Playlist Generation with Structured Queries for Efficient Media Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing large-language models (LLMs) lack detailed information about media items and are computationally intensive for media content navigation, making it difficult to efficiently identify and organize digital media items.
Innovation Solution
A system that uses a neural network to generate structured queries from unstructured search prompts, optionally leveraging vector-space models to identify media items efficiently, reducing computational load by using world knowledge and metadata filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large-language models (LLMs) are used to handle user queries and identify media items, then the system can process broad intent queries using world knowledge, but the computational cost and time consumption increase significantly
Solution Approach 1:
The system segments the query processing into two distinct paths: (1) LLM-based processing for broad intent queries that require world knowledge, and (2) structured query processing for specific media item retrieval. This segmentation allows each component to operate optimally within its designated scope, reducing unnecessary computational overhead.
Solution Approach 2:
The patent introduces an intermediary structured query generation step that translates natural language queries into efficient search queries. This intermediary layer acts as a bridge between the LLM's world knowledge and the media database, enabling the system to leverage LLM capabilities while avoiding direct computational burden on the LLM for routine retrieval operations.
2Measurement precision
If LLMs are used to directly identify media items from user queries, then the system can provide accurate results based on world knowledge, but the processing time increases due to multiple passes through the neural network
Solution Approach 1:
The system performs preliminary action by generating structured queries from natural language inputs before executing the actual media item retrieval. This preliminary structuring of the query allows subsequent database operations to be performed efficiently without requiring multiple passes through the neural network, thus reducing processing time while maintaining accuracy.
3Productivity
If the system uses only structured queries for media item identification, then computational efficiency improves, but the system loses the ability to effectively handle broad intent queries requiring world knowledge
Solution Approach 1:
The system dynamically selects the appropriate processing path based on the nature of the user query. For queries requiring world knowledge and broad interpretation, the LLM path is activated. For queries that can be directly translated into structured search operations, the structured query path is used. This dynamic adaptation allows the system to optimize computational efficiency while maintaining versatility.
Data Source
AI summary
An electronic device associated with a media-providing service stores, in a vector space, a plurality of respective vector representations for respective media content items. The electronic device receives a user input, including a text string. The electronic device generates, using a neural network, a structured query based on the text string. The electronic device determines, based on the structured query, whether to generate a vector representation of a portion of the text string. When the electronic device determines to generate the vector representation of the portion of the text string, it generates the vector representation of the portion of the text string, wherein the vector representation is embedded in the vector space, and identifies a set of media items using the vector representation of the portion of the text string. And the electronic device provides one or more select media items from the set of media items to a user.