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: During the preparation of our financial statements for the year ended June 30, 2023, we determined that we needed to restate our unaudited financial statements for the six months ended December 31, 2022 and the nine months ended March 31, 2023. In September 2022, certain intangible assets were transferred to us by a controlling stockholder. The value of the transferred assets was initially determined based on the fair value of the assets. Because the transfer was from a controlling stockholder, under GAAP, the transfer should have been recorded at the value on the books of the transferor and not at fair market value. In our unaudited condensed consolidated statements of changes in stockholders’ equity, we reflected the transfer of the intangible assets at the fair value of $74,259,915 rather than the carrying cost of nil. As a result of the restatement, our net loss for the six months ended December 31, 2022 decreased from $2,950,921, or $0.06 per share (basic and diluted), to $2,178,290, or $0.04 per share (basic and diluted) and our net loss for the nine months ended March 31, 2023 decreased from $6,057,776, or $0.12 per share (basic and diluted), to $4,512,513, or $0.09 per share (basic and diluted). The decrease in net loss reflects the reduced amortization of the intangible assets transferred from the controlling stockholder. On the March 31, 2023 balance sheet, (i) intangible assets decreased from $74,480,651 to nil. (ii) capital contribution decreased from $74,259,915 to nil and (iii) stockholders’ equity decreased from $79,953,608 to $7,238,957. Similar changes affected our financial statements at December 31, 2022 and for the six months ended December 31, 2022.
Assistant: