AI-Assisted Streaming Video Scene Search From Natural-Language Queries
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Solution Overview
Problem
Existing streaming video applications lack an efficient way for users to locate specific scenes within content, relying on title, keyword, or episode number searches that do not accommodate user descriptions of scenes.
Innovation Solution
Utilizing an AI model, specifically a Large Language Model (LLM), to interpret user queries describing scenes and search the streaming content library's metadata to locate matching scenes, which can then be displayed or navigated to directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional keyword or title search methods are used, then the search system remains simple and fast, but users cannot locate specific scenes based on natural language descriptions
Solution Approach 1:
The patent introduces an AI model as an intermediary between the user's natural language query and the video content database. The model translates descriptive queries into searchable parameters, enabling scene location without requiring users to know exact titles or keywords. This mediator handles the complexity of understanding natural language while keeping the user interface simple.
Solution Approach 2:
The patent replaces traditional mechanical search systems (keyword matching, title searching) with an AI-based semantic understanding system. Instead of relying on exact string matches or structured metadata, the system uses machine learning models to interpret the meaning of user descriptions and match them with relevant video scenes, substituting rigid mechanical search with flexible intelligent processing.
2Measurement precision
If AI models are introduced to interpret user queries and locate scenes, then scene search accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing video content into structured representations that the AI model can efficiently query. Video metadata, scene descriptions, and content features are prepared in advance, allowing the model to quickly match user queries against pre-organized data rather than analyzing raw video content in real-time.
Solution Approach 2:
The system applies partial action by focusing the AI model's processing on the most relevant features and parameters for scene matching, rather than analyzing every aspect of the video content. The model selectively processes only the necessary portions of the content database that are likely to match the user's query, reducing overall processing time while maintaining accuracy.
3Reliability
If the content database is searched comprehensively to ensure all relevant scenes are found, then search completeness improves, but search speed decreases
Solution Approach 1:
The patent segments the large content database into smaller, organized units such as individual video titles, episodes, or scene collections. The AI model can then search through these segmented units independently and in parallel, improving search speed while maintaining completeness. Each segment can be processed separately and results aggregated, ensuring no relevant scenes are missed.
Solution Approach 2:
The system performs partial searching by initially focusing on the most promising segments of the database based on the user query's key parameters. The search expands to cover more comprehensive areas only if needed, balancing speed and completeness by avoiding unnecessary processing of all content while ensuring relevant scenes are found.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for using a Machine Learning (ML) model to interpret a user query and locate one or more scenes on a streaming video application. The apparatus, consisting of memory and a processor connected to the memory, facilitates the reception of search queries from a user through a remote device. Utilizing the ML model, the processor conducts a comprehensive search of a content database specific to the streaming application to pinpoint relevant scene(s) that correspond to the user's request. The identified scenes are then presented on a display device. This approach simplifies the process of navigating extensive video content, providing users with an efficient means to access their desired scenes directly, enhancing user engagement with the streaming video application. The disclosed technology encompasses various embodiments, suitable for application across multiple types of hardware capable of streaming media content.