Structured Query Routing for Natural-Language Playlist Search
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Solution Overview
Problem
Existing neural networks, such as large-language models (LLMs), struggle with handling unstructured user queries for media content due to their broad knowledge but lack of detailed information, leading to computationally intensive and inefficient search operations.
Innovation Solution
A system that uses a neural network to generate structured queries from unstructured search prompts, optionally leveraging vector-space models and metadata to efficiently identify media items, reducing computational load by applying structured queries when appropriate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to handle unstructured user queries, then broad knowledge and versatility are improved, but computational efficiency and processing time deteriorate
Solution Approach 1:
The system segments the query handling process into two distinct paths: (1) structured query processing for specific, factual questions that can be answered from metadata, and (2) unstructured natural language processing for broad, open-ended queries. This segmentation allows each path to use the most appropriate model, improving overall efficiency while maintaining versatility.
Solution Approach 2:
The patent introduces an intermediary component that translates unstructured natural language queries into structured queries. This intermediary layer acts as a bridge between the user's natural language input and the structured query processing system, enabling efficient handling of diverse queries without requiring the LLM to process every query directly.
2Ease of operation
If LLMs are used to directly identify media items from unstructured queries, then query understanding capability is improved, but processing time and computational resources worsen
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring queries before they are submitted to the LLM. The query restructuring component prepares queries in advance in a format that is optimized for both structured and unstructured processing, reducing the computational burden during actual query execution and minimizing processing time.
3Measurement precision
If multiple passes through neural network are made to directly produce desired outputs, then query accuracy is improved, but computational load and processing time worsen
Solution Approach 1:
The system implements dynamic query routing that adapts the processing path based on the characteristics of each query. Rather than always making multiple passes through the neural network, the system dynamically determines whether a query requires structured processing, unstructured processing, or a combination, optimizing computational load while maintaining accuracy for each specific case.
Data Source
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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.