How AI Connects Different Databases Using Knowledge Graphs
This patent describes a server-based method that uses artificial intelligence and two learning models to automatically find and integrate connections between data fields and data values across multiple databases that have different structures.
Original patent title: “System and method of integrating databases based on knowledge graph”
This patent describes a server-based method that uses artificial intelligence and two learning models to automatically find and integrate connections between data fields and data values across multiple databases that have different structures. Granted to Samsung Electronics Co in 2022 with 19 claims and 2 forward citations, and it is expected to expire in 2039.
Coverage
What does this patent actually cover?
This patent describes a system for automatically integrating information from several databases, even if they are organized differently. First, the system creates 'knowledge graphs' for each database, which are like maps showing how data is structured (classes) and what specific data exists (instances) (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). These individual knowledge graphs are then fed into a 'first learning model' (an AI algorithm) to figure out how the data fields, or 'classes,' from different databases relate to each other. For example, it might learn that 'customer_ID' in one database is the same as 'client_number' in another. Next, the system uses a 'second learning model' to find connections between the actual data values, or 'instances,' across these databases, building on the class correlations already found (Claim 1). This results in a comprehensive, virtual integrated knowledge graph that can answer complex questions across all connected databases (Claim 9).
The gap
What does this patent NOT cover?
- Does not cover integrating databases without first generating knowledge graphs from them.
- Does not cover systems that integrate databases using only one learning model to find both class and instance correlations simultaneously.
- Does not cover manual methods of identifying correlations between data fields or values across databases.
- Does not cover systems that rely solely on predefined schemas or mapping rules without using AI learning models to discover correlations.
- Does not cover integrating databases where the learning models do not distinguish between correlations of 'classes' (data fields) and 'instances' (data values).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is using two distinct AI learning models: one specifically to find relationships between the *types* of data (classes) across different databases, and a second one to then find relationships between the *actual pieces of data* (instances), building on the first model's findings. This two-step, AI-driven approach automates a complex task that usually requires extensive manual effort.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Enterprise data lakes and data warehouses
Customer Relationship Management (CRM) systems integrating with sales and support databases
Healthcare systems combining patient records from different clinics
Financial institutions linking transaction data from various departments
Supply chain management platforms integrating supplier and logistics databases
Why it matters
The bigger picture
In today's world, organizations often have many databases that don't talk to each other, making it hard to get a complete picture of information. This patent provides a way for AI to automatically find connections across these different data sources. This is crucial for businesses that need to combine customer data, sales figures, and inventory from various systems to make smarter decisions or offer better services. It helps overcome a major challenge in data management by creating a unified view.
Filed
September 3, 2019
Granted
November 22, 2022
Market context
Who's building on this
Companies in this space
Companies like Google, Amazon, and Microsoft, through their cloud services, are actively developing and offering knowledge graph and AI-driven data integration solutions. Startups specializing in data fabric and data mesh architectures also build on these concepts to help enterprises unify their disparate data sources. Samsung, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop AI and data management technologies for its various product lines and services.
Market impact
This patent addresses a fundamental challenge in data management: integrating siloed data. Its approach of using AI and knowledge graphs to automatically discover correlations has influenced the development of modern data integration platforms. It enables more efficient data analytics, improved decision-making, and the creation of comprehensive data views, which are critical for businesses operating with vast and varied datasets. This technology helps reduce the manual effort and complexity traditionally associated with database integration.
Claim 1 — Plain English
What this patent covers
This patent describes a system for automatically integrating information from several databases, even if they are organized differently. First, the system creates 'knowledge graphs' for each database, which are like maps showing how data is structured (classes) and what specific data exists (instances) (Claim 1). These individual knowledge graphs are then fed into a 'first learning model' (an AI algorithm) to figure out how the data fields, or 'classes,' from different databases relate to each other. For example, it might learn that 'customer_ID' in one database is the same as 'client_number' in another. Next, the system uses a 'second learning model' to find connections between the actual data values, or 'instances,' across these databases, building on the class correlations already found (Claim 1). This results in a comprehensive, virtual integrated knowledge graph that can answer complex questions across all connected databases (Claim 9).
The clever bit
The clever part is using two distinct AI learning models: one specifically to find relationships between the *types* of data (classes) across different databases, and a second one to then find relationships between the *actual pieces of data* (instances), building on the first model's findings. This two-step, AI-driven approach automates a complex task that usually requires extensive manual effort.
What it does not cover
- Does not cover integrating databases without first generating knowledge graphs from them.
- Does not cover systems that integrate databases using only one learning model to find both class and instance correlations simultaneously.
- Does not cover manual methods of identifying correlations between data fields or values across databases.
- Does not cover systems that rely solely on predefined schemas or mapping rules without using AI learning models to discover correlations.
- Does not cover integrating databases where the learning models do not distinguish between correlations of 'classes' (data fields) and 'instances' (data values).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Strong
Citation count
10/40
Early citations
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$94K – $300K
Midpoint $187K · 13.2 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
19 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
LEE, Y., Kim, H., Kim, S., KANG, J., LEE, H., Hwang, T., & Lee, J. (2022). How AI Connects Different Databases Using Knowledge Graphs (U.S. Patent No. 11,507,851). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11507851/system-and-method-of-integrating-databases-based-on-knowledge-graph
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
Embed
Add this patent to your site
Drop this plain-English patent card into any blog post or article — free, no signup. It always links back to the full breakdown here.
<div data-patentlens-widget data-patent-number="US11507851"></div> <script src="https://patentbrief.org/embed.js" async></script>
Stay in the loop
Get a weekly digest of new patents.
One email per week. No spam. Unsubscribe anytime.
Keep exploring
Related patents you should know
US 4683195 · 1987
How to Make Billions of Copies of a DNA Segment
This patent describes the Polymerase Chain Reaction (PCR), a method to rapidly create many copies of a specific piece of DNA or RNA, enabling its detection and analysis.
Cetus Corp
US 8697359 · 2014
How to Edit Genes in Human Cells Using an Engineered CRISPR System
This patent describes an engineered CRISPR-Cas9 system for precisely cutting DNA in eukaryotic cells to change how genes work, opening the door for gene editing in complex organisms.
Massachusetts Institute of Technology
US 7657849 · 2010
How the iPhone's Slide-to-Unlock Gesture Works
Apple's 2010 patent describes unlocking a device by dragging a specific graphical image across the touchscreen along a predefined path, a gesture that became iconic with the original iPhone.
Apple Inc
US 4733665 · 1988
How Doctors Implant a Permanent Stent Using a Balloon
This patent describes the method for placing a permanent, expandable wire mesh tube inside a blood vessel or other body tube using a balloon-tipped catheter to widen it and keep it open.
Expandable Grafts Partnership
US 4965188 · 1990
How to Make Many Copies of a DNA Piece with Heat
This patent describes the Polymerase Chain Reaction (PCR) method, a technique to make millions of copies of a specific DNA segment using a heat-resistant enzyme and repeated temperature changes.
Cetus Corp
US 4235871 · 1980
How to Encapsulate Active Materials in Lipid Bubbles Efficiently
This patent describes a method for trapping biologically active substances inside tiny, multi-layered fat bubbles called liposomes, using a specific water-in-oil emulsion and gel-forming process to improve how much material gets captured.
Individual
Semantically similar
You might also find these interesting
US 12505360 · 2025 · Bmc Helix
How AI Learns to Fix IT Problems by Asking for Feedback
US 11704899 · 2023 · Meta Platforms
How Assistant Systems Combine Information About One Thing from Many Places
US 9361579 · 2016 · International Business Machines Corp
How Computers Calculate Probabilities in Large Knowledge Bases
US 9607103 · 2017 · Ab Initio Technology LLC
How Computers Match and Join Messy Data from Different Sources
More to explore
More in Software & Internet
US 4405829 · 1983 · Massachusetts Institute of Technology
How RSA Public-Key Encryption Keeps Digital Messages Secret
US 6285999 · 2001 · Leland Stanford Junior University
How Websites Get Ranked by Importance
US 5960411 · 1999 · Amazon com Inc
How Amazon's One-Click Ordering Works for Online Purchases
US 7669123 · 2010 · Facebook Inc
Displaying Friends' Activities in a Social Network Feed
New to patents?
Common Questions
Frequently Asked Questions
What does How AI Connects Different Databases Using Knowledge Graphs cover?
This patent describes a server-based method that uses artificial intelligence and two learning models to automatically find and integrate connections between data fields and data values across multiple databases that have different structures.
Who owns patent US 11507851?
Samsung Electronics Co owns this patent, granted in 2022.
When does this patent expire?
This patent is expected to expire on September 3, 2039, when the invention enters the public domain.
What is patent US 11507851 cited by?
This patent has been cited by 2 later patents that build on its ideas.
What problem does this patent solve?
In today's world, organizations often have many databases that don't talk to each other, making it hard to get a complete picture of information. This patent provides a way for AI to automatically find connections across these different data sources. This is crucial for businesses that need to combine customer data, sales figures, and inventory from various systems to make smarter decisions or offer better services. It helps overcome a major challenge in data management by creating a unified view.
What does this patent NOT cover?
Does not cover integrating databases without first generating knowledge graphs from them.
Patent monitoring