How to Check if a Large Language Model's Answer is True
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.
Original patent title: “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. Owned by Amazon Technologies with 23 claims and 12 forward citations, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
This patent describes a method for verifying answers from a large language model (LLM). First, a system obtains a 'conversational text' which includes a user's question (query) and the answer from a first LLM. Then, a *second* large language model translates this answer into 'first-order logic,' which is a formal way to represent statements and relationships (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). An automated theorem prover, specifically a 'satisfiability modulo theories solver,' then checks this logic translation. It determines if the answer is 'valid' by checking if its negation is impossible (unsatisfiable), and if it's 'invalid' by checking if the answer itself is impossible (Claim 1). Finally, the system outputs whether the answer is valid, invalid, or neither, to a user interface, database, or report.
The gap
What does this patent NOT cover?
- Does not cover verifying LLM answers without translating them into first-order logic.
- Does not cover verification methods that do not use an automated theorem prover or satisfiability modulo theories solver.
- Does not cover systems where the first LLM directly provides the first-order logic translation without a second LLM.
- Does not cover verifying answers that are not derived from a query-response conversational text from an LLM.
- Does not cover systems that only determine validity or invalidity without also identifying 'neither valid nor invalid'.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in using a *second* large language model to translate a natural language answer into a formal 'first-order logic' representation. This allows a traditional, rigorous automated theorem prover to then formally check the truthfulness of the answer, bridging the gap between flexible AI output and strict logical verification.
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
AI assistants providing factual summaries of documents
Generative AI tools for legal research
Medical diagnostic support systems using LLMs
Automated code generation and verification
Customer service chatbots providing policy information
Why it matters
The bigger picture
Large language models sometimes generate incorrect or misleading information, a problem often called 'hallucination.' This patent offers a structured, automated way to check the factual accuracy of LLM outputs. By providing a formal verification process, it aims to make LLMs more reliable for critical applications where accuracy is paramount, such as in scientific research, legal analysis, or financial advice.
Filed
December 15, 2023
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 large language models across its services, making this verification technology crucial for their offerings like Alexa and AWS AI services. Other major AI developers such as Google, Microsoft, OpenAI, and Anthropic are also heavily invested in improving the factual accuracy and reliability of their LLMs, often exploring similar formal verification or fact-checking methods.
Market impact
This type of verification technology is critical for overcoming a key limitation of large language models: their tendency to 'hallucinate' or generate incorrect information. By providing a mechanism to formally check LLM outputs, this patent enables the deployment of AI in more sensitive and regulated industries, such as healthcare, finance, and legal services. It helps build trust in AI-generated content, potentially expanding the market for reliable AI solutions and reducing the risks associated with their use.
Claim 1 — Plain English
What this patent covers
This patent describes a method for verifying answers from a large language model (LLM). First, a system obtains a 'conversational text' which includes a user's question (query) and the answer from a first LLM. Then, a *second* large language model translates this answer into 'first-order logic,' which is a formal way to represent statements and relationships (Claim 1). An automated theorem prover, specifically a 'satisfiability modulo theories solver,' then checks this logic translation. It determines if the answer is 'valid' by checking if its negation is impossible (unsatisfiable), and if it's 'invalid' by checking if the answer itself is impossible (Claim 1). Finally, the system outputs whether the answer is valid, invalid, or neither, to a user interface, database, or report.
The clever bit
The novelty lies in using a *second* large language model to translate a natural language answer into a formal 'first-order logic' representation. This allows a traditional, rigorous automated theorem prover to then formally check the truthfulness of the answer, bridging the gap between flexible AI output and strict logical verification.
What it does not cover
- Does not cover verifying LLM answers without translating them into first-order logic.
- Does not cover verification methods that do not use an automated theorem prover or satisfiability modulo theories solver.
- Does not cover systems where the first LLM directly provides the first-order logic translation without a second LLM.
- Does not cover verifying answers that are not derived from a query-response conversational text from an LLM.
- Does not cover systems that only determine validity or invalidity without also identifying 'neither valid nor invalid'.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
22/40
Moderately cited
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 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
$100K – $319K
Midpoint $200K · 17.1 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
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Whalen, M. W., Labai, N., KIESL-REITER, B., & Cook, J. B. How to Check if a Large Language Model's Answer is True (U.S. Patent No. 20,250,200,392). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20250200392/large-language-model-verification
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 to Check if a Large Language Model's Answer is True cover?
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.
Who owns patent US 20250200392?
This patent is owned by Amazon Technologies.
When does this patent expire?
This patent is expected to expire on December 15, 2043, when the invention enters the public domain.
What is patent US 20250200392 cited by?
This patent has been cited by 12 later patents that build on its ideas.
What problem does this patent solve?
Large language models sometimes generate incorrect or misleading information, a problem often called 'hallucination.' This patent offers a structured, automated way to check the factual accuracy of LLM outputs. By providing a formal verification process, it aims to make LLMs more reliable for critical applications where accuracy is paramount, such as in scientific research, legal analysis, or financial advice.
What does this patent NOT cover?
Does not cover verifying LLM answers without translating them into first-order logic.
Same assignee
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