Automated Digital Document Generation from Video
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Solution Overview
Problem
Conventional techniques for consuming digital videos require significant time and focused attention, making them inefficient and impractical for scenarios where immediate information is needed, such as roadside tire changes or kitchen baking.
Innovation Solution
The automated generation of digital documents from digital videos using machine learning, which extracts textual components describing entity and action sequences, allowing for efficient consumption and generation of concise, actionable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If digital videos are used to convey detailed information, then information completeness is improved, but time commitment and accessibility deteriorate
Solution Approach 1:
The patent extracts key information from digital videos by generating transcripts through speech-to-text conversion and identifying important entities and actions. This extraction process creates a condensed textual representation that preserves essential information while eliminating the need to watch the entire video, thus resolving the contradiction between information completeness and time commitment.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between the video content and the user. This system performs speech-to-text conversion, entity recognition, and action identification to generate a structured textual summary, allowing users to access video information without directly watching the video, thereby reducing time commitment while maintaining information quality.
2Loss of information
If digital videos are used to provide detailed instructions, then information quality is improved, but ease of operation deteriorates
Solution Approach 1:
The patent segments video content into discrete textual components including entities, actions, and structured steps. By dividing the continuous video stream into identifiable segments with specific roles (objects, verbs, procedures), the system makes information easier to scan and understand while preserving the detailed quality of the original content.
Solution Approach 2:
The patent replaces the mechanical act of watching and listening to videos with a textual reading system. By converting speech to text and structuring it with entities and actions, the system substitutes the passive video consumption mechanism with an active text-based information retrieval approach that is easier to operate and consume.
3Loss of information
If multiple digital videos are consumed to gain comprehensive information, then information completeness is improved, but productivity deteriorates
Solution Approach 1:
The patent merges information from multiple digital videos by processing their transcripts together and identifying common entities and actions. This consolidation approach combines the informational content of multiple sources into a unified structured representation, achieving comprehensive information coverage while eliminating the need to separately consume each video, thus improving productivity.
Data Source
AI summary
Techniques are described that support automated generation of a digital document from digital videos using machine learning. The digital document includes textual components that describe a sequence of entity and action descriptions from the digital video. These techniques are usable to generate a single digital document based on a plurality of digital videos as well as incorporate user-specified constraints in the generation of the digital document.