> ## Documentation Index
> Fetch the complete documentation index at: https://docs.syncline.run/llms.txt
> Use this file to discover all available pages before exploring further.
# POST /v1/meetings/{id}/reschedule
> Reschedule an existing meeting with AI learning
## Overview
The reschedule endpoint moves an existing meeting to a new time. Crucially, it records **why** the meeting was rescheduled so the AI can learn and avoid similar issues in the future.
## Authentication
Requires **Platform API Key** in the `X-API-Key` header.
## Request
```bash theme={null}
POST https://api.syncline.run/v1/meetings/{meeting_id}/reschedule
```
### Path Parameters
The meeting ID returned from the original schedule call
### Request Body
Same as path parameter (for consistency)
Email of person requesting the reschedule
Why the meeting is being rescheduled. The AI learns from this!
Example reasons:
* "Too early in the morning"
* "Too late in the day"
* "Prefer to avoid Fridays"
* "Need more preparation time"
* "Conflicts with another meeting"
Optional preferred date range for new meeting
```json theme={null}
{
"start": "2025-01-25T00:00:00Z",
"end": "2025-01-27T00:00:00Z"
}
```
### Example Request
```json theme={null}
{
"meeting_id": "evt_7d8f9e3a2b1c",
"requested_by": "alice@example.com",
"reason": "Too early in the morning - prefer afternoon slots"
}
```
## Response
### Success Response (200 OK)
```json theme={null}
{
"success": true,
"new_meeting_id": "new_evt_7d8f9e3a2b1c",
"new_meeting_time": "2025-01-23T14:00:00Z",
"reasoning": "System learned from reschedule reason and selected new optimal time",
"learned_from": "User prefers later times - future bookings will avoid early morning slots"
}
```
### Response Fields
Whether reschedule succeeded
ID of the newly scheduled meeting
ISO 8601 timestamp of new meeting
AI's explanation for the new time choice
What the system learned from this reschedule to improve future scheduling
## How AI Learning Works
When you reschedule with a reason, the system:
### 1. Pattern Recognition
Analyzes the reason text for patterns:
* "too early" → Deprioritizes morning slots for this user
* "too late" → Deprioritizes evening slots
* "Friday" → Reduces Friday slot scores
* "back-to-back" → Increases buffer time importance
### 2. Score Adjustment
Updates the user's SmartWeights:
```javascript theme={null}
{
"time_of_day_preference": {
"morning": 0.6, // Reduced from reschedule
"afternoon": 1.2, // Increased
"evening": 0.8
},
"day_of_week_preference": {
"friday": 0.7 // Reduced if Friday mentioned
}
}
```
### 3. Future Application
Next time this user needs a meeting:
* Morning slots get lower scores
* Afternoon slots get boosted
* AI naturally gravitates toward preferred times
## Example: Learning Loop
```javascript theme={null}
// First meeting: AI picked 9am (high score at the time)
const meeting1 = await scheduleAuto({
attendees: ['alice@example.com', 'bob@example.com'],
title: 'Sync'
});
// Scheduled for 9:00am
// User reschedules with reason
await reschedule({
meeting_id: meeting1.meeting_id,
requested_by: 'alice@example.com',
reason: 'Too early - I prefer afternoons'
});
// → System learns: Alice prefers afternoons
// Next meeting: AI now prioritizes afternoon
const meeting2 = await scheduleAuto({
attendees: ['alice@example.com', 'charlie@example.com'],
title: 'Another Sync'
});
// Scheduled for 2:00pm ✓ (learned preference)
```
## Best Practices
### Provide Detailed Reasons
Bad: `"Conflict"`
Good: `"Too early in the morning - I'm most focused after 11am"`
The more detail in the reason, the better the AI can learn.
### Consistent Language
If your users reschedule for similar reasons, use consistent phrasing:
* "Too early in the morning"
* "Too late in the afternoon"
* "Prefer to avoid Mondays"
This helps pattern recognition work better.
### Track Learning Outcomes
Monitor the `learned_from` field to see what patterns the AI is picking up. Share this with users so they understand their preferences are being learned.
## Related Endpoints
Schedule with AI learning
Cancel a meeting