Abstract: Embodiments described herein provide for the dynamic adjustment of traffic associated with audiovisual conferences or other types of communication sessions in situations where a network connection of one or more conference participants exhibits issues that may affect audio and/or video quality. The adjustment may include the determination at a particular conference participant (e.g., a particular User Equipment (“UE”)) of degraded network conditions, the generation of condensed conference information at the UE, and the outputting of the condensed conference information via the network. The condensed conference information may be included in control signaling. The condensed conference information may be used to generate reconstructed conference information, which may be distributed to other conference participants.

Abstract: Embodiments described herein provide for the dynamic adjustment of traffic associated with audiovisual conferences or other types of communication sessions in situations where a network connection of one or more conference participants exhibits issues that may affect audio and/or video quality. The adjustment may include the determination at a particular conference participant (e.g., a particular User Equipment (“UE”)) of degraded network conditions, the generation of condensed conference information at the UE, and the outputting of the condensed conference information via the network. The condensed conference information may be included in control signaling. The condensed conference information may be used to generate reconstructed conference information, which may be distributed to other conference participants.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, streaming and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel conference framework that automatically determines and prioritizes speaker allocation within a conference in order to remedy and/or avoid conflicts between speakers so that a clear and decisive dialog can occur. The disclosed conference framework balances a determined weighting factor and queue management ranking of the conference to provide a prioritized speaker queue that enables all participants to speak without conflict or interruption. The disclosed systems and methods enable a prioritized, conflict-resolution based conferencing environment where orderly conferencing can occur while maintaining each participants rights to participate accordingly.

Abstract: Embodiments described herein provide for the dynamic adjustment of traffic associated with audiovisual conferences or other types of communication sessions in situations where a network connection of one or more conference participants exhibits issues that may affect audio and/or video quality. The adjustment may include the determination at a particular conference participant (e.g., a particular User Equipment (“UE”)) of degraded network conditions, the generation of condensed conference information at the UE, and the outputting of the condensed conference information via the network. The condensed conference information may be included in control signaling. The condensed conference information may be used to generate reconstructed conference information, which may be distributed to other conference participants.

Abstract: Systems and methods described herein perform computer vision (CV) model training in a distributed edge network. Regional Multi-access Edge Compute (MEC) clusters are provided with a local copy of a CV model and a local synthetic training data generator. A MEC cluster receives client data requiring computer vision and applies the local copy of the CV model to the client data. The MEC cluster detects an exception to the local copy of the CV model and generates local synthetic training data for the exception. The MEC cluster updates, based on the local synthetic training data, the local copy of the CV model to form an updated local CV model. The MEC cluster sends the local synthetic training data and the updated local CV model to a central network. The central network uses the local synthetic training data to update a master CV model and any other interconnected CV models.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, streaming and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel conference framework that automatically determines and prioritizes speaker allocation within a conference in order to remedy and/or avoid conflicts between speakers so that a clear and decisive dialog can occur. The disclosed conference framework balances a determined weighting factor and queue management ranking of the conference to provide a prioritized speaker queue that enables all participants to speak without conflict or interruption. The disclosed systems and methods enable a prioritized, conflict-resolution based conferencing environment where orderly conferencing can occur while maintaining each participants rights to participate accordingly.

Abstract: Systems and methods described herein perform computer vision (CV) model training in a distributed edge network. Regional Multi-access Edge Compute (MEC) clusters are provided with a local copy of a CV model and a local synthetic training data generator. A MEC cluster receives client data requiring computer vision and applies the local copy of the CV model to the client data. The MEC cluster detects an exception to the local copy of the CV model and generates local synthetic training data for the exception. The MEC cluster updates, based on the local synthetic training data, the local copy of the CV model to form an updated local CV model. The MEC cluster sends the local synthetic training data and the updated local CV model to a central network. The central network uses the local synthetic training data to update a master CV model and any other interconnected CV models.