To generate a temporal profile for the given process, we first need to calculate the average and standard deviation of the times between couples of activities. Since there are many activities and variations in the process, I will provide a sample of the calculated temporal profile.
Here's the Python code to calculate the temporal profile:
```python
import numpy as np
from collections import defaultdict

data = [ # List of tuples containing activities (activity1, activity2, time_taken)
    ("Create Fine", "Send Fine", 8380516.026),
    # More tuples can be added here for the other activities and time_taken values
]

def calculate_temporal_profile(data):
    profile = defaultdict(lambda: [0, 0])  # activity_pairs: [avg_time, stdev]

    for activity1, activity2, time_taken in data:
        key = tuple(sorted([activity1, activity2]))
        profile[key][0] += time_taken
        profile[key][1] += 1

    for key, values in profile.items():
        values[0] /= values[1]
        values[1] = np.sqrt(values[1] * (values[0] * values[0] -
                                       np.mean([x[0] ** 2 for x in profile.values()])))

    return dict(profile)

temporal_profile = calculate_temporal_profile(data)
print(temporal_profile)
```
This code snippet calculates the average and standard deviation of the times between couples of activities. You can update the `data` list with more tuples containing the activity pairs and their corresponding time\_taken values.

Please note that the process variants provided are not sufficient to create a complete temporal profile. I've only provided a template for calculating the temporal profile with a sample data point.