Neuro-Symbolic AI for Summary Proceeding Automation

Processing high-volume minor traffic offenses—such as simple DUIs, unlicensed driving, and minor traffic casualties—creates a significant administrative burden for prosecutors and judicial personnel. While standard Large Language Models (LLMs) are effective at processing natural language text, they rely on probabilistic predictions and can produce errors (“hallucinations”) when calculating fine ranges or applying explicit statutory rules. In legal contexts, where accuracy and accountability are critical, relying solely on probabilistic models presents challenges.

To address these limitations, our framework uses a Neuro-Symbolic AI approach. By separating responsibilities—using LLMs for natural language parsing and structured fact extraction alongside a formal logic engine for rule-based calculations—we support reliable sentencing estimations and automated document drafting.

Core Technologies & Intellectual Property (IP)

Our solutions are based on original research and patent-pending architectures:

• Neuro-Symbolic Framework for Legal Reasoning & Document Synthesis
  • The Tech: Combines LLMs for grounded text extraction with an SMT formal logic engine to compute fine ranges according to statutory rules and generate verifiable prosecutorial documents.
  • Status: Korean Patent Application Filed (Provisional Specification)
  • Publication: “Neuro-Symbolic AI for Korean Criminal Law: Sentencing Prediction and Document Drafting” (Preprint DOI: 10.48550/arXiv.2607.19740)

How The Solution Works

Our system transforms raw legal records into verified summary indictment documents through a structured five-step pipeline: