Embedded Vector Database for Local Hybrid Data Queries

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Solution Overview

Problem

Existing vector databases are limited by reliance on off-premises servers, leading to issues with internet access constraints, single-point-of-failure scenarios, and exposure of confidential data, and lack support for querying with non-vector data.

Innovation Solution

A vector database designed for embedded systems that stores and manages both vector and non-vector data, enabling local operations, reducing reliance on internet access, mitigating single-point-of-failure risks, and supporting queries with both data types.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If vector databases are stored on off-premises servers, then centralized management and access control are improved, but internet access constraints and single-point-of-failure risks increase

Engineering Contradiction:

Improvecentralized managementVSAvoidsingle-point-of-failure risk

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent segments the vector database system into distributed embedded instances across multiple edge devices rather than a single centralized server. Each embedded system maintains local vector database capabilities, eliminating the single-point-of-failure risk while preserving management functionality through distributed architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces synchronization mechanisms and data replication as intermediaries between embedded systems and external servers. This allows centralized management features to be maintained through coordinated communication while ensuring local systems can operate independently when internet access is unavailable.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Quantity of substance

If vector databases are stored on off-premises servers, then hardware resource requirements for individual devices are reduced, but exposure of confidential data and internet access constraints increase

Engineering Contradiction:

Improvehardware resource requirementsVSAvoidconfidential data exposure

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The patent implements local quality by deploying vector database functionality directly within embedded systems at the edge. This allows confidential data to remain localized where it is generated and processed, minimizing exposure risks during transmission while maintaining necessary computational capabilities locally.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent uses data replication and synchronization mechanisms that create local copies of vector databases in embedded systems. This copying approach enables devices to have sufficient hardware resources locally stored data while reducing the need for constant internet access, thereby mitigating data exposure risks during transmission.

Inventive Principle:

Principle #26Copying

3Device complexity

If vector databases are stored on off-premises servers, then device complexity is reduced, but lack of support for querying with non-vector data increases

Engineering Contradiction:

Improvesystem architecture complexityVSAvoidquery capability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent implements multi-functionality by designing the embedded vector database system to handle both vector and non-vector data types within the same local database instance. This universal approach enables diverse query capabilities including vector similarity search, traditional SQL queries, and hybrid operations without requiring separate systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent merges vector database functionality with traditional database capabilities into a unified embedded system. This combination allows the system to manage both vector and non-vector data in a single localized database, enhancing query versatility while maintaining manageable device complexity through integrated architecture.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250390471A1Method for Embedded Vector Databases

Publication Date: 2025.12.25 GLAZER ALON

AI summary

A method for managing vector data within a vector database is disclosed, encompassing a storage mechanism to store digital representations of vectors with associated metadata characteristics such as length, magnitude, and numerical type. The method includes data fields capable of storing non-vector data, including text, numeric, and general-use memory fields (blob). It features search and retrieval algorithms allowing users to query using both vector and non-vector data characteristics and functions, along with data manipulation mechanisms for modifying stored data, including vectors. The method employs a query language for manipulating and retrieving stored data. Additionally, the method provides application programming interface (API) endpoints for third-party integration and a computer-memory storage mechanism enabling the saving, loading, and transfer of the vector database data between devices and software processes. The database can be run on various platforms, including embedded systems, mobile and handheld devices, robotic devices, vehicles, wearable devices, and Internet-of-Thing devices.