Meeting-Video Tailoring Engine for User-Relevant Segment Delivery
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Solution Overview
Problem
Conventional meeting-video management systems lack the computing infrastructure or logic to deliver uniquely tailored meeting-video segments, leading to inefficient user operations and increased resource burden due to the need to review full recordings for relevant content.
Innovation Solution
A meeting-video management engine utilizing clip-generator and meeting-video tailoring machine learning models to generate tailored meeting-video segments based on video, meeting, and user data, enabling efficient delivery of relevant content to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple video conferencing sessions are conducted simultaneously using traditional systems, then meeting capacity is limited, but system complexity and resource consumption increase significantly
Solution Approach 1:
The system segments video processing by creating separate processing pipelines for each video conferencing session. Each session is handled by dedicated processing resources that can be independently managed and scaled, allowing multiple sessions to run simultaneously without interfering with each other.
Solution Approach 2:
The video processing system is designed with universal components that can serve multiple sessions. The same processing engine and resource pool can be dynamically allocated to different sessions based on demand, enabling the system to handle varying meeting capacities without requiring separate dedicated infrastructure for each session.
2Adaptability or versatility
If video processing resources are allocated dynamically to support multiple sessions, then meeting flexibility improves, but resource management complexity increases
Solution Approach 1:
The system implements dynamic resource allocation where processing resources are not statically assigned but are instead allocated in real-time based on session requirements. The resource manager can dynamically adjust the number of processing threads, memory allocation, and computational power distributed to each session according to current meeting demands.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor resource utilization across all sessions. Based on this feedback, the resource manager automatically adjusts allocation to maintain optimal performance, balancing load across available resources and preventing any single session from monopolizing system capacity.
3Ease of manufacture
If traditional video conferencing systems are used, then implementation is straightforward, but integration with existing telephony and messaging systems is limited
Solution Approach 1:
The system introduces intermediary components that act as bridges between the video conferencing engine and existing telephony/messaging infrastructure. These intermediaries handle protocol translation and interface standardization, allowing seamless integration with legacy systems without requiring complex custom development for each integration scenario.
Solution Approach 2:
The video processing system is designed with universal integration capabilities that can connect to multiple different communication platforms. Standardized APIs and protocol support enable the same core system to integrate with various telephony and messaging systems, providing versatile connectivity without sacrificing implementation simplicity.
Data Source
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AI summary
Methods, systems, and computer storage media for providing a tailored meeting-video segment associated with a meeting-video management engine of a meeting-video management system. The tailored meeting-video segment corresponds to a portion of meeting-video content that is programmatically generated based on features associated with video data, meeting data, and user data. A tailored meeting-video segment – or a plurality of tailored meeting-video segments – can be generated by employing a meeting-video tailoring machine learning model of the meeting-video management engine. In particular, the features – associated with video data comprising the plurality of clips, meeting data of the meeting, and user data of the user – are meeting-video tailoring features used by the meeting-video tailoring machine learning model to generate the tailored meeting-video segment. The tailored meeting-video segment is communicated to a user to enable uniquely tailored presentation and playback of meeting-video content computed to be relevant to the user via the meeting-video management engine.