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: In embodiments, a reusable and adaptive multi-task orchestration dialogue system orchestrates a set of single-task dialogue systems to provide multi-scenario dialogue processing. In embodiments, for each question propounded by a user, using a deep learning predictive model, a best single-task dialogue system is chosen out of the set. In embodiments, multi-task orchestration is done without the need to change, or even understand, the inner workings or mechanisms of the individual single-task dialogue systems in the set. Moreover, the multi-task orchestration is also unconcerned with what rules are set in each individual single-task dialogue system. In embodiments, prior to selection of the best single-task dialogue system to return the best answer, new intents and entities are discovered and used to update an existing dialogue path. In embodiments, additional data is continually collected, and used to retrain model so as to further improve performance.

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: In embodiments, a reusable and adaptive multi-task orchestration dialogue system orchestrates a set of single-task dialogue systems to provide multi-scenario dialogue processing. In embodiments, for each question propounded by a user, using a deep learning predictive model, a best single-task dialogue system is chosen out of the set. In embodiments, multi-task orchestration is done without the need to change, or even understand, the inner workings or mechanisms of the individual single-task dialogue systems in the set. Moreover, the multi-task orchestration is also unconcerned with what rules are set in each individual single-task dialogue system. In embodiments, prior to selection of the best single-task dialogue system to return the best answer, new intents and entities are discovered and used to update an existing dialogue path. In embodiments, additional data is continually collected, and used to retrain model so as to further improve performance.

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.

Abstract: A method and system for providing integrated learning materials to a learner within an organization. The learning materials are generated by subject matter experts and compiled in a database on a server. The learning materials are organized corresponding to at least one topic into learning suites and further organized into nodes that provide the learners with different contextual bases and modalities with respect to the compiled materials.