1. Overview

OdorMind™ introduces a unified odor data format designed for sharing across research, industry, and AI-driven robotics. Just as JSON for structured text and JPEG for images became universal standards, odors also require a structured digital format for storage, analysis, and sharing.

Critically, odor is deeply tied to molecular structure. Without a standard way to represent and exchange 3D molecular data (e.g., PDB, MOL, SDF formats), AI and robots cannot fully replicate or compare scent-related knowledge across different platforms.

2. Why Standardization?

Robots and AI systems that rely on olfactory sensors require not only raw data but also structured metadata: perception values, cultural associations, and safety guidelines. A standard format ensures:

• Consistency across sensors, labs, and datasets
• Interoperability between different AI models
• Reliable communication for robotics, healthcare, and food industries
• Integration into global data ecosystems

3. Core Structure

Here is an example JSON representation of an odor record:

{
  "odor_id": 115,
  "unique_id": "ODOR-000115",
  "version": "0.2",
  "name": "Black tea",
  "description": "Complex aroma derived from the oxidation of tea leaves.",
  "main_compounds": ["Theaflavins", "Terpenes"],
  "odor_category": "Complex",
  "danger_level": 1,
  "tags": ["tannin", "floral"],
  "created_at": "2025-08-12T21:06:35.415542-05:00",
  "last_updated": "2025-08-16T09:22:00-05:00",

  "metadata": {
    "source": "PubChem / AI Odor Classifier",
    "confidence_score": 0.92
  },

  "measurement": {
    "concentration_ppm": 0.8,
    "threshold_ppb": 50,
    "half_life_hours": 3.2
  },

  "perception": {
    "perceived_intensity": 6,
    "pleasantness": 4,
    "cultural_notes": ["comforting", "ritual beverage"],
    "robotic_use_case": ["food quality check", "flavor profiling"]
  },

  "health_safety": {
    "toxicity_class": "Non-toxic",
    "exposure_limit_ppm": 200,
    "protective_measures": ["none required"]
  },

  "ai_integration": {
    "embedding_vector": [0.123, -0.045, 0.982, "..."],
    "classification_label": "floral",
    "training_data_refs": ["OdorMind-v1", "PubChem-2025"]
  },

  "molecular_structure": {
    "format": "SDF",
    "file_url": "https://odormind.com/data/structures/blacktea.sdf"
  },

  "image_url": "https://odormind.com/static/assets/img/odormind-high-resolution-logo-grayscale-transparent.png"
}

4. Extended Fields

Beyond the core structure, fields like measurement, perception, and health_safety allow for richer analysis and safe application in robotics, healthcare, and consumer products.

5. AI & Robotics Integration

OdorMind’s standardized format allows:

• Training AI models on consistent embeddings
• Deploying robots that can detect, classify, and respond to odors
• Cross-domain applications: food safety, medical diagnosis, environmental monitoring

By unifying odor data, we are paving the way for a global “smell internet”, enabling robots and AI systems to exchange scent-based knowledge just as easily as text or images.

6. 3D Molecular Structures

Every odor is ultimately defined by the 3D arrangement of its molecules. To ensure interoperability, OdorMind recommends:

• Using established chemical file standards (PDB, MOL, SDF)
• Embedding molecular metadata (bond angles, charge, conformations)
• Providing direct file links alongside visualizations (via 3Dmol.js)
• Mapping odor perception values directly to molecular structures for AI learning

By standardizing both metadata and 3D molecular geometry, robots and AI systems can not only detect odors but also simulate, compare, and predict scent behaviors in a reproducible way.