# Using Non-AI Systems to Improve AI Text Generation > 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. - **Patent:** US 12353827 - **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:** 2025 - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, software, telecommunications ## What it does This patent outlines a system where a non-LLM data processing system, which uses symbolic representations (like rules and facts), works alongside a Large Language Model (LLM). The non-LLM system takes an initial input, enhances it by accessing external knowledge sources, and then feeds this improved information to the LLM as a prompt. The LLM then uses this enhanced prompt to generate text that is more factually accurate, internally consistent, and aligned with real-world understanding. For example, if you ask an LLM about a historical event, the non-LLM system could first pull verified dates and facts from a database and provide them to the LLM, ensuring the LLM's response is grounded in reality. ## What it does NOT cover - Methods where the LLM directly accesses external knowledge sources without an intermediary non-LLM system. - Systems that rely solely on statistical methods for data processing, without symbolic representations. - LLM outputs that are not fact-checked or verified for accuracy. - Methods where the non-LLM system does not enhance or augment the input before providing it to the LLM. - LLM-based systems that operate without any external data processing system. ## The clever bit The innovation lies in using a separate, symbolic reasoning system to act as an intelligent agent for the LLM, providing it with curated, fact-checked context rather than letting it rely solely on its training data, which can be outdated or biased. ## Real-world examples 1. Customer service chatbots that provide accurate product information. 2. AI assistants that generate factual reports or summaries. 3. Tools that help researchers verify AI-generated hypotheses. 4. Systems for generating educational content with high factual integrity. ## Why it matters As LLMs become more powerful, ensuring their outputs are accurate and reliable is crucial for many applications. This patent addresses the common problem of LLMs 'hallucinating' or generating incorrect information by introducing a structured way to guide their responses using more traditional, verifiable data processing. ## Frequently asked questions ### What does Using Non-AI Systems to Improve AI Text Generation cover? 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. ### Who owns patent US 12353827? Unlikely Artificial Intelligence owns this patent, granted in 2025. ### When does this patent expire? This patent is expected to expire on October 23, 2044, when the invention enters the public domain. ### What problem does this patent solve? As LLMs become more powerful, ensuring their outputs are accurate and reliable is crucial for many applications. This patent addresses the common problem of LLMs 'hallucinating' or generating incorrect information by introducing a structured way to guide their responses using more traditional, verifiable data processing. ### What does this patent NOT cover? Methods where the LLM directly accesses external knowledge sources without an intermediary non-LLM system. **Full plain-English explainer:** https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including **Original patent:** https://patents.google.com/patent/US12353827 --- _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 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 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. - [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.