Abstract: A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.

Abstract: A computer implemented method, computer system and computer program product are provided for transferring in a BOT conversation. According to the method, a user input is received, by a device operatively coupled to one or more processing units, from a user. A first response from a conversation BOT responding to the user input is obtained by the device. The first response is evaluated by the device according to configured rules to determine whether a human agent is needed, wherein the evaluation according to at least one of the configured rules is carried out by a trained engine of a reinforcement learning model. Finally, responding to determine the human agent is needed, a transferring recommendation is sent to the user by the device.

Abstract: A computer implemented method, computer system and computer program product are provided for transferring in a BOT conversation. According to the method, a user input is received, by a device operatively coupled to one or more processing units, from a user. A first response from a conversation BOT responding to the user input is obtained by the device. The first response is evaluated by the device according to configured rules to determine whether a human agent is needed, wherein the evaluation according to at least one of the configured rules is carried out by a trained engine of a reinforcement learning model. Finally, responding to determine the human agent is needed, a transferring recommendation is sent to the user by the device.

Abstract: A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.