Video to data

What is this patent about?

’972 is related to the field of automated video analysis and content generation. It addresses the technical challenge of efficiently extracting meaningful semantic information from large video files, which typically requires significant computational resources to process both visual and auditory streams simultaneously.

The underlying idea behind ’972 is the parallelized synchronization of disparate data streams to create a unified semantic map of a video. By splitting a video into discrete audio and image segments and distributing them across a cluster of processors, the system can perform object recognition and speech-to-text transcription in near real-time, subsequently cross-referencing these outputs to verify and weight the most relevant topics.

The claims of ’972 focus on a method and system that decomposes a video into audio and image files for parallel processing, specifically extracting and identifying objects to derive topical meta-data. The independent claims cover the specific workflow of generating video text through the cross-referencing of transcribed audio and identified visual objects, which is then used to dynamically inject new text, images, or animations back into the video stream.

In practice, the invention functions by segmenting audio at spectrum thresholds to identify quiet periods and dividing image frames into chunks for distributed processing. This allows the system to perform color edge detection and object template matching alongside natural language processing of the dialogue. By comparing the visual symbols found—such as a specific brand or object—with the topics extracted from the audio, the system assigns higher weights to confirmed themes, ensuring the generated meta-data accurately reflects the video's context.

This approach differs from prior art by moving beyond simple keyword transcription or basic image search. Instead of treating audio and video as isolated tracks, ’972 utilizes a multi-layered architecture to fuse visual and auditory semantics into a single data file. This enables sophisticated downstream applications, such as placing contextually relevant advertisements at precise timestamps or optimizing video search engines through deep, automated indexing of the actual content within the frames.