# How Chatbots Use AI and Fact Databases to Learn and Improve > This patent describes how a chatbot can combine the broad answering abilities of a large language model with a structured knowledge graph to provide more accurate, verifiable, and debuggable responses, especially for repeat questions. - **Patent:** US 12531820 - **Original title:** Knowledge graph assisted large language models - **Owner:** Amazon Technologies - **Granted:** 2026 - **Status:** Active - **Times cited:** 1 - **Field:** software, ai_ml, telecommunications, consumer_electronics ## What it does This patent describes a method for a chatbot to answer user queries by combining a large language model (LLM) with a knowledge graph. When a query is received, the system first converts it into a 'formal query language' to search the knowledge graph for relevant information. This information from the knowledge graph is then given to the LLM to help it generate a more accurate 'formal language response' to the query. Critically, the system then 'validates an accuracy' of this response. After validation, the formal response is converted back into a natural language answer for the user. Based on this validated response, or user feedback, the knowledge graph is updated by 'modifying,' 'removing,' or 'adding' information, making the chatbot smarter over time. For example, if a user asks about a product's specifications, the chatbot might use the knowledge graph to find basic facts, then send those facts to an LLM to generate a natural-sounding answer, and finally use that answer to correct or expand the product data in the knowledge graph. ## What it does NOT cover - Does not cover chatbots that rely solely on a large language model without querying a structured knowledge graph for information. - Does not cover systems that do not convert natural language queries into a 'formal query language' for a knowledge graph. - Does not cover chatbots that fail to validate the accuracy of the large language model's response before updating the knowledge graph. - Does not cover systems where the knowledge graph is not updated based on the large language model's responses or user feedback. - Does not cover large language models that generate responses without first receiving specific information from a knowledge graph node. ## The clever bit The cleverness lies in using the knowledge graph to both guide the LLM's responses with factual data and to continuously learn from the LLM's validated outputs, creating a self-improving, more reliable system. This combines the LLM's broad understanding with the knowledge graph's structured accuracy. ## Real-world examples 1. Amazon Alexa's information retrieval 2. Customer service chatbots for e-commerce 3. Virtual assistants providing factual answers 4. Enterprise search tools enhanced with AI 5. AI-powered question-answering systems ## Why it matters This patent addresses a key challenge with large language models: their tendency to sometimes produce incorrect or 'hallucinated' information. By integrating a knowledge graph, the system can ground LLM responses in verifiable facts, improving reliability. This approach also allows for easier debugging and the ability to provide 'provenance data,' or sources, for answers, which is crucial for trust in AI systems. This combination helps create more dependable and efficient chatbots for various applications. ## Frequently asked questions ### What does How Chatbots Use AI and Fact Databases to Learn and Improve cover? This patent describes how a chatbot can combine the broad answering abilities of a large language model with a structured knowledge graph to provide more accurate, verifiable, and debuggable responses, especially for repeat questions. ### Who owns patent US 12531820? Amazon Technologies owns this patent, granted in 2026. ### When does this patent expire? This patent is expected to expire on September 29, 2043, when the invention enters the public domain. ### What is patent US 12531820 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? This patent addresses a key challenge with large language models: their tendency to sometimes produce incorrect or 'hallucinated' information. By integrating a knowledge graph, the system can ground LLM responses in verifiable facts, improving reliability. This approach also allows for easier debugging and the ability to provide 'provenance data,' or sources, for answers, which is crucial for trust in AI systems. This combination helps create more dependable and efficient chatbots for various applications. ### What does this patent NOT cover? Does not cover chatbots that rely solely on a large language model without querying a structured knowledge graph for information. **Full plain-English explainer:** https://patentbrief.org/patent/us/12531820/knowledge-graph-assisted-large-language-models **Original patent:** https://patents.google.com/patent/US12531820 --- _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 a Computer System Fact-Checks AI Language Models](https://patentbrief.org/patent/us/12073180/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-12073180) — This patent describes a method for a separate computer system to fact-check and improve the output of a large language model by translating its text into a structured, machine-readable format. - [How a Computer System Checks and Improves AI Text](https://patentbrief.org/patent/us/11989527/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-11989527) — This patent describes a method where a separate computer system, using structured knowledge, checks and corrects the output from a large language model to make it more accurate and consistent. - [How to Make AI Smarter by Secretly Adding Facts to Your Questions](https://patentbrief.org/patent/us/20250094707/supplementation-of-large-language-model-knowledge-via-prompt-modification) — This patent describes a system that automatically finds important words in your questions to an AI, then adds relevant facts from a knowledge base before the AI answers, making the AI's response more informed without you knowing the extra facts were added. - [How to Check if a Large Language Model's Answer is True](https://patentbrief.org/patent/us/20250200392/large-language-model-verification) — Amazon's patent details a system for verifying large language model responses by having a second AI translate the answer into formal logic, which an automated theorem prover then checks for validity or invalidity. - [How a Computer System Improves Large Language Model Output with Structured Data](https://patentbrief.org/patent/us/12067362/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-12067362) — This patent describes a method where a separate computer system analyzes and refines the initial output from a large language model (LLM) by translating it into a highly structured, machine-readable language to reduce bias and improve accuracy.