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.
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 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. Granted to Unlikely Artificial Intelligence in 2024 with 29 claims and 5 forward citations, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
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 (claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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).
The gap
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.
- 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 claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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 claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
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 fact-checked information
Content generation platforms with built-in accuracy checks
Automated research tools summarizing documents without bias
Customer service chatbots offering logically consistent responses
Why it matters
The bigger picture
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.
Filed
April 17, 2023
Granted
August 20, 2024
Market context
Who's building on this
Companies in this space
Unlikely Artificial Intelligence Ltd, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing technologies in this space. Other companies focusing on AI safety, fact-checking, and robust deployment of Large Language Models are also working on similar problems. This includes major cloud providers offering LLM services and startups building validation layers for AI applications.
Market impact
This patent addresses a critical need for more trustworthy AI outputs, which could enable wider adoption of LLMs in sensitive industries like finance, healthcare, and legal services. It could lead to the development of new software tools and services focused on validating and refining AI-generated content. The technology described could become a standard component in enterprise-grade LLM deployments, helping to mitigate risks associated with AI hallucinations and biases.
Claim 1 — Plain English
What this patent covers
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).
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.
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.
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
16/40
Early citations
Claim breadth
19/20
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
$94K – $300K
Midpoint $187K · 16.6 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
29 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. (2024). How a Computer System Improves Large Language Model Output with Structured Data (U.S. Patent No. 12,067,362). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12067362/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including-12067362
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Common Questions
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.
Same assignee
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