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.
Original patent title: “Knowledge graph assisted large language models”
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. Granted to Amazon Technologies in 2026 with 25 claims and 1 forward citation, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
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.
The gap
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.
- 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
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
Amazon Alexa's information retrieval
Customer service chatbots for e-commerce
Virtual assistants providing factual answers
Enterprise search tools enhanced with AI
AI-powered question-answering systems
Why it matters
The bigger picture
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.
Filed
September 29, 2023
Granted
January 20, 2026
Market context
Who's building on this
Companies in this space
Amazon Technologies Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing and deploying such systems in products like Alexa and their customer service operations. Other major technology companies like Google, Microsoft, and OpenAI are also investing heavily in combining large language models with structured data sources to improve the factual accuracy and reliability of their AI offerings and chatbots.
Market impact
This patent contributes to the ongoing evolution of AI-powered chatbots and virtual assistants, aiming to overcome the limitations of large language models alone. By improving the reliability and verifiability of AI responses, it enables more trustworthy applications in customer service, information retrieval, and enterprise knowledge management. The approach helps reduce 'hallucinations' and provides a pathway for AI systems to continuously learn and refine their factual knowledge, making them more valuable across various industries.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
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
6/40
Early citations
Claim breadth
17/20
Very broad protection
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
$75K – $240K
Midpoint $150K · 16.9 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.
Claim text not yet imported for this patent
The original legal language
Original claims
25 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Lassila, O. Y., Labai, N., & Bayless, S. (2026). How Chatbots Use AI and Fact Databases to Learn and Improve (U.S. Patent No. 12,531,820). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12531820/knowledge-graph-assisted-large-language-models
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Same assignee
More from Amazon Technologies
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