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.
Original patent title: “Computer implemented methods for the automated analysis or use of data, including use of a large language model”
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. Granted to Unlikely Artificial Intelligence in 2025 with 32 claims, and it is expected to expire in 2044.
Key facts
Coverage
What does this patent actually cover?
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.
The gap
What does this patent 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
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providing it with curated, fact-checked context rather than letting it rely solely on its training data, which can be outdated or biased.
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
Customer service chatbots that provide accurate product information.
AI assistants that generate factual reports or summaries.
Tools that help researchers verify AI-generated hypotheses.
Systems for generating educational content with high factual integrity.
Why it matters
The bigger picture
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.
Filed
October 23, 2024
Granted
July 8, 2025
Market context
Who's building on this
Companies in this space
Companies developing enterprise AI solutions, such as Microsoft, Google, and OpenAI, are actively working on methods to improve LLM factuality and reliability. Startups focused on AI safety and responsible AI deployment are also exploring similar hybrid approaches.
Market impact
This patent could influence the development of more trustworthy AI applications by encouraging hybrid architectures that combine the generative power of LLMs with the accuracy of symbolic AI. It may lead to stricter standards for AI-generated content in fields like finance, healthcare, and journalism.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
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
0/40
No citations yet
Claim breadth
20/20
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
$58K – $184K
Midpoint $115K · 18.4 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.
The original legal language
Original claims
32 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Heywood, R., BENN, P., REYNOLDS, D., Zhu, Z., WARREN, S., Shah, A., Tunstall-Pedoe, W., & KRNIC, L. (2025). Using Non-AI Systems to Improve AI Text Generation (U.S. Patent No. 12,353,827). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including
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 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.
Patent monitoring