Abstract: Example methods and systems for facilitating queries about a virtual communication session are provided. A communication platform receives an initial query about the virtual communication session from a user. The communication platform accesses virtual communication data associated with a virtual communication session. The communication platform generates an initial response to the initial query based on the virtual communication data using a first pre-trained generative artificial intelligence (AI) model. The communication platform generates a first set of follow-up queries based on the initial response using a second pre-trained generative AI model. The communication platform receives a selection of a first follow-up query out of the first set of follow-up queries. The communication platform provides a first response to the first follow-up query using the first pre-trained generative AI model.

Abstract: Example methods and systems for automatic generation of interaction tools. A communication platform receives a request to generate an interaction tool associated with a virtual communication session and accesses virtual communication data associated with the virtual communication session. The communication platform identifies a set of keypoint data from the virtual communication data based on the request using a machine learning model. The communication platform generates a list of questions based on the set of keypoint data and the request using a first generative artificial intelligence (AI) model. The communication platform provides the interaction tool based on the list of questions.

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: One method for multimodal meeting segmentation includes receiving a meeting recording and a transcript, the meeting recording comprising a first media stream; generating, using a trained machine learning (“ML”) model, a first set of embeddings corresponding to the first media stream and a second set of embeddings corresponding to the transcript; generating a first set of segments based on the first set of embeddings and a second set of segments based on the second set of embeddings; generating a final set of segments based on the first and second sets of segments; and associating the final set of segments with the meeting recording and storing the final set of segments.

Abstract: Example methods and systems for automatic suggestion and generation of meeting schedules are provided. A communication platform accesses virtual interaction data associated with a virtual interaction. The communication platform detects a meeting intent based on the virtual interaction data. The communication platform also predicts multiple meeting attendees, determines one or more meeting times, and generates a meeting title based on the virtual interaction data and other related data. The communication platform provides a meeting schedule suggestion to a participant in the virtual interaction. The meeting schedule suggestion includes the meeting title, identifications of the multiple attendees, and the one or more meeting times.

Abstract: One example method includes receiving, by a virtual assistant, a task from a remote client device; transmitting a first prompt to a large language model (“LLM”), the first prompt comprising the task and a request to process the task; receiving, from the LLM in response to the first prompt, a plurality of sub-tasks and an ordering of the plurality of sub-tasks; obtaining descriptions of a plurality of available services; for each sub-task: transmitting a second prompt to the LLM, the second prompt comprising a description of the respective sub-task and the descriptions of the available services; receiving, from the LLM in response to the respective second prompt, an identification of one or more available services; and initiating, for the respective sub-task, the one or more identified available services; and after completion of the plurality of sub-tasks, generating and providing a response to the task to the remote client device.

Abstract: Systems and methods for video summary generation are disclosed. A chat and video conference provider generates a text summary for a meeting video and an audio summary based on the text summary. The chat and video conference provider determines a first set of correspondences between a first set of video portions from the meeting video and portions of the text summary based on a transcript of the meeting video. The chat and video conference provider determines a second set of correspondences between a second set of video portions from the meeting video and the portions of the text summary based on image data of the meeting video. The chat and video conference provider selects a plurality of video frames from the first set of the correspondences and the second set of correspondences to generate a video summary of the meeting video based on the audio summary.

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, by a virtual conference provider, audio streams from a plurality of participants during a virtual conference; performing speech recognition, by the virtual conference provider, on the received audio streams to generate a transcript of the virtual conference; determining one or more utterances made during the virtual conference; determining, using a first trained machine learning (“ML”) model, one or more sub-topics discussed during the virtual conference based on the one or more utterances; determining, using a second trained machine learning (“ML”) model, one or more topics discussed during the virtual conference based on the one or more sub-topics; determining metadata corresponding to each of the one or more topics, the one or more sub-topics, and the one or more utterances; and generating and providing suggested follow-up topics for a subsequent virtual conference based on the metadata.

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 receives a request to determine a number of agents working at a future time. The contact center server generates, using a combination engine, a combination of one or more modeling engines from multiple modeling engines. The contact center server determines, using the combination of the one or more modeling engines, the number of agents. The contact center server provides an output representing the number of agents.

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: 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 providing rule-based and AI-based hybrid analytics to facilitate actions for a project are provided. A communication analytics platform accesses project metadata and communication data associated with a project. The communication analytics platform determines a first risk score of the project based on the project metadata and the communication data using a rule-based analytics model. The communication analytics platform determines a second risk score of the project based on the project metadata and the communication data using an artificial intelligence (AI)-based analytics model. The communication analytics platform determines a risk level of the project based on the first risk score and the second risk score. The communication analytics platform provides a notification message based on the risk level to a user associated with the project.

Abstract: Systems and methods for recommending communication channels and generating content for next-step communication are disclosed. A communication analytics platform accesses project metadata and communication data associated with a project. The communication analytics platform determines a recommendation of one or more communication channels for next-step communication for the project based on the project metadata and the communication data. The communication analytics platform generates content for the next-step communication using a generative artificial intelligence (AI) model based on the project metadata and the communication data. The communication analytics platform provides the recommendation of one or more communication channels and the generated content to a user associated with the project.

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: Methods and systems provide for virtual meeting coaching with dynamically extracted content. In one embodiment, the system receives transcripts for a plurality of selected meetings from within a communication platform, the meetings being selected for question extraction; extracts, via a question extraction model, questions from the transcripts for the selected meetings; extracts, from the questions, a number of expected answers, each expected answer associated with one of the questions; connects to a coaching session with one or more participants and a virtual coaching agent; transmits, by the virtual coaching agent, at least a subset of the extracted questions to the participants; receives answers to the subset of the extracted questions by participants; and generates one or more evaluation scores for the participants corresponding to each of the answers, the evaluation scores being based at least in part on the extracted expected answers associated with the subset of the extracted questions.

Abstract: Systems, methods, and computer-readable media are provided for customizing customer onboarding for a service.

Abstract: Systems, methods, and computer-readable media are provided for customizing an authorization request schedule. In some embodiments, application of one or more statistical or rule-based models may be combined with the use of one or more learning-based models to provide a hybrid model approach to customizing an authorization request schedule that may allow for the personalization and flexibility of a learning model as well as the fine tunability of a statistical model.

Abstract: Systems, methods, and computer-readable media are provided for customizing payment sessions.