Abstract: A method for agent interaction analysis using artificial intelligence according to an embodiment includes receiving a transcript for a real-time agent interaction between a contact center agent and client, processing the interaction using at least one artificial intelligence model to determine a call adherence score, an agent positivity score, a client sentiment, an agent sentiment, and a plurality of agent skill levels associated with respective agent skills, determining a combined agent performance score based on the call adherence score and the agent positivity score, transmitting the combined agent performance score to an agent device for display on a gamification dashboard, retrieving agent-specific content of the contact center agent in response to determining that the agent sentiment is negative, and transmitting the agent-specific content to the agent device for display in conjunction with the real-time agent interaction to improve the agent sentiment.

Abstract: A method of leveraging machine learning to predict empathy for improved contact center interactions according to an embodiment includes receiving, by a computing system, at least one user message from a real-time contact center interaction with a user, generating, by an artificial intelligence system of the computing system, at least one empathy score based on the at least one message using the machine learning, wherein each of the at least one empathy score is indicative of a real-time empathy of the user, generating, by the artificial intelligence system of the computing system, an empathetic text response to the at least one user message based on the at least one empathy score, and responding to the at least one user message in the real-time contact center interaction based on the empathetic text response generated by the artificial intelligence system of the computing system.

Abstract: A method for agent interaction analysis using artificial intelligence according to an embodiment includes receiving a transcript for a real-time agent interaction between a contact center agent and client, processing the interaction using at least one artificial intelligence model to determine a call adherence score, an agent positivity score, a client sentiment, an agent sentiment, and a plurality of agent skill levels associated with respective agent skills, determining a combined agent performance score based on the call adherence score and the agent positivity score, transmitting the combined agent performance score to an agent device for display on a gamification dashboard, retrieving agent-specific content of the contact center agent in response to determining that the agent sentiment is negative, and transmitting the agent-specific content to the agent device for display in conjunction with the real-time agent interaction to improve the agent sentiment.

Abstract: A method of asynchronously restoring an incomplete co-browse session according to an embodiment includes initiating an interaction between a user and a chat bot, determining whether data indicative of an incomplete co-browse session is stored in association with a user in response to initiating the interaction between the user and the chat bot, retrieving an intent configuration file associated with the incomplete co-browse session, wherein the intent configuration file defines a plurality of actions to be executed by the chat bot to resolve a user intent of the interaction between the user and the chat bot, and performing, via the chat bot and during a resumed co-browse session with the user, one or more actions of the plurality of actions defined by the intent configuration file that were not previously performed during the incomplete co-browse session.

Abstract: A method for authenticating a contact center user to a contact center agent includes a cloud system, to which the agent is securely authenticated, triggering a sending of an electronic message to a user device. The user device has one or more biometric authentication features, which it requires to access a private key, of a key pair, securely stored on the user device. The electronic message contains a validation link or a trigger for a validation prompt. A passkey system stores an agent identifier, a user identifier, an interaction identifier, and a credential identifier in a database, in response receiving a positive authentication status from a business web server indicating that the business web server has authenticated the customer device by passkey. In response to the passkey system receiving the positive authentication status, the passkey system transmits an authentication status notification indicating the positive authentication status to the cloud system.

Abstract: A method of asynchronously restoring an incomplete co-browse session according to an embodiment includes initiating an interaction between a user and a chat bot, determining whether data indicative of an incomplete co-browse session is stored in association with a user in response to initiating the interaction between the user and the chat bot, retrieving an intent configuration file associated with the incomplete co-browse session, wherein the intent configuration file defines a plurality of actions to be executed by the chat bot to resolve a user intent of the interaction between the user and the chat bot, and performing, via the chat bot and during a resumed co-browse session with the user, one or more actions of the plurality of actions defined by the intent configuration file that were not previously performed during the incomplete co-browse session.

Abstract: A method of self-learning actions for an automated co-browse session according to an embodiment include initiating an interaction between a user and a chat bot, determining a user intent of the user based on the interaction between the user and the chat bot, routing the interaction to a human contact center agent for a co-browse session between the user and the human contact center agent, storing a plurality of actions performed by the human contact center agent during the co-browse session to a data store, and performing machine learning to determine an optimal solution for resolving the user intent based on an analysis of the plurality of actions performed by the human contact center agent during the co-browse session.