Samsung electronics co., ltd. (20240330290). SYSTEM AND METHOD FOR PROCESSING EMBEDDINGS simplified abstract
Contents
SYSTEM AND METHOD FOR PROCESSING EMBEDDINGS
Organization Name
Inventor(s)
Susav Lal Shrestha of College Station TX (US)
Rekha Pitchumani of Oak Hill VA (US)
SYSTEM AND METHOD FOR PROCESSING EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330290 titled 'SYSTEM AND METHOD FOR PROCESSING EMBEDDINGS
The abstract of the patent application describes a system that includes a storage device for storing a document embedding vector. An accelerator connected to the storage device processes a query embedding vector and the document embedding vector. A processor connected to the storage device and the accelerator transmits the query embedding vector to the accelerator.
- The system involves a storage device for document embedding vectors.
- An accelerator processes query embedding vectors and document embedding vectors.
- A processor facilitates the transmission of query embedding vectors to the accelerator.
Potential Applications: - Information retrieval systems - Natural language processing applications - Recommendation systems
Problems Solved: - Efficient processing of query and document embedding vectors - Improved search accuracy and relevance
Benefits: - Faster retrieval of relevant information - Enhanced performance of recommendation systems - Streamlined natural language processing tasks
Commercial Applications: Title: Enhanced Information Retrieval System This technology can be utilized in search engines, e-commerce platforms, and content recommendation systems to improve user experience and increase engagement.
Questions about the technology: 1. How does the system handle large volumes of document embedding vectors efficiently? The system utilizes an accelerator to process query and document embedding vectors quickly and accurately.
2. What are the potential limitations of using this technology in real-world applications? The technology may face challenges in scaling for extremely large datasets or in scenarios with high computational requirements.
Original Abstract Submitted
a system is disclosed. a storage device may store a document embedding vector. an accelerator connected to the storage device may be configured to process a query embedding vector and the document embedding vector. a processor connected to the storage device and the accelerator may be configured to transmit the query embedding vector to the accelerator.
