A system establishes an agent communication with a customer. An agent computer with a first processor and a first memory receives a batch notification identifying a batch of one or more customer records in the agent computer, for communication in the near future. The one or more customer records is associated with one or more customers. A second computer with a second processor and a second memory provides the batch notification identifying the batch of one or more customer records in the agent computer. The system determines a next step action by taking into account touch specific outcome correlation data, marketing outcome correlation data, and/or correlation between touch specific outcome correlation data and overall outcome data.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

Agent conversation item selection is automated by a server that automatically detects speech in a call and converts that speech to text. Software running on the server retrieves one or more items from a data store based on a determination that the text includes one or more keywords or a change in the subject of the call. The keywords can include phrases. The retrieved items include one or more of scripts, articles, manuals, daily bulletins regarding a system state, or any resource that can be used to assist with a customer call or interaction. The software running on the server generates a user interface (UI) output based on the retrieve items, and transmits the UI output to an agent device. Software running on the agent device receives the UI output and displays the retrieved items on a display of the agent device.

Customer relationship management (“CRM”) implemented in a computer system, including parsing a word, from call notes of a conversation between a tele-agent of a call center and a customer representative, into a parsed triple of a description logic; determining whether the parsed triple is recorded in a semantic CRM triple store of the computer system; if the parsed triple is not recorded in the semantic CRM triple store, recording the parsed triple as a call note in the semantic CRM triple store.

A system comprises a database configured to store a plurality of baseline scripts, an applications server communicatively coupled to the database, and an analytics engine. Each of the plurality of baseline scripts is associated with a client of a call center and comprises a plurality of questions for guiding a communication session between an agent and a party. The applications server is configured to monitor a plurality of communication sessions. Each of the plurality of communication sessions is guided by a respective one of the plurality of baseline scripts. The applications server is further configured to obtain information about an actual call flow of each of the communication sessions and send the obtained information about the actual call flow of each of the communication sessions to the analytics engine. The analytics engine is configured to determine one or more proposed changes to a baseline script.

A system establishes an agent communication with a customer. An agent computer with a first processor and a first memory receives a batch notification identifying a batch of one or more customer records in the agent computer, for communication in the near future. The one or more customer records is associated with one or more customers. A second computer with a second processor and a second memory provides the batch notification identifying the batch of one or more customer records in the agent computer. The system connects the agent with the customer associated with a customer record selected from the batch by the agent.

A system for managing a communication session between a number of participants is provided. The system can analyze activity of each participant and generate recommendations on the communication flow of a meeting. For instance, the system can analyze user activity and other contextual data for each user to determine a recommended speaking arrangement for the participants. The recommendations can be presented to a moderator who can then utilize the recommendations to control the flow of the meeting. Inputs received from the moderator can modify speaking and viewing permissions to emphasize or deemphasize displays and audio of individual participants. In some configurations, the recommendations can be utilized by a system to automatically control permissions for each participant for the purposes of controlling communication and flow of an event, such as an online meeting.

This disclosure relates to a method of highlighting at least a part of communication segments between a plurality of participants in a communication network. The method includes extracting, by a highlighting device, semantic information and a plurality of vocal cues from multimedia communication data exchanged between the plurality of participants; identifying, by the highlighting device, communication segments within the multimedia communication data by aggregating the semantic information and the plurality of vocal cues; associating, by the highlighting device, meta-data with each of the communication segments based on communication segment parameters; and highlighting, by the highlighting device, contextually, at least a part of the communication segments based on highlighting parameters received from a user.

A contact center server receives an utterance from a customer device as part of a conversation. The contact center server identifies utterance parameters of the utterance, generates a first response to the utterance based on the utterance parameters, and outputs the utterance parameters and the first response to an agent device. The contact center server receives agent-identified-information corresponding to the utterance or agent-modified-information corresponding to the utterance parameters from the agent device. Subsequently, the contact center server generates a second response to the utterance based on the agent-identified-information or the agent-modified-information and outputs the second response to the agent device.

A contact center server receives an input from a customer device as part of a conversation. The contact center server (CCS) identifies at least one of: one or more intents, one or more entities, or one or more entity values from the input. The CCS detects one or more escalation conditions based on the input and pauses the conversation based on the detected one or more escalation conditions. The CCS outputs a conversation transcript and the identified one or more intents, the one or more entities, or the one or more entity values to an agent device. The CCS receives agent-identified-information or agent-modified-information from the agent device. Subsequently, the CCS resumes the conversation by providing a response to the input based on the received agent-identified-information or the agent-modified-information.