Capital one services, llc (20240311556). SYSTEMS FOR DATABASE SEARCHING AND DATABASE SCHEMAS MANAGEMENT AND METHODS OF USE THEREOF simplified abstract

SYSTEMS FOR DATABASE SEARCHING AND DATABASE SCHEMAS MANAGEMENT AND METHODS OF USE THEREOF

Organization Name

capital one services, llc

Inventor(s)

Cruz Vargas of Ocean Springs MS (US)

Phoebe Atkins of Midlothian VA (US)

Alexander Lin of Arlington VA (US)

Joshua Edwards of Philadelphia PA (US)

Lin Ni Lisa Cheng of New York NY (US)

Rajko Ilincic of Annandale VA (US)

Max Miracolo of Brooklyn NY (US)

Brian Mcclanahan of Hyattsville MD (US)

SYSTEMS FOR DATABASE SEARCHING AND DATABASE SCHEMAS MANAGEMENT AND METHODS OF USE THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240311556 titled 'SYSTEMS FOR DATABASE SEARCHING AND DATABASE SCHEMAS MANAGEMENT AND METHODS OF USE THEREOF

The patent application describes systems and methods for database search using word embeddings to measure similarity between documents.

• Word embeddings are generated for words in an input document to create vector representations.
• An average input document word embedding vector is calculated.
• Stored documents with average stored document word embedding vectors are accessed.
• A similarity model is used to determine the similarity metric between the input document and stored documents.
• The similarity is based on the average input document word embedding vector and the average stored document word embedding vector.

Potential Applications: - Information retrieval systems - Document search engines - Content recommendation systems

Problems Solved: - Enhancing search accuracy - Improving document retrieval efficiency

Benefits: - Faster and more accurate search results - Enhanced user experience - Increased productivity in information retrieval tasks

Commercial Applications: Title: Advanced Document Search Engine Technology This technology can be utilized in various industries such as: - E-commerce for product recommendations - Legal research for case law analysis - Academic research for literature review

Prior Art: Researchers can explore existing technologies in natural language processing and information retrieval systems for related prior art.

Frequently Updated Research: Stay updated on advancements in word embedding techniques and similarity models for document search applications.

Questions about Database Search Technology: 1. How does word embedding technology improve document search accuracy?

  - Word embeddings capture semantic relationships between words, enabling more precise similarity measurements.

2. What are the key differences between traditional keyword-based search and word embedding-based search?

  - Traditional keyword-based search relies on exact matches, while word embeddings consider semantic similarities for better results.

Original Abstract Submitted

systems and methods of the present disclosure enable database search. the systems and/or methods may include receiving a search query that includes an input document having text. word embeddings are generated within the input document, where the word embeddings include vector representations of words in the text of the input document. an average input document word embedding vector is determined for the word embeddings of the input document. a set of stored documents is accessed, where each stored document includes a stored text has a particular average stored document word embedding vector. a similarity model is used to determine a similarity metric measuring the similarity between the input document and each stored document based on the average input document word embedding vector and the particular average stored document word embedding vector of each stored document.