How a Computer System Checks and Improves AI Text

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.

What it covers

The patent describes a computer-implemented method to improve the output of a large language model (LLM). First, the LLM generates an initial response, called "first output" (Claim 1a). Then, a separate "processing system" steps in. This system uses a special, structured, machine-readable language where ideas are represented as "semantic nodes" and "semantic links" (Claim 1b). These nodes and links can represent specific meanings, reasoning steps, and even computation units. The processing system translates the LLM's initial output into this structured language and "verifies" it for accuracy and consistency using its internal knowledge (Claim 1c, 17). For example, it might check if facts are correct or if the logic makes sense. If issues are found, the processing system creates "second input data" which includes the verified or corrected information. This "second input data" is then sent back to the LLM, often as part of a new prompt, to guide the LLM to generate an "improved first output" (Claim 1d). This iterative process aims to make the LLM's final response more factually accurate, logically consistent, and less biased (Claims 5-8).

What it doesn't cover

• —Does not cover LLMs that improve their own output without an external "processing system" using a structured, machine-readable language.
• —Does not cover systems where the external processing system does not represent semantic nodes, semantic links, reasoning steps, and computation units in its machine-readable language.
• —Does not cover methods where the LLM's first output is not translated into the machine-readable language for verification.
• —Does not cover systems that only verify LLM output without feeding verified or corrected information back to the LLM to generate an improved output.
• —Does not cover basic prompt engineering or fine-tuning an LLM if it doesn't involve the specific verification and feedback loop with a structured knowledge system.
• —Does not cover using an external system that merely filters or edits LLM output without representing it in semantic nodes and feeding it back to the LLM.

The clever bit

The clever bit is using a separate, symbolic reasoning system, which understands structured, machine-readable knowledge and logic, to verify and correct the statistical, pattern-based output of an LLM. This combines the LLM's generative power with a robust, verifiable knowledge base, creating a feedback loop that improves accuracy and consistency rather than just filtering output.

Why it matters

Large language models sometimes "hallucinate" or generate factually incorrect or inconsistent information. This patent addresses a core problem in making LLMs reliable for critical applications. By introducing a structured, verifiable feedback loop, it aims to make LLM outputs trustworthy. This approach could be crucial for industries requiring high accuracy, such as scientific research, legal analysis, or financial reporting, where errors from AI could have significant consequences. It provides a mechanism to ground LLM responses in verifiable knowledge.

Real-world examples

• 1.AI assistants that provide fact-checked answers
• 2.Automated legal research tools verifying case summaries
• 3.Medical diagnostic aids cross-referencing patient data with established knowledge
• 4.Financial analysis tools validating market reports
• 5.Content generation platforms ensuring factual accuracy in articles