5.7 – Provisional Schema Extensions (schema-layer: "temp")

Sometimes an AI agent must describe new or unregistered event types or statuses in real time — especially when surfacing official updates, community initiatives, or national observances that haven’t yet been formalized in the core schema.

These provisional extensions can be broadcast using a schema-layer: "temp" block, allowing agents and platforms to adapt to live inputs while preserving forward compatibility.

🧾 Example: Government Announces a New Bank Holiday

{
  "schema-layer": {
    "temp": {
      "semantic-event-type": "govnotice",
      "semantic-event-status": "bank-holiday-declared",
      "trigger-source": "https://www.gov.uk/government/news/new-bank-holiday-june-27th",
      "context-tags": ["public-announcement", "calendar", "civic-life"]
    }
  }
}

🔍 Field Reference

This pattern supports future-facing flexibility — ensuring your platform stays responsive to emerging semantics from trusted sources like governments, institutions, or news APIs, without disrupting core validation flows.

6 - Use Cases

6.1 Personal & Social Applications

• On-Device Memory Journaling
  Store each MetARverse URI in a local SQLite timeline to power a “smart journal” that reconstructs your day on demand.
• Ephemeral LLM-Driven Recommendations
  Feed the most recent URIs into a transient chat prompt—without ever uploading your full history—to surface personalized tips and reminders.

6.2 AI & HCI

• Context-Aware Assistants
  Local URI history gives AI agents instant recall of what you’ve done, felt, and where you’ve been—without leaking private data.
• Adaptive UX via Temporary Chat
  Bundle only the last N events into an LLM prompt for real-time interface adjustments or conversational enhancements.

6.3 Healthcare & Well-Being

• Private Mental Health Tracking
  Persist emotion URIs (feeling/anxious, feeling/happy) locally and periodically send a rolling window to an on-device LLM for mood insights.
• Preventative Care Signals
  Analyze recent patterns of “item/damaged” or “machine/warning” URIs in-app to trigger health or safety check-ins before issues escalate.

6.4 Scientific & Sociological Research

• Anonymized Local Event Logs
  Researchers can work with de-identified URI snapshots stored on participants’ devices—then request short, aggregated windows for analysis.
• Ephemeral Data Sharing
  Only the necessary URI segments are POSTed as a temporary chat to an LLM, preserving privacy while enabling cultural-behavior studies.

6.5 Market & Commercial Use Cases

• Venue Sentiment Dashboards
  Collect “feeling” URIs in a local store on staff tablets; send rolling batches to a private LLM for live crowd-mood insights.
• AR Memory Pop-Ups
  Let visitors load their own URI history in a venue app to replay personalized AR highlights—no central server needed.
• Mood-Triggered Promotions
  Match “feeling/deflated” or “feeling/happy” URIs against offer rules in-app and push coupons via a transient LLM chat, ensuring minimal data exposure.

7 Memory-Bucket Recall Template

When a user requests to recall a specific memory bucket (e.g., "Greek Holiday 2023"), the system should construct the LLM prompt as follows:

• <MEMORY_BUCKET_NAME>: The identifier for the memory bucket, e.g., Greek Holiday 2023.
• <Memory entry X>: Individual memory records retrieved for that bucket, ordered as needed (chronologically, by relevance, etc.).
• <USER_QUESTION>: The user’s follow-up query, which the LLM must answer using only the provided bucket entries.

Integration Notes:

• Fetch and surface only the entries tagged with the specified bucket ID from your memory store.
• Prepend this block to the LLM system prompt so that no other memory content is visible to the model.
• This ensures laser-focused context and eliminates noise from unrelated memories.

8 - Conclusion

MetARverse transforms digital interaction by weaving emotional depth, spatial context, and memory continuity into a shared open standard. By uniting humans, AI agents, and physical environments under one semantic framework, we pave the way for a more empathetic, actionable, and intelligent Web3 future.

References

1. IEEE 2874-2025 Spatial Web Standard (2025)
2. W3C DID Core, v1.0 Recommendation (2023)
3. Schema.org Vocabulary (ongoing)
4. Open311 GeoReport v2 Spec (2016)
5. Uber H3: A Hexagonal Hierarchical Spatial Index, https://h3geo.org (accessed 2025)

Appendix - MetARverse URI Regex Pattern

(?x)                                      # free-spacing
^
metarverse/                               # literal prefix

# — memory-bucket (UUID)
(?P[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})/

# — actor DID or agent DID
(?P
   user-[a-z0-9\-]+-did:[A-Za-z0-9]{8,64}
  |ai-agent-[a-z0-9\-]+-version-\d+\.\d+\.\d+
)/

# — reporter DID or agent DID
(?P
   user-[a-z0-9\-]+-did:[A-Za-z0-9]{8,64}
  |ai-agent-[a-z0-9\-]+-version-\d+\.\d+\.\d+
)/

# — semantic-event-type
(?Pfeeling|item|flight|machine)/

# — semantic-event-status
(?P
     lonely|happy|anxious
    |damaged|lost|found
    |landed|delayed|boarding
    |warning|offline|maintenance
)/

# — intensity, arousal (unsigned decimals)
(?P-?\d+(?:\.\d+)?)/

(?P-?\d+(?:\.\d+)?)/

# — datetime (ISO-8601 extended)
(?P
  \d{4}-\d{2}-\d{2}T        # date
  \d{2}:\d{2}:\d{2}         # time
  (?:\.\d{1,3})?            # optional ms
  Z                         # UTC
)

# — anchor-type 
(?Phuman-body|robot|postcode|h3|latlon|hsid)/

# — anchor-id (format varies by type)
(?P[A-Za-z0-9\-._~,%]+)

# — optional entity path
(?:
  /
  (?P[A-Za-z0-9\-/_]+)
)?$