Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on facial gestures and limb gestures derived from captured images. An example method includes receiving a series of images captured in real-time. The example method further includes causing generation of a face segmentation and one or more limb segmentations using the series of images. The example method further extracting one or more face segmentations vectors and one or more limb segmentation vectors. The example method further includes causing generation of weighted vectors using the one or more face segmentation vectors and the one or more limb segmentation vectors for each of the one or more the limb segmentations. The example method includes calculating a probability distribution based on the one or more probabilities corresponding to one or more emotions. The example method finally includes determining a predicted emotion based on the probability distribution.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on a multimodal input including at least (i) an amount of keystrokes over a period of time and (ii) text. An example method may include receiving, by a communications circuitry, a multimodal input from a user including at least (i) an amount of keystrokes over a period of time and (ii) text. The example method may further include generating, by a trained machine learning model of an emotion prediction circuitry and using the multimodal input, an EmotionPrint for the user. The example method may finally include determining, by the emotion prediction circuitry and using the EmotionPrint, a next action.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an entity and intent based on captured speech. An example method includes capturing speech and converting the speech to text. The example method further includes causing generation of one or more entities and one or more intents based on the speech and the text. The example method further includes determining a next action based on each of the one or more entities and each of the one or more intents.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion based on speech and text derived from the speech. An example method includes capturing speech and generating hidden vectors based on the speech and text derived from the speech. The example method further includes generating a word aligned hidden vector based on the speech hidden vector and text hidden vector. The example method further includes generating a hidden vector based on the word aligned hidden vector and the text hidden vector. The example method further includes, after further processing of the final vector, generating one or more probabilities corresponding to one or more emotions. The example method finally includes determining a next action based on the generated one or more probabilities corresponding to one or more emotions.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for predicting an entity, intent, and emotion based on weighted and segmented portions of captured speech. An example method includes capturing speech and separating the speech and/or text derived from the speech into a plurality of discrete segments. The example method further includes determining or generating metadata, the metadata an entity, an intent, an emotion, and a weight associated with each of the entity, the intent, the emotion. The example method further includes determining a next action based on the determined or generated entity, intent, and emotion of each of the plurality of discrete segments.

Abstract: Method and system for facilitating collaboration among enterprise agents are disclosed. A response provided by a first agent to a first customer is tagged by the first agent. The response is tagged during an interaction between the first agent and the first customer with an intent relevant to the interaction. The tagged response is used as an agent response of a second agent during an ongoing interaction between a second agent and a second customer. The use of the response as an agent response of the second agent is facilitated if at least one intent relevant to the ongoing interaction matches the intent tagged to the response by the first agent.