System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Through our AMVs, command-and-control systems, we log information about each AMV’s use, such as charge time, battery usage, mileage and location information, in order to aid us in vehicle diagnostics, repair and maintenance, as well as to help us customize and optimize the flying experience. Images and videos captured by cameras attached to our AMVs are stored on our servers, servers of third-party cloud storage providers or other servers designated by our customers. We, therefore, process, including but not limited to collect, store, process, use, transfer, provide, disclose and delete, personal data from our users in order to better understand our users and their needs for the purpose of our content feeds recommendation. Possession and use of our users’ flying behavior and data in conducting our business may subject us to legislative and regulatory oversight in Japan and other jurisdictions, such as the European Union and the United States. For example, in January 2018, the European Union promulgated the General Data Protection Regulation to further protect fundamental rights in privacy and personal information so that members of the general public have more control over their personal information. Regulations in relevant jurisdictions may require us to obtain user consent for the collection of personal information, restrict our use of such personal information and hinder our ability to expand our user base. In the event of a data breach or other unauthorized access to our user data, we may have obligations to notify users about the incident and we may need to provide some form of remedy for the individuals affected by the incident.
Assistant: