Abstract: Systems and methods for generating online meeting summaries for videoconferencing are provided. For example, a computing device establishes a video conference for a plurality of participants. While the video conference is in progress, the computing device receives a first portion of a transcript of the video conference and generates a first meeting summary based on the first portion of the transcript. The computing device causes the first meeting summary to be presented in a user interface accessible by a client computing device associated with at least one of the plurality of participants. The computing device receives a second portion of the transcript of the video conference and generates a second meeting summary based on the first portion and the second portion of the transcript. The computing device causes the second meeting summary to be presented in the user interface.

Abstract: Example methods and systems recommend and summarize unread messages for a user on an online chat platform. A client device accesses multiple unread messages associated with the user, and a machine learning (ML) model provided by the online chat platform. The client device selects one or more unread messages from the multiple unread messages using the ML model trained with historical chat data and user metadata. The client device also generates a summary of some or all of the unread messages using a second ML model trained with the historical chat data and the user metadata. The indication of the selected unread messages and the summary of unread messages are displayed on the client device.

Abstract: Some examples involve an artificial intelligence (AI) system for handling a query about a conversation, such as a conversation between attendees of a videoconferencing meeting. As one example, the system can receive a query from a user about a conversation between attendees of a videoconferencing meeting. The system can determine a relevant portion of the conversation based on the query, determine an intent of the query by providing the query as input to an intent detection model, and select a machine-learning model from among a group of machine-learning models based on the intent of the query. The system can then provide the relevant portion of the conversation as input to the selected machine-learning model. The machine-learning model can generate an output based on the relevant portion of the conversation. The system can transmit the output to the user in a response to the query.

Abstract: One example method includes receiving a set of communication records, the set of communication records representing one or more communications between a first person and a second person; receiving a user query, the user query comprising one or more constraints; generating a plurality of segments from the communication records, at least a subset of the plurality of segments based on the one or more constraints; selecting one or more segments based on the one or more constraints; generating one or more queries based on the one or more constraints; providing, to a trained large language model, the one or more selected segments and the one or more generated queries; and receiving, from the trained LLM, and outputting a multidimensional analysis of the set of communication records.

Abstract: Techniques for video conference transcript querying using artificial intelligence are provided. In an example method, a video conference provider joins a client device to a video conference. The video conference provider receives a deletion election. The video conference provider receives an audio stream from the client device and generates a portion of a transcript of the video conference. Prior to the video conference concluding, the video conference provider processes the portion of the transcript to configure a large language model (“LLM”) to respond to queries based on the video conference. The video conference provider receives a query relating to the video conference and causes the LLM to process the query and the portion of the transcript. The video conference provider outputs a response, generated by the LLM, to the query. The video conference provider then deletes the portion of the transcript based on the deletion election.

Abstract: Example methods and systems for automatic task generation are provided. A communication platform receives an instruction to generate one or more task items associated with a virtual communication session related to a user. The communication platform accesses virtual communication data associated with the virtual communication session. The communication platform generates a task item based on the virtual communication data using a generative artificial intelligence (AI) model. The communication platform provides the task item to the user.

Abstract: Techniques for video conference transcript querying using artificial intelligence are provided. In an example method, a video conference provider joins a first client device of a plurality of client devices to a video conference hosted by a video conference provider. The video conference provider receives an audio stream from the first client device and generates, based on the audio stream, a portion of a transcript of the video conference. The video conference provider processes the portion of the transcript to configure an AI service to respond to queries based on the video conference. The video conference provider receives a query relating to the video conference and causes the AI service to process the query and the portion of the transcript. The video conference provider outputs a response, generated by the AI service, to the query. The video conference provider then deletes the transcript, responsive to the video conference concluding.

Abstract: Example methods and systems for automatic task generation is provided. A communication platform receives an instruction to generate one or more task items associated with a virtual communication session related to a user. The communication platform accesses virtual communication data associated with the virtual communication session. The communication platform generates a task item based on the virtual communication data using a generative artificial intelligence (AI) model. The communication platform provides the task item to the user.

Abstract: In one embodiment, a system receives a set of coaching items including a number of questions each associated with an expected answer; connects to a coaching session including one or more participants and a virtual coaching agent; for each question and for at least a subset of the participants: transmitting the question, by the virtual coaching agent, to the client device used by the participant; receiving an answer to the question by the participant, the answer including media of the participant; receiving text of utterances spoken by the participant during the answer; generating one or more evaluation scores for the answer based on evaluating at least the content of the answer to the question; and transmitting an overall evaluation score for each of the subset of participants based on the generated evaluation scores for that participant.

Abstract: Example methods and systems for prompt enhancement are provided. A communication platform accesses an initial meta prompt. The initial meta prompt is a prompt for a generative model to enhance a task prompt. The communication platform generates a first set of variant meta prompts using a first generative model based on the initial meta prompt. The communication platform generates a first set of enhanced baseline task prompts corresponding to a set of baseline task prompts using a second generative model based on the first set of variant meta prompts. The communication platform evaluates the first set of variant meta prompts to obtain a first set of evaluation data. The communication platform selects a first variant meta prompt as a first optimized meta prompt based on the first set of evaluation data. The communication platform provides the first optimized meta prompt to a third generative model for task prompt enhancement.

Abstract: One example method includes receiving a request to generate an analysis of communication records, the communication records associated with a plurality of types of communication records; accessing a plurality of communication records associated with the request, each communication record of the plurality of communication records corresponding to one type of the plurality of types of communication records; for the communication records of a respective type of communication records, generating, using a trained large language model (“LLM”), one or more analyses of the respective communication records; for each type of communication record, generating, using the trained LLM, a homogeneous analysis of the one or more analyses of the respective communication records corresponding to the respective type of communication records; generating, using the trained LLM, a heterogeneous analysis of the homogeneous analyses of the types of communication records; and providing the heterogeneous analysis in response to the req

Abstract: One example method includes receiving a set of communication records, the set of communication records representing one or more communications between a first person and a second person; receiving a user query, the user query comprising one or more constraints; generating a plurality of segments from the communication records, at least a subset of the plurality of segments based on the one or more constraints; selecting one or more segments based on the one or more constraints; generating one or more queries based on the one or more constraints; providing, to a trained large language model, the one or more selected segments and the one or more generated queries; and receiving, from the trained LLM, and outputting a multidimensional analysis of the set of communication records.

Abstract: One example method includes receiving, by an artificial intelligence (“AI”) assistant, a user query comprising one or more tasks; determining one or more services based on the user query; obtaining, for each of the one or more services, a plurality of examples, each example providing an example command suitable for execution by the respective service; generating one or more instructions based on the user query, the one or more services, and the one or more pluralities of examples; providing the one or more instructions to a trained large language model (“LLM”); receiving, from the LLM, one or more commands corresponding to the user query; for each command of the plurality of commands, issuing the respective command to a corresponding service of the one or more services; generating a response to the user query based on results of the plurality of commands; and outputting the response

Abstract: One example method for communication analysis using LLMs includes receiving a set of communication records, the set of communication records representing one or more communications between a first person and a second person; receiving a set of analytical parameters associated with the set of communication records; generating a plurality of segments from the communication records; for each analytical parameter in the set of analytical parameters: determining a subset of segments semantically associated with the respective analytical parameter; generating, using a trained large language model (“LLM”), an evaluation of each segment of the respective subset of segments with respect to the respective analytical parameter; and generating, using the trained LLM, a response to the analytical parameter based on the evaluations of the segments; and outputting a full evaluation of the set of communication records based on the set of analytical parameters and the respective generated responses to the analytical parameter

Abstract: Example methods and systems facilitate user actions during a chat session on an online chat platform. A client device is installed with a chat and video conference application. The chat and video conference application includes a machine learning (ML) model. The client device receives a chat message during a chat session and identifies an action item from the chat message using the ML model. One or more GUI elements can be generated associated with a functionality of an application corresponding to the action item. A GUI element can be activated to invoke the functionality of the application.

Abstract: A contact center server obtains historical contact center data of a contact center by tracking contact center conditions. The contact center server trains, based on the historical contact center data, multiple modeling engines to generate agent demand data representing a number of agents working at a given time. The contact center server trains, based on the historical center contact center data and performance data of the multiple modeling engines, a combination engine to generate a combination of one or more modeling engines from the multiple modeling engines. The contact center server provides an output representing the trained combination engine and the multiple modeling engines.

Abstract: Systems and methods for generating online meeting summaries for videoconferencing are provided. For example, a computing device establishes a video conference for a plurality of participants. While the video conference is in progress, the computing device receives a first portion of a transcript of the video conference and generates a first meeting summary based on the first portion of the transcript. The computing device causes the first meeting summary to be presented in a user interface accessible by a client computing device associated with at least one of the plurality of participants. The computing device receives a second portion of the transcript of the video conference and generates a second meeting summary based on the first portion and the second portion of the transcript. The computing device causes the second meeting summary to be presented in the user interface.

Abstract: Methods and systems provide for virtual meeting coaching with content-based evaluation. In one embodiment, the system receives a set of coaching items including a number of questions each associated with an expected answer; connects to a coaching session including one or more participants and a virtual coaching agent; for each question and for at least a subset of the participants: transmitting the question, by the virtual coaching agent, to the client device used by the participant; receiving an answer to the question by the participant, the answer including media of the participant; receiving text of utterances spoken by the participant during the answer; generating one or more evaluation scores for the answer based on evaluating at least the content of the answer to the question; and transmitting an overall evaluation score for each of the subset of participants based on the generated evaluation scores for that participant.

Abstract: Example methods and systems facilitate user actions during a chat session on an online chat platform. A client device is installed with a chat and video conference application. The chat and video conference application includes a machine learning (ML) model. The client device receives a chat message during a chat session and identifies an action item from the chat message using the ML model. One or more GUI elements can be generated associated with a functionality of an application corresponding to the action item. A GUI element can be activated to invoke the functionality of the application.

Abstract: One example method includes receiving, during a virtual conference from a client device, a request to generate a real-time summary of the virtual conference, a plurality of client devices participating in the virtual conference and exchanging audio and video streams; receiving, during the virtual conference, a plurality of utterances generated by automatic speech recognition (“ASR”) of the audio streams; generating a group of consecutive utterances from the plurality of utterances; determining a segment based on the group of consecutive utterances; generating, using a large language model (“LLM”), a segment summary based on the segment; and providing the segment summary to the client device.