# How a Computer System Fact-Checks AI Language Models > 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. - **Patent:** US 12073180 - **Original title:** Computer implemented methods for the automated analysis or use of data, including use of a large language model - **Owner:** Unlikely Artificial Intelligence - **Granted:** 2024 - **Status:** Active - **Times cited:** 12 - **Field:** ai_ml, software, telecommunications, consumer_electronics ## What it does The patent describes a computer-implemented method for fact-checking the output of a large language model (LLM). First, the LLM processes an initial prompt to generate text, referred to as "first output" (claim 1a). This "first output" is then provided to a separate "processing system" (claim 1c). This processing system uses a special "structured, machine-readable representation of data" that conforms to a "machine-readable language" (claim 1b). This language represents specific meanings as "semantic nodes" and includes "semantic links" between them, along with "reasoning steps" and "computation units" (claim 1b). The processing system translates factual assertions from the LLM's output into this structured language (claim 2) and then analyzes it for factual inaccuracies (claim 3), contradictions (claim 4), and even bias (claim 13). Using its built-in reasoning steps and computation units, it generates a "second output," which is a fact-checked and improved version of the original text, and provides it to the user (claim 1d). For example, if an LLM incorrectly states "The capital of France is Berlin," the processing system would translate this assertion, check it against its structured knowledge, identify the inaccuracy, and then provide the corrected information "The capital of France is Paris" to the user. ## What it does NOT cover - Does not cover LLMs that fact-check themselves without using a separate processing system with a structured, machine-readable language. - Does not cover fact-checking methods that do not translate the LLM's output into a specific structured, machine-readable representation with semantic nodes and links. - Does not cover systems where the fact-checking process does not involve "reasoning steps" and "computation units" represented in the machine-readable language. - Does not cover simply comparing LLM output to a database without the semantic analysis described in the claims. ## The clever bit The novelty lies in using a *separate* processing system that translates the LLM's free-form text into a highly structured, machine-readable language with defined semantic nodes, links, reasoning steps, and computation units to perform rigorous, automated fact-checking, rather than relying on the LLM itself to self-correct. ## Real-world examples 1. AI assistants providing factual summaries 2. Content generation tools requiring high accuracy 3. Automated legal document review systems 4. Medical information systems ## Why it matters Large language models sometimes generate factually incorrect information, a phenomenon often called "hallucination." This patent addresses a critical problem by providing a mechanism to automatically verify and improve the accuracy of LLM outputs. This is important for applications where factual correctness is essential, such as in scientific research, legal analysis, or news generation, helping to build trust in AI systems. ## Frequently asked questions ### What does How a Computer System Fact-Checks AI Language Models cover? 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. ### Who owns patent US 12073180? Unlikely Artificial Intelligence owns this patent, granted in 2024. ### When does this patent expire? This patent is expected to expire on April 17, 2043, when the invention enters the public domain. ### What is patent US 12073180 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 factually incorrect information, a phenomenon often called "hallucination." This patent addresses a critical problem by providing a mechanism to automatically verify and improve the accuracy of LLM outputs. This is important for applications where factual correctness is essential, such as in scientific research, legal analysis, or news generation, helping to build trust in AI systems. ### What does this patent NOT cover? Does not cover LLMs that fact-check themselves without using a separate processing system with a structured, machine-readable language. **Full plain-English explainer:** https://patentbrief.org/patent/us/12073180/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-12073180 **Original patent:** https://patents.google.com/patent/US12073180 --- _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 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 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. - [Using Non-AI Systems to Improve AI Text Generation](https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including) — This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking. - [How AI Checks Documents for Policy Rules and Compliance](https://patentbrief.org/patent/us/12299406/large-language-model-artificial-intelligence-text-evaluation-system) — This patent describes a system where an AI model helps evaluate parts of a document against specific rules or policies given in plain language, then identifies problems or suggests fixes to ensure compliance. - [How an AI System Answers Financial Questions Using Specialized Bots](https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi) — This patent describes an AI system where a large language model (LLM) directs specialized machine learning agents to answer natural language questions about financial data, then refines the answers using an adversarial AI.