Music Segment Tagging and Image Generation for Context-Rich Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing music segment sharing systems lack the ability to provide relevant context and context-based tags, leading to suboptimal search results and user engagement.
Innovation Solution
A system that automates the generation of context-based tags and images for music segments using natural language processing and machine learning, incorporating artist and historical information to improve search relevance and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated tag generation using natural language processing is implemented, then search relevance and user engagement are improved, but system complexity and processing time increase
Solution Approach 1:
The system pre-generates context-based tags for music segments during off-peak times or as part of the initial ingestion process, so that when users perform searches, the tags are already available and no real-time processing is needed. This resolves the contradiction by improving search relevance through automated tagging while avoiding the complexity and processing time overhead during user interactions.
Solution Approach 2:
The patent introduces an intermediary layer between the music segments and the search functionality - a pre-computed tag database that stores context-based tags generated from artist biographical information, historical context, and other external sources. This intermediary allows the system to provide relevant search results without requiring complex real-time processing, thus improving search relevance while managing system complexity.
2Loss of information
If context-based tags including artist biographical information and historical information are added, then information richness is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs information enrichment in advance by gathering artist biographical information, historical context, and other external data during the initial tag generation phase. This pre-processing ensures that comprehensive information is available without requiring extensive processing time during user queries, thus improving information richness while minimizing data processing time impact.
Solution Approach 2:
The tag generation process is segmented into multiple independent components: basic metadata extraction, context-based tag generation, and image generation. Each segment can be processed separately and cached, allowing the system to provide rich information without requiring all processing to occur simultaneously, thus reducing overall data processing time while maintaining information richness.
3Ease of operation
If automated image generation based on music segment data is implemented, then user engagement and visual appeal are improved, but computational energy consumption and processing time increase
Solution Approach 1:
The system generates images for music segments in advance during off-peak hours or as part of the initial processing pipeline, so that when users access the content, the images are already available. This resolves the contradiction by improving user engagement through visual appeal while avoiding high computational energy consumption during user interactions, as the heavy image generation workload is performed beforehand.
Solution Approach 2:
The system implements selective image generation based on user behavior patterns and content priorities. Instead of generating images for all music segments uniformly, it focuses computational resources on generating images for high-priority content or content likely to be frequently accessed, thus improving user engagement for key content while reducing overall computational energy consumption.
Data Source
AI summary
A method of providing a music segment responsive to a user request includes receiving a natural language request describing one or more properties of a music segment and querying an electronic database based on the natural-language request. The electronic database organizes a plurality of music segments and a plurality of sets of descriptive tags. Each music segment of the plurality of music segments corresponds to one set of descriptive tags of the plurality of sets of descriptive tags, each music segment of the plurality of music segments has basic metadata information including an artist name and release year, and each set of descriptive tags of the plurality of sets of descriptive tags is distinct from corresponding basic metadata information and includes a descriptive tag describing at least one of artist biographical information, artist political affiliation, and historical information in a release year of the music segment.