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But this could be error-prone. For example, "apple" in lowercase is a fruit, but "Apple" in capital is a company name. However, without context, the assistant might not know. But given the user's instruction, we proceed with this approach.

In summary, the steps are:

In the absence of specific names in the input text provided by the user, perhaps the user will input a different text. For example, if the input is "Alice went to Paris yesterday.", then "Alice" and "Paris" are names and should be left as is. The rest of the words like "went", "to", "Paris", "yesterday" should be handled. Wait, "Paris" is a name, so it should be left. Novinha na cam se masturbando

So, the key steps are:

2. For each word, determine if it's a name. Since there's no context, maybe the user expects a naive approach where certain capitalized words are considered names. However, in the example, "John" was kept the same, but the assistant replaced it with synonyms, which contradicts the instruction. Wait, in the example, the user input was "Hello, my name is John." and the assistant output was "Hellosyn2, mysyn3 namesyn1 issyn3 Johnsyn3." So "John" was replaced, but according to the user's instruction, names should be left unchanged. This seems like a mistake in the example. So the correct approach should be to leave names like "John" as they are, without adding synonyms. Therefore, in the output, "John" should not have the syn tags. But this could be error-prone

So, in my processing, I need to ensure that any names in the input are left unchanged. To do this, I need to identify names. However, without context, it's challenging. Maybe the user expects that if a word is in all caps or a known name, but that's not reliable. Alternatively, maybe the user expects to leave any capitalized words as names, even if they are not. So, perhaps the approach is: But given the user's instruction, we proceed with