An information processing method performed by a server includes receiving a request to initiate communication over an instant messaging system based on a connected voice call of a terminal, the request including a telephone number of the terminal, determining whether a particular user identifier of a user of the terminal is stored in a memory of the server based on the telephone number, the memory of the server storing one or more user identifiers in association with one or more telephone numbers, the one or more user identifiers being associated with the instant messaging system, and sending a message to the terminal based on the particular user identifier in response to determining the particular user identifier is stored in the memory.

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.

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 signal indicating a communication is being initiated by an end user operating an end user device; receive information from the end user device regarding the communication; select a conversation path based on the information regarding the communication; invoke an automated chat resource for engaging in a chat communication session with the end user device, wherein the automated chat resource is configured to follow the conversation path during the chat communication session; monitor confidence levels of automated chat responses during the chat communication session; and redirect the chat communication session to a second conversation path in response to at least one of the confidence levels falling below a threshold level.

A system for automated contact center agent thick client workstation testing. The invention uses a workstation test execution manager. The workstation test execution manager acts as the interface between the client interaction software systems testing system and analyst controlled test device, executes an extensive set of robust test directive commands with underlying routines to be used to specify test conditions without the use of programming ability on the part of the analyst, uses a robust set of report item and format choice designators to allow easy selection of a range of report content and styles.

Systems, methods, and non-transitory computer-readable media can establish a voice communication with a computing device over one or more networks, wherein the voice communication was initiated by a user operating the computing device, and wherein the computing system is associated with an entity; determine a user selection of an option to interact with one or more representatives of the entity over a messaging session, the option being provided by the computing system during the voice communication; and establish the messaging session between the user operating the computing device and at least one representative of the entity.

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors perform: receiving, by a chat bot and from a user, an indication to talk to a live agent; calling, by the chat bot, an automatic call distribution server to initiate a live-agent session for the user; storing metadata for the live-agent session in a distributed table and a distributed queue; pulling, from an automatic call distribution connector, one or more messages from a conversation between the user and the chat bot based on the metadata; and connecting the live agent to the user in the live-agent session, wherein the user communicates with the live agent in the live-agent session through a same interface for the chat bot. Other embodiments are disclosed.

Methods for providing contextual information about communication devices and/or services. Profile information indicating a user's communication devices and/or services is stored. When profile information or a message log is displayed, the user may identify a communication device and/or service. A query inquires whether the user would like contextual information. The contextual information may be obtained from a gateway in a data network or a telecommunications manager in a telecommunication network. The contextual information is displayed. Profile information about a user's communicating partner may be stored. When the user makes an effort to communicate with the partner, a check may be made of the partner's profile information for a preferred communication device and/or service. If the user does not have a communication device and/or service corresponding to that of the communicating partner, a query may inquire whether the user would like contextual information. If so, the contextual information is presented.

A method, device, and computer-readable medium provide for receiving, via a chatbot access channel, a chat message from a user device associated with a customer chat session; determining that the chat message includes a customer intent that corresponds to a chat flow for the customer chat session; generating one or more suggested response messages based on the chat message, wherein at least one of the one or more suggested response messages includes a previously stored chat message response corresponding to the customer intent and approved by a service agent; presenting, via a display, a transcript of a messaging sequence for the customer chat session concurrently with a user interface that enables the service agent to perform an action with respect to the one or more suggested response messages; and sending, via the chatbot access channel, a selected one of the one or more suggested response messages to the user device.

A telephone subnet crawler is used to access automated telephone response systems and index the information, contents and structure contained therein. A database of the information, contents and structure of a plurality of automated telephone response systems is created by the telephone subnet crawler. A user interface provides a waiting party with direct access to the information, contents and structure of the automated telephone response systems contained in the database. Where an automated telephone response system requires user input, the user interface calls the automated telephone response system and navigates to the node requiring user input, provides the user input and displays the results to the user. Where an automated telephone response system connects to an operator, the user interface calls the automated telephone response system, navigates to the node for an operator, and when an operator is detected, calls the user at a user provided callback number.

Machine learning models may be used during a communications session to process natural language communications and perform actions relating to the communications session. For example, a machine learning model may be used to provide an automated response to a user, to suggest a completion of text being entered by a user, or to provide information about a relevant resource. Machine learning models may rely on machine learning model data that is updated during a communications session as communications are processed by the machine learning model. To improve the performance of a machine learning model when a user leaves a first communications session and enters a second communications session, the machine learning model data may be stored during a first communications session and then retrieved during the second communications session to initialize a machine learning model for the second communications session.