Described herein is a system and method for capturing data associated with actions attempted by an automated agent. The system described herein captures data associated with the actions attempted by an automated agent during the messaging session between an automated agent and the user and present a summary of the actions in a messaging platform. In an embodiment, the automated agent uploads data associated with actions attempted during the messaging session to a server. The server captures the data associated with the actions and generates a description of each action that was attempted. The server generates a summary including the description of each action. The summary of the actions are rendered in the messaging platform.

A computer based system and method for automatically detecting frustration in an interaction, may include: identifying in the interaction using a set of linguistic rules, natural language patterns related to frustration, wherein the linguistic rules further define weights associated with the natural language patterns and rule metadata; reviewing the rule metadata associated with the identified natural language patterns to identify override attributes, wherein if the rule metadata does not include override attributes, then a frustration level in the interaction is determined based on the identified natural language patterns and weights associated with the identified natural language patterns; and if the rule metadata includes override attributes than the frustration level is determined based on the identified override attributes.

A computer based system and method for automatically detecting frustration in an interaction, may include: identifying in the interaction using a set of linguistic rules, natural language patterns related to frustration, wherein the linguistic rules further define weights associated with the natural language patterns and rule metadata; reviewing the rule metadata associated with the identified natural language patterns to identify override attributes, wherein if the rule metadata does not include override attributes, then a frustration level in the interaction is determined based on the identified natural language patterns and weights associated with the identified natural language patterns; and if the rule metadata includes override attributes than the frustration level is determined based on the identified override attributes.

Examples for conversation-based report generation are described herein. In some examples, a report context based on user input to a conversation manager is received. A report is generated based on the report context. The report context is saved for subsequent report generation. The report context may include information related to the intent of the user input.

A contextual lead generation in an automated communication link establishment and management system may store information related to sales calls. The system may identify strengths and weaknesses of a sales representative. The system may provide training content to the sales representative in real time base on the identified strengths and weaknesses.

A computer-implemented method for providing performance information related to a customer service representative. A processor aggregates statistical data related to customer service representatives performance. A menu is then caused to be generated and displayed indicating a plurality of menu items. Each of the plurality of menu items corresponds to one of a plurality of the work-related types of data. Additionally, displayed and processed are one or more work-related alerts in response to receiving the one or more work-related alerts.

A computer-implemented method and supporting system transcribes spoken words being monitored from a telephonic interaction among two or more individuals. Telephonic interactions among the individuals are monitored, and at least two of the individuals are each assigned to a separate channel. While still being monitored, each of the channels is assigned a context-based speech recognition models, and in substantially real-time, the monitored telephonic interaction is transcribed from speech to text based on the different assigned models.

While a teleconference is occurring, data of a teleconference is analyzed to determine first participant data associated with a first speaker and second participant data associated with a second speaker. At a different application, addition of a first speaker indicator and a second speaker indicator to a text entry of a user is caused, the first speaker indicator added concurrently with identification that the first speaker is speaking and the second speaker indicator added concurrently with identification that the second speaker is speaking. At the different application, addition of key information to a text entry of a user is caused, the key information comprising participant data associated with the first speaker and second participant data associated with the second speaker.

A data processing system for artificial intelligence-based setting of controls in an evaluation interface comprising a data store storing: a plurality of transactions; a plurality of completed evaluations, each completed evaluation including an indication of a transcript portion associated with an evaluation answer. The system determines a word or phrase common to a first set of transcript portions associated with the evaluation answer; creates a first set of auto answer parameters that includes the word or phrase; auto answers the question for a set of test transactions to generate an auto answer for each test transaction; and based on a determination that the first set of auto answer parameters auto answered the question with a threshold level of accuracy, configures an evaluation system to use the first set of auto answer parameters to preset an answer control in an evaluation operator interface.

A method for personalizing a delivery of services to a first customer including: providing a customer profile; updating the customer profile via performing a first process to collect interaction data, the first process including the steps of: monitoring activity on the communication device and, therefrom, detecting the first interaction with the first contact center; identifying data relating to the first interaction for collecting as the interaction data; and updating the customer profile to include the interaction data identified from the first interaction; generating an interaction predictor, the interaction predictor comprising knowledge about the first customer derived, at least in part, from the data stored within the customer profile, the knowledge comprising a behavioral factor attributable to the first customer given a first type of interaction; and augmenting the customer profile by storing therein the interaction predictor.