Intent Structuring Framework (ISF)

The Intent Structuring Framework (ISF) is a patented method for transforming free-form user inquiries into structured intent representations and autonomously clarifying missing information before generating deterministic, auditable responses.

Conventional FAQ and keyword-based systems treat user input as text patterns.
ISF instead models the structure of intent, enabling reliable guidance in domains where correctness, explainability, and consistency are essential.

ISF is registered as JP Patent 7774933 (2025), granted with no office actions.

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🧩 Core Concept

ISF decomposes any inquiry into two layers:

1. Main Intent Structure
   The core objective of the user’s request.

2. Supplementary Structures
   Conditions, constraints, modifiers, background context, and relational details.

This structure allows the system to identify whether necessary information is missing.
If requirements are incomplete, the system autonomously generates clarification questions instead of hallucinating or guessing.

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🔧 How ISF Works

1. Input Interpretation
   The system converts natural language input into a structured set of intent units.

2. Autonomous Clarification
   When required information is absent, ISF automatically issues clarification prompts.
   This ensures deterministic behavior even when using LLMs as the linguistic layer.

3. Template Composition Engine
   ISF selects and assembles modular templates according to the extracted intent structure.

4. Deterministic Output
   Each response is reproducible and explainable.
   The decision path can be inspected and audited at any time.

This architecture enables the use of LLMs in high-stakes public applications without sacrificing safety or consistency.

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📘 Patent Information

• Patent Number: JP 7774933
• Status: Granted (2025), no rejections issued
• Title: Intent Structuring Framework for Dialog Understanding and Template-Based Response Generation

The patent covers:

• Intent decomposition into structured layers
• Autonomous clarification logic
• Rule-based template extraction and assembly
• Mechanisms for ensuring transparency and auditability in dialog systems

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📄 Technical Summary (PDF)

A detailed technical overview is available here:

➡️ ISF_Technical_Summary_YTakahashi_2025-11.pdf

The document includes:

• System architecture
• Intent decomposition model
• Clarification process
• Template design principles
• Application scenarios in public and enterprise sectors

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🏛 Application Domains

ISF is designed for environments where hallucinations, inconsistent behavior, or ambiguous reasoning are unacceptable:

• Government service desks and municipal consultation systems
• Public-facing chatbots requiring auditability
• Financial advisory and compliance-sensitive workflows
• Customer support systems requiring consistent guidance
• Enterprise knowledge systems with large rulesets
• High-trust AI applications where deterministic responses are necessary

ISF supports multilingual queries and free-form expressions while maintaining structured, explainable outputs.

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🎯 Objectives of This Repository

This repository provides:

• The official ISF technical summary
• Patent documentation references
• Conceptual and architectural descriptions
• A foundation for collaborative research and PoC development with universities, government agencies, and industry partners

Future updates may include:

• Example templates
• Reference implementation concepts
• Extended clarification strategies

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📞 Contact

Yuji Takahashi
Independent Researcher
GitHub: https://github.com/optzen-lab
For collaboration or PoC inquiries, please reach out via LinkedIn or GitHub.

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ISF is published as an open research framework to support safe, transparent, and explainable AI development in public and enterprise domains.