# Common Use Cases

### Document Timestamping

One of the most straightforward applications is **document timestamping**—creating cryptographic proof that a document existed at a specific time without revealing its contents.

#### Process:

1. Generate SHA-256 hash of document
2. Embed hash in OP\_RETURN output
3. Once mined, the hash has immutable timestamp from block
4. Future verification: hash document and compare to blockchain record

#### Use cases:

* Legal evidence and prior art for patents
* Academic integrity for research papers
* Intellectual property timestamp proof
* Regulatory compliance records

**Example transaction structure:**

```
Output 0: Payment to recipient (standard P2PKH)
Output 1: OP_0 OP_RETURN <protocol_id> <document_hash> <metadata>
Output 2: Change back to sender
```

<figure><img src="/files/dtb3nszqz9tVWjjirc1V" alt=""><figcaption></figcaption></figure>

### Token Protocols

OP\_RETURN serves as the foundation for token protocols that enable issuing and transferring digital assets on Bitcoin. Token protocols use False Return outputs to encode **state transitions**—the metadata defining how tokens move between addresses.

#### Example token transfer:

Token-aware wallets read the OP\_RETURN **metadata to track balances**. The actual B**itcoin value (satoshis) in outputs is minimal**—just enough for **validity and fees**.

#### Token applications:

* Securities and equity representation
* Supply chain tracking
* Loyalty points and rewards
* Gaming assets and collectibles
* Stablecoins pegged to fiat

### Data Anchoring

Data anchoring **stores compact cryptographic commitments to large datasets**, enabling **verification without storing everything on-chain**. The most common approach uses Merkle trees:

#### Merkle Root Anchoring:

1. Organize large dataset as Merkle tree leaves
2. Compute single Merkle root (32-byte hash)
3. Store root in False Return output
4. Provide Merkle proofs to verify individual records

This pattern allows a company processing millions of daily transactions to anchor one Merkle root per batch, while users can verify individual transactions with compact proofs.

**Applications:**

* Payment processors batching transactions
* IoT networks committing sensor readings
* Healthcare systems proving record integrity
* Government auditable public records

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