer and the Agent and the customer) so that the agent does not have to spend their time filling in the notes and can quickly move on to the next interaction.

In another embodiment, when a voice interaction between a customer and a first agent is transferred to a second agent, the second agent has to read through the entire conversation while keeping the customer waiting and/or asking repetitive questions to understand the reason for the transfer and customer problem. A sample conversation may appear as such:.

Whenever there is a call transfer from one agent to another, the designer <NUM> is executed to obtain the voice transcript from the ASR connector <NUM>. Once the voice transcript has been obtained, the designer <NUM> sends this transcript to the Contextual Summarizer <NUM> in a web request. The Contextual Summarizer <NUM> processes the transcript and sends the final summary and detected metadata as a response back to the web request. The summary and metadata are provided as "attach data" to the next agent. Using embodiments described herein, the second agent is provided with a summary as follows:.

"Customer wants to go on a trip from New York or Philadelphia to Amsterdam or Paris on August <NUM>, <NUM>. As no trip available for <NUM> to <NUM> adults in price range of <NUM> to <NUM> USD, customer wants to speak to manager when requested to increase budget.

Claim 1:
A computer-implemented method for providing a contextual summary of an interaction (<NUM>, <NUM>) between a first party (<NUM>) and a second party (<NUM>, <NUM>) over a media channel in an interaction transfer to a third party (<NUM>), the method comprising:
receiving a request to transfer an interaction (<NUM>, <NUM>) to the third party (<NUM>), the request being triggered from the interaction (<NUM>, <NUM>);
obtaining a text transcript of the interaction (<NUM>, <NUM>);
processing the transcript to obtain the contextual summary and metadata; and
providing the contextual summary and metadata to the third party (<NUM>) for handling the interaction (<NUM>, <NUM>) with the interaction transfer, wherein the contextual summary and metadata further comprises a list of suggested actions for the third party (<NUM>).