Semantic Video Search Query Generation via Automated Extraction
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Solution Overview
Problem
Users face difficulty in finding relevant video content among vast amounts due to subjective user-provided descriptions and lack of tools to determine appropriate keywords, leading to low visibility and irrelevance in search results.
Innovation Solution
A method that automatically extracts semantic data from selected content, generates queries based on 'subject-verb-object' relationships, and presents relevant video content, reducing user subjectivity and improving search relevance by targeting specific video services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-provided descriptions are used for video content search, then users can provide their own keywords, but the search results become subjective and less relevant
Solution Approach 1:
The patent introduces an intermediary system that automatically extracts semantic data from video content and generates optimized search queries. This intermediary layer between the user-provided descriptions and the search engine transforms subjective user inputs into objective, structured semantic representations, thereby maintaining user freedom while improving search relevance through automated semantic analysis and query generation.
2Ease of operation
If manual keyword selection is required for searching, then users can control search terms, but users lack tools to determine appropriate keywords
Solution Approach 1:
The patent applies preliminary action by automatically extracting semantic data and generating optimized search queries before the user performs the search. The system pre-processes the video content to identify key semantic elements and constructs search queries in advance, eliminating the need for users to manually determine appropriate keywords while preserving their ability to control search terms through the generated queries.
3Ease of manufacture
If free text descriptions are used for video content, then content can be easily uploaded, but search accuracy decreases due to subjective vocabulary
Solution Approach 1:
The patent replaces the mechanical system of manual keyword tagging with an automated semantic analysis system. Instead of relying on users to manually select and input keywords, the system uses automated extraction of semantic data from video content and generates search queries through computational processes, thereby maintaining upload simplicity while significantly improving search accuracy through objective semantic analysis.
4Measurement precision
If automated semantic extraction is implemented, then search relevance improves, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically extract semantic data from video content and generate optimized search queries without requiring external intervention. The automated semantic analysis system serves itself by processing video content, identifying key semantic elements, and constructing search queries independently, thereby improving search relevance while managing system complexity through self-contained automated processes.
Data Source
AI summary
A method for assisting video content searches over a communication network by a user, comprising: A step of determining a first content item (CZ) by said user; A step of automatically extracting semantic data from this first content item; A step of automatically generating queries for at least one service, as a function of semantic data, making it possible to retrieve a set of videos on this service or these services; A step of presenting that set of video content to the user.