Abstract: In an example method, a system accesses first data including text transcripts of a plurality of voice calls, and generates, based on the first data, labeled representations of the voice calls using the one or more computerized LLMs. The system generates the labeled representations by determining a plurality of contextual categories associated with the voice calls, segmenting the text transcript into a plurality of transcript segments, and associating each of the transcript segments with a respective one of the contextual categories. Further, the system generates second data representing the labeled representations of the voice calls and stores the second data using the one or more hardware storage devices.

Abstract: A computer-implemented system and computer instructions stored on non-transitory computer readable medium for bilingual medical inquiry in both Arabic and English, including multiple-choice question answering, open-ended question answering, and multi-turn question answering. The system and instructions use a mixture of experts large language model (MOE LLM) having a router network connected to multiple expert networks. The MOE LLM is trained with medical domain data and is used to receive the input bilingual text in a format for a medical inquiry, and output text in a format of a response to the medical inquiry, in sequence. The system and instructions incorporate an English-to-Arabic translation pipeline having a language translation model to generate Arabic language medical instruction sets from English language medical instructions, for large scale use in Arabic and English medical inquiry.

Abstract: A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use a visual attention based mechanism to generate a visual context representation from an image and caption. The system can use the visual context representation to identify one or more terms of the caption as a named entity.

Abstract: Aspects of the disclosure are directed to a transactional agent for user interactions. The agent can seamlessly respond to user requests in a conversational manner while maintaining the conversational state. The agent can include a multi-stage modular model architecture, including a semantic understander and a semantic matcher. The semantic understander can be configured to understand common conversation conventions and/or patterns to produce a structure representation of a user request. The semantic matcher can be configured to map items and modifiers to product entries for a particular domain.

Abstract: An illustrative intent classification engine may access a text transcript and determine one or more features associated with the text transcript. Based on the one or more features, the intent classification engine may generate an aggregate embedding vector and provide the aggregate embedding vector as an input to a trained model configured to output an intent classification. Corresponding methods and systems are also disclosed.

Abstract: Disclosed herein are devices, systems, and computer-implemented methods for intelligent conversational intent detection. Example methods include acquiring a conversational transcript input that is requested for intent detection, inputting the conversational transcript input into a model configured to decipher a conversational intent segment, and returning to a user the conversational intent segment. The conversational transcript input can include one or more conversational transcript segments. The conversational intent segment can correspond to which of the one or more conversational transcript segments is likely to indicate a conversational intent of the conversational transcript input based on the intent detection.

Abstract: An example operation may include one or more of executing an interaction with an account via a chatbot within a chat element running on an account device, determining a next response for the chatbot to output within the chat element based on an execution of an artificial intelligence (AI) model on content within the chat element, determining an interaction attribute of the next response based on an execution of a second AI model on the content from the chat element and on historical chat content of the account device and the chatbot, and outputting the next response via the chatbot within the chat element based on the determined interaction attribute. The example operation may further include an AI agent that performs an action based on the next response.

Abstract: A text analysis processing for detecting computer-generated text is provided. In some cases, a text-based chat interaction may be initiated and analyzed to determine whether the text-based chat generated by a communicating entity is computer-generated. The text of the chat session may be analyzed to evaluate punctuation, use of emojis, spacing, grammar, words, phrases, and the like to determine a further likelihood of whether the text is computer-generated. A duration of the chat session may be used as a scoring factor. The various probabilities and scores may be combined to provide a composite score.

Abstract: Verbal language analysis is provided to users. The user enrolls or subscribes for verbal language analysis or analytics. The user carries out or conducts a conversation with a third party. An intelligence device associated with the user records the conversation. The intelligence device performs verbal language analysis on the conversation. The verbal language analysis generates individual metrics for verbal factors of energy, word count, inflection, tone (e.g. pitch and sentiment), rate, and/or the like. A verbal intelligence index is determined from the individual metrics using aggregation, averaging, weighted averaging, and/or the like. An interface component generates views to display to the user for review of the conversation to facilitate better verbal performance during current and in future conversations.

Abstract: Systems and methods for recording and transcribing conversations in real-time. Sentiment analysis is performed on each utterance to determine both an intent of the conversation along with sentiment. Annotated transcripts are provided. A machine learning model may be used to augment sentiment analysis.

Abstract: A device includes a processor, and a memory storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following functions: receive textual user input from a user describing a design to be generated; implement a first prompt generator to generate a first prompt for a Large Language Model (LLM) to restructure the user input; and implement a second prompt generator to generate a second prompt for a text-to-image model using output of the LLM to produce, the second prompt to prompt the text-to-image model to produce a proposed design based on the user input. The proposed design is provided to the user via an application comprising controls for further editing the proposed design.

Abstract: Devices and techniques are described for embedding-free speaker diarization. In some examples, a first speaker ID label is determined for a first frame and a second speaker ID label may be determined for a second frame of a first window of audio. A third speaker ID label may be determined for a third frame of a second window. First combined data representing at least the first frame and the third frame and second combined data representing at least the second frame and the third frame may be generated. First posterior data associated with the first frame and second posterior data associated with the third frame may be generated. Third posterior data associated with the second frame and fourth posterior data associated with the third frame may be generated. A determination may be made that the first speaker ID label and the third speaker ID label correspond to the same speaker.

Abstract: Methods and apparatuses for optimizing performance of conversational interface applications using example forgetting include a server that retrieves training data comprising utterances each mapped to one or more known intents. The server determines a forgetting count for each utterance and selects utterances from the training data that have a forgetting count above a predetermined threshold. The server identifies whether the predicted intent associated with each utterance is accurate. The server generates updated training data comprising the selected utterances and corresponding predicted intents, and trains conversational interface applications using the updated training data. The server validates performance of the trained conversational interface applications and saves the updated training data.

Abstract: The information processing device according to one embodiment includes: a setting part configured to pair and set, based on first information sent from a terminal that is connected via a communication network, user identification information with second information, the user identification information identifying a user using a given telephone machine, the second information being obtainable from a voice packet of a telephone call between the given telephone machine and another telephone machine, and the first information being predetermined information and the second information being predetermined information; and a specifying part configured to obtain, when a voice packet of a telephone call between the given telephone machine and another telephone machine arrives, the second information from the voice packet, and specify the user identification information paired with the second information obtained.

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 and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

Abstract: Aspects of the disclosure are directed to a token aggregator for aggregating outputs from various generative models. The token aggregator can operate on a token-by-token basis, serving to aggregate several weighted generative model outputs to generate a joint output. By providing weights to the token aggregator as to what the preferred distribution may be, the weights can be used to tradeoff between generative model outputs to help determine the relative weight of the generative model outputs for creating the joint output as well as determining contribution amounts, e.g., bid payments, credits, or points, from respective model outputs.

Abstract: Techniques are described for performing automated operations that include analyzing computer-detected event activity to improve further computer processing, such as to determine tasks performed that cause the events, to use natural language processing (NLP) to generate textual descriptions of the tasks, and to use the generated descriptions to improve further processing related to the tasks.

Abstract: In an example embodiment, a method includes determining an attitudinal negativity score associated with a contact center agent, among a plurality of contact center agents, based on an interaction between the contact center agent and a user during a communication session, receiving data associated with an incoming user communication, determining a user ease score associated with the incoming user communication based on the data, and blocking routing of the incoming user communication to the contact center agent based on the attitudinal negativity score being above a first threshold score and the user ease score being below a second threshold score.

Abstract: A system that enables synchronized interaction between voice input and a visual user interface is described. The system receives, by a network site via a first user interaction channel, a user request to perform an action with a listing network platform. The system establishes, by the network site, a session associated with a session identifier for the user request and provides an option for the user to continue interacting with the listing network platform through a second user interaction channel. The system, in response to receiving input that selects the option, uses the session identifier associated with the session to synchronize a first set of inputs received through the first user interaction channel with a second set of inputs received through the second user interaction channel to complete the action on the listing network platform.