| CPC H04L 65/80 (2013.01) [H04L 41/147 (2013.01); H04L 65/61 (2022.05)] | 20 Claims |

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1. A method comprising:
determining training data from first nearline features for a first sliding time window, wherein the first sliding time window changes during a first time period;
training a prediction model using the training data, wherein the prediction model is trained for a second time period that is within the first time period;
determining first values for second nearline features for a second sliding time window and second values for the second nearline features for a fixed time window for a request for a current session that is received during the second time period, wherein:
the second sliding time window is based on a time for the request and changes within the first time period based on different times of requests that are received during the second time period, and
the fixed time window is static during the first time period and used for requests that are received during the second time period;
inputting the first values and the second values for the second nearline features into the prediction model to generate a prediction; and
performing an action for the current session based on the prediction.
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