Video to data

What is this patent about?

’954 is related to the field of automated video analysis and content generation. It addresses the technical challenge of extracting meaningful, context-aware metadata from video streams by simultaneously processing both auditory and visual data. The background context involves improving the accuracy of image recognition and speech transcription, which often suffer from false positives or a lack of semantic understanding when processed in isolation.

The underlying idea behind ’954 is the use of cross-modal synchronization to validate and refine data extracted from a video. By processing audio and image frames in parallel across a distributed architecture, the system uses the contextual topic derived from one stream (such as audio transcripts) to weight the probability of accuracy for objects identified in the other stream (the image frames). This creates a feedback loop where the audio context helps resolve visual ambiguities, such as distinguishing between a basketball and a soccer ball based on the surrounding commentary.

The claims of ’954 focus on a method and system that decompose a video into parallel processing pipelines for audio-to-text conversion and object identification. The independent claims specifically protect the mechanism of assigning a probability of accuracy to identified objects based on extracted contextual topics. This cross-referenced data is then used to generate new content—such as contextual text, images, or animations—which are integrated back into the original media to produce a content-rich video.

In practice, the invention functions by segmenting video and audio into small chunks that are handled by a distributed computation engine. While the audio pipeline performs speech recognition and keyword detection, the image pipeline performs edge detection and pattern recognition to identify faces, logos, or specific objects. The system then performs a temporal sync of these findings, using natural language processing to ensure that the metadata applied to a specific frame range is semantically consistent with what is being heard at that exact timestamp.

This approach differs from prior solutions by moving beyond simple keyword tagging or standalone image search. Instead of treating background elements as noise, the system uses them to build a probabilistic model of the scene's context. By leveraging the linear sequencing of video frames rather than static images, the invention can identify complex actions and reduce false positives, ultimately allowing for more precise advertisement targeting, automated closed captioning, and enhanced video search optimization.