Streaming Content Search Using Semantic Metadata Vectors

The patent transforms the search parameter from discrete keywords to continuous vector representations. By converting both search terms and content metadata into vector space using language models, the system enables semantic similarity search that captures meaning rather than relying on exact keyword matches, thereby improving search accuracy while maintaining manageable system complexity through established NLP techniques

The patent replaces the mechanical keyword-matching system with a semantic understanding system based on language models. Instead of simple string comparison, the system uses trained language models to generate vector representations that encode semantic meaning, allowing for more accurate and nuanced content retrieval based on conceptual similarity rather than literal term matching

The patent applies preliminary action by pre-computing and storing vector representations of content metadata (synopses, hashtags, genres) in advance. When a search query arrives, the system only needs to compute the vector for the search term and compare it against the pre-computed content vectors, significantly reducing real-time computational energy consumption while maintaining high similarity determination accuracy

The patent segments the computational task by dividing it into offline preprocessing (training the language model and computing content vectors) and online querying (computing search term vector and performing similarity comparison). This segmentation allows the energy-intensive operations to be performed when computational resources are abundant, while the actual search operation requires minimal energy