Automated Media Clipping Using Entity-Based Transcript Segmentation
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Solution Overview
Problem
The process of creating short-form video-on-demand (VOD) or audio-on-demand (AOD) clips from long-form media content is time-consuming and inefficient due to manual processes, resulting in a limited number of clips available for consumption.
Innovation Solution
A system and method for automated video/audio comprehension and clipping that identifies specific topics and entities in media content, using speech-to-text transcription, entity recognition, and classification to create short clips with titles, and provides a user interface for easy selection and viewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection and review processes are used to create short-form clips from long-form media content, then clip quality and relevance can be maintained, but the time required to create clips increases significantly and the quantity of clips available is limited
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated system that uses machine learning models to analyze media content. The system automatically transcribes audio, identifies entities and topics, segments content, and generates clips without human intervention, thereby eliminating time-consuming manual inspection while maintaining clip quality through intelligent algorithms.
Solution Approach 2:
The system performs self-service by automatically analyzing long-form media content, identifying relevant segments, and generating short-form clips independently. The machine learning models self-train on media data to improve their ability to accurately identify entities, topics, and appropriate clipping points without requiring continuous human oversight or manual creation processes.
2Quantity of substance
If manual processes are used to identify specific topics and create clips, then accuracy in topic identification can be maintained, but the quantity of clips produced is very limited
Solution Approach 1:
The patent replaces manual topic identification with automated machine learning models that process media content at scale. The system uses transcription services, entity recognition algorithms, and topic modeling to automatically identify relevant segments across large volumes of media content, enabling high-throughput clip production while maintaining accuracy through intelligent processing.
Solution Approach 2:
The system performs preliminary analysis by pre-processing media content through transcription, entity extraction, and topic identification before actual clip creation. This preliminary action allows the system to prepare and index content in advance, enabling rapid clip generation when needed and significantly increasing the quantity of clips that can be produced from large media libraries.
3Productivity
If automated clipping systems are implemented, then clip creation time is reduced and clip quantity increases, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the complex media analysis task into distinct modular components: transcription module, entity recognition module, topic identification module, segment selection module, and clip generation module. Each component handles a specific function independently, making the overall complex system manageable and easier to implement through coordinated interaction of simpler subsystems.
Data Source
AI summary
Systems and Methods for Video/Audio Comprehension and Automated Clipping includes providing at least one media clip (MC) within an event for display or listening on a user device including receiving audio or video media data indicative of the event, transcribing the media data into timestamped text, identifying entities within the text, creating text segments having a begin timestamp and end timestamp and having a minimum number of entity mentions in the text segments, clipping from the media data the at least one media clip having a begin timestamp and end timestamp corresponding to the begin timestamp and end timestamp of a corresponding one of the text segments, and providing the at least one media clip to the user device for viewing or listening by a user. Feedback may also be provided to adjust the logic that identifies MCs. MC Alerts may also be sent to users autonomously or based on user-set parameters.