# 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. - **Patent:** US 11507851 - **Original title:** System and method of integrating databases based on knowledge graph - **Owner:** Samsung Electronics Co - **Granted:** 2022 - **Status:** Active - **Times cited:** 2 - **Field:** software, ai_ml, telecommunications, ecommerce, finance, consumer_electronics ## What it does 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). ## 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). ## 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. ## Real-world examples 1. Enterprise data lakes and data warehouses 2. Customer Relationship Management (CRM) systems integrating with sales and support databases 3. Healthcare systems combining patient records from different clinics 4. Financial institutions linking transaction data from various departments 5. Supply chain management platforms integrating supplier and logistics databases ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/11507851/system-and-method-of-integrating-databases-based-on-knowledge-graph **Original patent:** https://patents.google.com/patent/US11507851 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Combine Information from Multiple Knowledge Graphs](https://patentbrief.org/patent/us/20240144032/knowledge-graph-data-fusion) — This patent describes a method for merging information from two or more existing knowledge graphs into a single, unified knowledge graph, using special rules called "graph operators." - [Improving AI Language Understanding with Aligned Knowledge Graphs](https://patentbrief.org/patent/us/11798529/generation-of-optimized-knowledge-based-language-model-through-knowledge-graph-m) — This patent describes a method for making AI language models smarter by combining them with knowledge modules, which learn from two different, but related, knowledge graphs. - [How to Build a Smart Knowledge Graph for Specific Topics](https://patentbrief.org/patent/us/12340316/techniques-for-building-a-knowledge-graph-in-limited-knowledge-domains) — Oracle's patent describes a method for automatically expanding a small, topic-specific knowledge graph by intelligently pulling relevant information from a much larger knowledge graph, helping applications understand user requests better. - [How AI Learns to Fix IT Problems by Asking for Feedback](https://patentbrief.org/patent/us/12505360/continuous-knowledge-graph-generation-using-causal-event-graph-feedback) — This patent describes an AI system that continuously learns to identify and prevent IT issues by building a map of cause-and-effect relationships, getting human feedback, and automatically updating its understanding. - [How Assistant Systems Combine Information About One Thing from Many Places](https://patentbrief.org/patent/us/11704899/resolving-entities-from-multiple-data-sources-for-assistant-systems) — This patent describes a system that gathers all known information about a single person, place, or thing from various sources and combines it into one complete profile for an assistant system.