"""Create error_monitor
Generated by Phi-Octopus Eden
2025-11-07 05:34:49.708297
"""

```python
import logging

def create_error_monitor(function):
    """
    A decorator that wraps the input function and monitors for errors during execution.
    If an error occurs, it will attempt to recover by either retrying or notifying a support team,
    based on the nature of the exception.

    Args:
        function (Callable): The function to wrap with error monitoring.

    Returns:
        Callable: The wrapped function with enhanced error handling capabilities.
    """
    def wrapper(*args, **kwargs):
        try:
            return function(*args, **kwargs)
        except Exception as e:
            # Log the exception
            logging.error(f"An error occurred in {function.__name__}: {e}", exc_info=True)

            # Determine if we should retry or notify support based on the type of error
            if isinstance(e, (ConnectionError, TimeoutError)):
                logging.info("Retrying operation...")
                return function(*args, **kwargs)
            else:
                # Notify a support team via email/sms etc.
                notify_support(function.__name__, e)
                raise

    return wrapper


def notify_support(func_name: str, error: Exception):
    """
    Simulate notifying the support team about an error in a specific function.

    Args:
        func_name (str): The name of the function where the error occurred.
        error (Exception): The exception that was caught during execution.
    """
    logging.info(f"Support notified about error in {func_name}: {error}")


@create_error_monitor
def process_data(data: list) -> dict:
    """
    Processes a list of data points and returns a summary.

    Args:
        data (list): A list of numeric values to be processed.

    Returns:
        dict: Summary statistics including count, mean, min, max.
    """
    if not data:
        return {}

    summary = {
        'count': len(data),
        'mean': sum(data) / len(data),
        'min': min(data),
        'max': max(data)
    }
    return summary


# Example usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    try:
        print(process_data([1, 2, 3, 4]))
        # Introduce an error by passing a non-numeric value to simulate an exception
        print(process_data(['a', 'b', 3, 4]))  # This should trigger the error monitoring
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}", exc_info=True)
```