Embedding-Based Data Retrieval for Large Multimedia Indexes
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Solution Overview
Problem
Existing data retrieval methods require significant computing power and storage, often leading to inefficient use, especially when dealing with large databases and multimedia files, and struggle to accurately identify specific sets of data without pre-loading all files.
Innovation Solution
A method and system that classify data into distinct indices with micro-indices using pre-trained mathematical models to generate embeddings, allowing efficient retrieval of specific data sets based on simple queries, reducing the need for extensive computing power and enabling quick access to multimedia files or IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classification methods use pre-defined explicit categories and rule-based systems, then data can be organized into indexes, but the risk of error increases and may propagate through the retrieval process
Solution Approach 1:
The patent replaces rule-based mechanical classification systems with deep learning-based embedding models. Instead of using predefined categories and manual rule-based classifiers, the system uses neural networks to automatically learn and generate embeddings that capture semantic relationships, thereby eliminating error propagation from rigid rule systems while maintaining organizational efficiency.
Solution Approach 2:
The patent transforms the classification approach by changing from discrete categorical parameters to continuous embedding space parameters. Data and queries are represented as vectors in a high-dimensional space where similarity is measured by distance metrics, allowing for more nuanced and accurate classification without requiring explicit predefined categories.
2Reliability
If all multimedia files are pre-loaded for processing and transmission, then complete data availability is achieved, but computing power and storage requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing only the embedding representations of multimedia files rather than loading the actual files. These embeddings serve as compact proxies that enable efficient similarity search and retrieval without requiring the full multimedia content to be loaded into memory, significantly reducing storage and computing requirements while maintaining retrieval effectiveness.
Solution Approach 2:
The patent extracts only the essential semantic features of multimedia files in the form of embeddings, separating this compressed representation from the full original files. This extraction allows the system to work with lightweight vector representations for classification and retrieval operations, eliminating the need to handle large multimedia files during the search process.
3Measurement precision
If deep learning models are trained from scratch for each query task, then task-specific accuracy is maximized, but training time and computational resources are excessively consumed
Solution Approach 1:
The patent uses pre-trained embedding models that have been trained in advance on large corpora. These pre-trained models capture general semantic relationships and can be directly applied to new query tasks without requiring task-specific training, thereby eliminating the time-consuming training process while maintaining high accuracy in matching queries to relevant data.
Solution Approach 2:
The patent employs universal pre-trained embedding models that can handle multiple different query tasks and data types without requiring separate training for each task. These models serve multiple functions across different applications, reducing the need for repeated training and enabling the system to adapt to new tasks by simply providing new data for embedding generation.
Data Source
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AI summary
The present technology relates to a method for retrieving at least one specific set of data, also called documents, among a plurality of data (100) using a specific query. The method is a computer-implemented method. The method is configured to be executed by at least one computer-implemented system, such as a computer or a server, for example.