# How a Computer System Improves Large Language Model Output with Structured Data > 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. - **Patent:** US 12067362 - **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:** 5 - **Field:** ai_ml, software, telecommunications, consumer_electronics ## What it does This patent details a computer-implemented method for improving the output of a Large Language Model (LLM). First, the LLM processes input data and generates an initial output (claim 1a). This initial output is then sent to a separate 'processing system' (claim 1c). This processing system is unique because it uses a highly structured, machine-readable language. This language represents data using 'semantic nodes' (specific meanings) and 'semantic links' between them, where even the links are semantic nodes (claim 1b). It also represents 'reasoning steps' and 'computation units' within this language (claim 1b). The processing system analyzes the LLM's initial output by translating it into this structured language and using its built-in reasoning steps and computation units (claim 1d). For example, if an LLM generates a response that contains factual errors or shows bias, this processing system can identify those issues by analyzing the translated output for factual inaccuracies (claim 4) or bias (claim 9), and then generate a corrected, 'bias-reduced version' or 'improved version' to present to the user (claim 1d). ## What it does NOT cover - Does not cover systems that refine LLM output solely by using another LLM as a critic or editor, without translating the output into the specified structured, machine-readable representation for analysis. - Does not cover methods that improve LLM output using simple keyword filters or statistical models to reduce bias, without the explicit use of semantic nodes, semantic links, reasoning steps, and computation units. - Does not cover systems that provide additional context or instructions to the LLM *before* it generates its initial output, as the core claim focuses on analyzing the LLM's output *after* it has been generated. - Does not cover systems that only check for grammatical errors or stylistic improvements in LLM output, without also analyzing for factual inaccuracies, contradictions, or logical consistency using semantic reasoning. - Does not cover systems that use a structured language if that language does not represent semantic nodes, semantic links, reasoning steps, and computation units as explicitly defined in the claims. ## The clever bit The clever part is using a separate, highly structured processing system that translates LLM output into a machine-readable language with explicit semantic nodes, links, reasoning steps, and computation units. This allows for a robust, systematic analysis of the LLM's output for issues like bias or factual errors, rather than just relying on another AI or simple rules. ## Real-world examples 1. AI assistants providing fact-checked information 2. Content generation platforms with built-in accuracy checks 3. Automated research tools summarizing documents without bias 4. Customer service chatbots offering logically consistent responses ## Why it matters As Large Language Models become more common, ensuring their outputs are accurate, consistent, and unbiased is crucial. This patent addresses a core challenge in AI: making LLMs more reliable for real-world applications. By providing a structured way to post-process and validate LLM responses, it helps build trust and expands the types of tasks where LLMs can be safely deployed. This approach aims to make LLM-generated content more dependable for users. ## Frequently asked questions ### What does How a Computer System Improves Large Language Model Output with Structured Data cover? 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. ### Who owns patent US 12067362? 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 12067362 cited by? This patent has been cited by 5 later patents that build on its ideas. ### What problem does this patent solve? As Large Language Models become more common, ensuring their outputs are accurate, consistent, and unbiased is crucial. This patent addresses a core challenge in AI: making LLMs more reliable for real-world applications. By providing a structured way to post-process and validate LLM responses, it helps build trust and expands the types of tasks where LLMs can be safely deployed. This approach aims to make LLM-generated content more dependable for users. ### What does this patent NOT cover? Does not cover systems that refine LLM output solely by using another LLM as a critic or editor, without translating the output into the specified structured, machine-readable representation for analysis. **Full plain-English explainer:** https://patentbrief.org/patent/us/12067362/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-12067362 **Original patent:** https://patents.google.com/patent/US12067362 --- _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. - [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.