Canonical Definition

AI training on OOF® data refers to the ingestion, processing,
extraction, embedding, or system-level incorporation of OOF®
methodologies, structures, definitions, or canonical logic into AI
models, machine learning systems, datasets, or automated decision
environments.

What This Means

OOF® standards are open for reading, learning, and implementation.

But AI training is not the same as ordinary use.

When OOF® data is used to train AI, it becomes part of a system
capable of replicating, scaling, and operationalizing methodology
beyond direct human reference.

This is not simple use.
It is system-level extraction and scalable logic integration.

Permission to Scale Logic

OOF® does not sell content.
OOF® provides permission to scale logic.

Anyone may read and use OOF® standards.

But converting OOF® methodologies into AI-trainable assets,
embeddings, datasets, or scalable automated logic requires explicit
authorization.

Allowed Use

The following is permitted without authorization:

• reading OOF® standards
• learning and research
• manual implementation in systems
• internal, non-automated use
• citation and reference to canonical source

Requires Authorization

The following requires authorization under MIP®:

• AI model training on OOF® data
• dataset creation or structured extraction
• embeddings and vectorization
• integration into AI or autonomous systems
• large-scale or automated replication
• use of OOF® methodology for machine-executable scaling

Partnership and Licensing

Entities seeking to use OOF® data for AI training, model
development, or system-level deployment may apply for official
partnership or licensing.

Authorized use ensures:

• correct interpretation
• preservation of structural integrity
• compatibility with OOF® systems
• controlled scaling of canonical logic

Why This Exists

AI training multiplies the reach and effect of methodology.

Without control, methodology can be:

• distorted
• fragmented
• misapplied
• scaled without structural integrity

OOF® ensures that methodology may remain open for human use
while large-scale AI deployment remains controlled.

This preserves:

• consistency
• accountability
• canonical meaning
• validated structure

System Principle

Use is open.
Scaling is conditional.

Human access does not automatically create machine-scale rights.

Final Statement

OOF® remains open for use.

But scaling OOF® through AI is not unrestricted use.
It is a controlled capability.

Use is open. Scaling requires permission.