Your task is to aggregate the features extracted from the multiple trajectories.

The features extracted from the multiple trajectories are given to you:
${potential_features}

The schema of the features is given to you:
${features_schema}

You should look at the features and decide if they are relevant to the classification task.

Remove features that are not relevant to the classification task, or are unobtainable using a trajectory that stops before the classification task.

Output Format:
{
  "filtered_features": [
    {
      "feature_name": "descriptive name",
      "feature_type": "text/numeric/categorical/computed",
      "description": "what this feature represents",
      "extraction_source": "where in the data this comes from",
      "computation": "how to compute this feature",
      "classification_relevance": "how this helps with the classification task",
      "rationale": "why this would be useful",
    }
  ],
  "rejected_features": [
    {
      "feature_name": "descriptive name",
      "feature_type": "text/numeric/categorical/computed",
      "description": "what this feature represents",
      "extraction_source": "where in the data this comes from",
      "computation": "how to compute this feature",
      "classification_relevance": "how this helps with the classification task",
      "rationale": "why this was rejected",
    }
  ]
}
