A system and method of use to train customer service agents. The training system employs intelligent systems to facilitate or enable the training of customer service agents. The training system provides training to customer service agents and tracks the progress of the customer service trainees. In one aspect, the training system emulates a customer engaging with the customer service trainee, by emulating one or both of the persona of the customer and the scenario of the customer/trainee interaction.

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.

A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.

Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

A system and method are disclosed for recommending a resource to a customer service representative that includes one or more databases that store data describing electronic communication between one or more customer system communication devices and one or more service center communication devices. Embodiments further include a computer coupled with one or more databases and configured to monitor communication activity to determine whether a customer service ticket has been opened between one or more customer system communication devices and one or more service center communication devices and determine a customer service representative score based on one or more customer service representative ranking factors.

A system and method for automated call analysis using context specific lexicons. A system includes memory and a processor configured to executed instructions. The system includes a recording component, a lexicon component, an analysis component, and a display component. The lexicon component defines a plurality of context specific lexicons, with each context specific lexicon having elements associated with one of a plurality of unique conversation segments. The analysis component configured to identify elements of the context specific lexicons, and associate each identified element with a time location in a telephonic conversation. The display component configured to graphically present a multi-line graph such that the intersections of the lines indicate transitions between the unique conversation segments.

Merchant/consumer calls may be recorded and evaluated according to a variety of criteria. The call recordings and analyses thereof, as well as consumer tracking information, may be displayed in a user interface of a web-based online portal for convenience in evaluating the use and efficacy of marketing channels as well as the quality of merchant/consumer interactions. In an aspect, the user interface provides call visualization in the form of audio data from a telephone call displayed as a waveform on a call timeline. The call may be (automatically or manually) annotated with various business-value-specific keywords spoken during the telephone call, and markers for these keywords can be presented on the call timeline to visually indicate the keyword and the time during the call when the keyword was spoken. A business value for the call may be determined based at least in part on keywords spoken during the call.

A conversation system may dynamically control a conversation or workflow by performing multi-modal conversation monitoring, generating actions that control the conversation based on the multi-modal monitoring producing conversation elements that deviate from patterns of a selected plan for that conversation, and/or by dynamically generating and/or updating the plan for future conversations based on the pattern recognition. For instance, the conversation system may detect a pattern within completed conversations that resulted in a common outcome, may monitor an active conversation between at least an agent and a participant, may extract different sets of conversation elements from different points in the active conversation, may determine that a particular set of conversation elements deviates from the pattern, and may modify the active conversation by performing one or more actions based on the particular set of conversation elements that deviate from the pattern.

A system, computer program product, and method are disclosed. In an approach to train customer service agent using chatbots. The method includes training a chatbot for a customer chat simulation based on a customer service conversation data, a task scenario, and a customer persona. The method also includes monitoring an interaction between a customer service agent and the chatbot. The method further includes determining an assessment of the performance of the customer service agent based on the interaction between the customer service agent and the chatbot. The method additionally includes generating feedback for the customer service agent based on the assessment of the performance of the customer service agent.

A method and an apparatus for testing a dialogue platform, and a storage medium are proposed. The specific solution is that: creating at least one simulation test instance, the simulation test instance comprises a plurality of test task information, each test task information comprises test numbers, ringing simulation data, and call simulation data; sending the test numbers to the dialogue platform to start a test; sending the ringing simulation data to the dialogue platform, to receive task states fed back by the dialogue platform; sending the call simulation data to the dialogue platform, to receive dialogue data fed back by the dialogue platform; and performing a dialogue test on the dialogue platform based on the test tasks, the task states corresponding to the test tasks, and the dialogue data.