SESSION TYPE CLASSIFICATION FOR MODELING

Number: 20260205660
Published: 2026-07-16
Filed: 2026-02-27
Assignee: Disney Enterprises, Inc.
Inventors: Gallusser; Fabian
CPC: H04N21/4532; H04N21/4665
Verdict: Set aside cpc prior 0.09
In edition: 2026-W29
Source: Google Patents · FreePatentsOnline

The keeper's note

a method determines a number of session types.

Abstract

In some embodiments, a method determines a number of session types. Training data from sessions is analyzed to cluster the data in the number of session types. The method trains a model using the number of session types. Feature values for the session types are input into the model. The model generates an output based on the feature values. The output is compared to a reference output for the feature values. The method alters the number of session types based on comparing.

Background

BACKGROUND

A company may generate predictions from information related to user data. For example, a company may provide a service, such as a video delivery service in which users playback content, and the company may want to generate different predictions, such as whether the user account may become disengaged with the service, upgrade the service, increase or decrease engagement, watch a type of video, etc. In some examples, a company may use a prediction network that can generate the predictions. A typical method may be to determine values for features based on the user account that is using the service. The values are input into a model that is implemented by the prediction network to generate a prediction.

Sometimes, more features are added in an attempt to generate a more accurate prediction. For example, some features may be the result of coarse feature aggregation, such as total hours streamed in the past month, the number of items added to a watchlist for later viewing since signup, etc. These highly aggregated numbers may fail to capture true user account level engagement and interactions with the service. Also, as more features are added, the model may not be able to sensibly analyze all the features to determine a prediction that makes sense, essentially leading to overfitting where the model fits too closely against the training data used for the features, but not for new data in which a prediction is being determined. Also, once the prediction is re

Claims

1. A method comprising: determining a number of session types; analyzing training data from sessions to cluster the data in the number of session types; training a model using the number of session types, wherein feature values for the session types are input into the model, and the model generates an output based on the feature values; comparing the output to a reference output for the feature values; and altering the number of session types based on comparing. || 19. A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for: determining a number of session types; analyzing training data from sessions to cluster the data in the number of session types; training a model using the number of session types, wherein feature values for the session types are input into the model, and the model generates an output based on the feature values; comparing the output to a reference output for the feature values; and altering the number of session types based on comparing. || 20. An apparatus comprising: one or more computer processors; and a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for: determining a number of session types; analyzing training data from sessions to cluster the data in the number of session types; training a model using the number of session types, wherein feature values for the session types are input into the model, and the model generates an output based on the feature values; comparing the output to a reference output for the feature values; and altering the number of session types based on comparing.