Time series classification, without the time dimension Edit
Thanks to the answer of @pcko1, I understand that I should use data augmentation to make my model resilient to order of data points.
Clarification after the answer of @Icrmorin :
My problem is actually more complex than just finding bullets. I also need to find title, order text appropriately (think 2-columns PDF), find header/footer, etc...
I am currently trying a rule-based approach, and it was working well so far. But as the number of PDF format I need to handle grow, the complexity of the code grow as well, and I'm reaching a point where PDF formats have opposite features and can't be handled by the same code.
I was hoping the machine learning approach might solve this issue and works well for any format.
According to the answer of @mariq vlahova, it seems there is no name for this kind of task ? Just use LSTM ?
end of edit

I'm looking for the name of a task, in order to search more literature about the subject.
The best I can describe this task is given in the title itself...
Basically I have data points with several features each, and I need to classify each of these data points, but not independently. However the order does not (or should not) matter.

Example
For example I have 3 data points [D1, D2, D3], and I want to classify is as [True, False, False].
These data points are dependent, ie changing 1 data point might alter the result of other as well :
[D1, D2', D3] might be classified as [True, True, True].
Also, the order does not matter (that's why I wrote &quot;without the time dimension&quot;):
If [D1, D2, D3] is classified as [True, False, False],
then [D2, D3, D1] should be classified as [False, False, True].

Context
A bit more context... Basically I want to classify PDF content, as being a bullet point or not.
So I want to parse my PDF file, extract chunks of text along with additional information (font size, position, etc...), and classify these chunks as bullet or not bullet.
But we can't classify each chunk independently. Consider the following example :
...End of previous paragraph

1. This is a title

Beginning of next paragraph...

In this case, 1. This is a title should not be classified as bullet. But :
1. This is a title

2. This is a second title

3. This is a third title

In that case, 1. This is a title should be classified as bullet.
I need to find literature about this problem but I don't even know the name of the task...
Honestly it seems you are quite far from what would need a supervised vision approach. I suggest you to try a simple non-ML approach first : extract text with a standard library then just label what would count as a 'bullet' then check if there is more than one in a row. This might just work and if it doesn't it will help you understand why.
Going the whole OCR way would imply a lot of pdf annotation for a gain that is not really clear without trying a simple benchmark.
Ps: if you want to deal with text, the relevant field is Natural Langage Processing not time-series.